Tag Archives: AWS Outposts

The attendee’s guide to hybrid cloud and edge computing at AWS re:Invent 2025

Post Syndicated from Rachel Zheng original https://aws.amazon.com/blogs/compute/the-attendees-guide-to-hybrid-cloud-and-edge-computing-at-aws-reinvent-2025/

AWS re:Invent 2025 returns to Las Vegas, Nevada, from December 1–5, 2025. This year, we’re offering a comprehensive lineup of sessions and booth activities to help you build resilient, performant, and scalable applications wherever you need them—in the cloud, on premises, or at the edge.

Session types

These sessions are available in the following formats. Most of the sessions are under the topic of Hybrid Cloud and Multicloud (HMC) in the event catalog. If you plan to attend in person, lightning talks and theater sessions are walk-up only. For all other session types, you can reserve your seat through the event portal (login required) or join as a walk-up based on availability.

  • Breakout sessions – Lecture-style 60-minute presentations led by AWS experts and customers.
  • Lightning talks – 20-minute content on specific topics. Each Hybrid Cloud lightning talk features a real-world customer implementation.
  • Chalk talks – 60-minute interactive sessions where AWS experts lead discussions and whiteboard in real time.
  • Code talks – 60-minute sessions featuring coding demonstrations and technical implementations.
  • Workshops – Hands-on, 120-minute sessions where you work directly with AWS services in a guided environment.
  • Theater sessions – 15-minute quick sessions on the Expo floor, typically featuring partner solutions.

Our Hybrid Cloud sessions explore how you can extend AWS infrastructure, services, and tools to distributed locations for low latency, data residency, and local processing needs. These sessions focus on AWS Local Zones, AWS Outposts, and AWS Dedicated Local Zones. We’ve curated the following sessions by theme to help you navigate re:Invent and find content most relevant to your needs.

Leadership session

HMC202 | Breakout Session | AWS wherever you need it: From the cloud to the edge

Wednesday, Dec 3 | 2:30 PM – 3:30 PM PST

Wynn | Convention Promenade | Lafite 7 | Content Hub | Turquoise Theater

Presented by our engineering and product management leaders, Spencer Dillard and Madhura Kale, this session provides an overview of all our latest innovations for hybrid cloud and edge computing, and how they help you address infrastructure requirements in digital sovereignty, generative AI with local data processing needs, and migration and modernization with on-premises dependencies.

Theme 1: Generative AI and agentic AI with local data processing

As you scale generative AI implementations from pilots to production, you need to balance speed of innovation with data sovereignty requirements, low-latency edge inference needs, and space, power, and cost efficiency. In the following sessions, you will learn how to address these challenges.

HMC308 | Breakout session | Build generative and agentic AI applications on-premises and at the edge

Thursday, Dec 4 | 2:30 PM – 3:30 PM PST

Wynn | Upper Convention Promenade | Cristal 7

This session shares reference architectures, best practices, and demos for running small language models (SLMs), Retrieval Augmented Generation (RAG), and agentic AI with AWS Hybrid and Edge services. Gain insights into strategies for model selection and optimization.

HMC324 | Lightning talk | BCC: Hybrid architecture for generative AI to meet regulatory needs

Monday, Dec 1 | 10:00AM – 10:20AM PST

Mandalay Bay | Oceanside C | Content Hub | Lightning Theater

Learn how Bank CenterCredit (BCC) implemented two generative AI use cases: anonymizing personally identifiable information (PII) in customer service calls with Outposts before sending it to the parent AWS Region for foundation model (FM) fine-tuning, and implementing local RAG with regulated data to improve HR efficiency.

HMC311 | Chalk talk | Developing end-to-end SLM pipelines from the cloud to the edge

Thursday, Dec 4 | 11:30AM – 12:30 PM PST

MGM | Level 3 | Chairman’s 362

This session presents a comprehensive approach to deploying SLMs to Local Zones and Outposts using Amazon SageMaker and Amazon EKS. Learn how to deliver domain-specific, fine-tuned SLMs from Regions to edge locations for low-latency inference.

HMC312 | Chalk talk | Implement RAG while meeting data residency requirements

Wednesday, Dec 3 | 5:30PM – 6:30 PM PST

Mandalay Bay | Level 3 South | South Seas A

This session explores how to implement RAG with on-premises and edge data to help you meet data residency and digital sovereignty needs.

HMC317 | Code talk | Implement Agentic AI at the edge for industrial automation

Monday, Dec 1 | 10:30AM – 11:30AM PST

Mandalay Bay | Level 3 South | Jasmine H

Manufacturing and industrial operations demand real-time, intelligent decision-making in low-connectivity environments. Learn how to deploy SmolVLMs (small vision language models) and AI agents to automate site operations using Outposts and Strands Agents.

HMC302 | Workshop | Implementing agentic AI solutions on-premises and at the edge

Wednesday, Dec 3 | 4:00PM – 6:00PM PST

MGM | Level 3 | Chairman’s 368

In this workshop, learn how to extend Amazon Bedrock AgentCore to Outposts and Local Zones to build distributed agentic applications using Model Context Protocol (MCP) and agent-to-agent (A2A) communication with on-premises data.

HMC305 | Workshop | Low-latency SLM deployment: Optimizing inference on AWS Hybrid and Edge services

Monday, Dec 1 | 08:00AM – 10:00AM PST

MGM | Level 1 | Grand 117

This workshop demonstrates a fully local deployment approach for running SLMs at the edge using Local Zones and Outposts. The implementation focuses on achieving low-latency inference and enabling data sovereignty compliance through RAG within local infrastructure.

Theme 2: Migration and modernization with on-premises or edge dependencies

Certain workloads need to stay on-premises or closer to end users. These can be driven by data residency and digital sovereignty requirements or the need to access legacy on-premises systems at a low latency. When a Region is not close enough to meet these needs, AWS brings AWS infrastructure and AWS services closer to where you need them to accelerate migration and modernization.

HMC309 | Breakout session | Migrating your VMware workloads with on-premises dependencies

Thursday, Dec 4 | 11:30AM – 12:30PM PST

Caesars Forum | Level 1 | Summit 212 | Content Hub | Orange Theater

Learn how AWS can help you migrate VMware-based workloads while addressing data residency requirements and latency-sensitive application interdependencies. Gain insights from a real-world implementation at Caesars Entertainment.

HMC217 | Lightning talk | Rivian: Modernize mission-critical manufacturing applications with AWS

Wednesday, Dec 3 | 2:30PM – 2:50PM PST

Mandalay Bay | Level 2 South | Oceanside C | Content Hub | Lightning Theater

Manufacturing applications like Ignition, SCADA, MES, and robotic control require low-latency connectivity to on-premises manufacturing equipment. Learn how Rivian is modernizing mission-critical and latency-sensitive manufacturing applications with Outposts.

HMC313 | Chalk talk | Extend Amazon EKS clusters for on-premises and edge use cases

Wednesday, Dec 3 | 4:00PM – 5:00PM PST

MGM | Level 3 | Chairman’s 356

Dive deep into strategies for modernizing distributed applications with Amazon EKS across Regions, Local Zones, and Outposts.

HMC303 | Workshop | Migrate and modernize VMware workloads with on-premises dependencies

Tuesday, Dec 2 | 12:30PM – 2:30PM PST

Wynn | Convention Promenade | Margaux 2

This workshop guides you through migrating VMware and other on-premises applications to Outposts while modernizing them through containerization.

Theater session | Deploying robust disaster recovery for mission-critical workloads

Wednesday, Dec 3 | 1:00PM – 1:15PM PST

The Venetian | Level 2 | Expo Hall B | NetApp Booth (#1039)

With Outposts third-party storage integration, you can modernize right inside your data centers while leveraging your investment in on-premises storage solutions. Join this session to learn how you can implement a robust disaster recovery solution for mission-critical workloads using Amazon FSx for NetApp ONTAP and on-premises ONTAP with Outposts. Learn how to perform real-time SnapMirror replication between Regions and on-premises environments and monitor replication status and RPO metrics.

Theme 3: Data residency and digital sovereignty

As organizations scale innovative solutions globally, they need to navigate complex digital sovereignty requirements. Learn how AWS Hybrid and Edge services help you adopt a consistent approach to security, monitoring, management, and auditing across jurisdictions while meeting regulatory obligations.

HMC310 | Breakout session | Digital sovereignty and data residency with AWS Hybrid and Edge services

Tuesday, Dec 2 | 4:30PM – 5:30PM PST

Caesars Forum | Level 1 | Summit 212 | Content Hub | Yellow Theater

This session examines best practices for data residency, security controls, and operational consistency across distributed locations. Hear how AWS helped Geidea, a leading fintech company, accelerate business expansion in the Middle East while meeting country-specific data residency requirements.

HMC214 | Lightning talk | DraftKings: Meeting gaming regulations at Super Bowl scale with AWS Local Zones

Wednesday, Dec 3 | 2:00PM – 2:20PM PST

Mandalay Bay | Level 2 South | Oceanside C | Content Hub | Lightning Theater

Learn how DraftKings built a scalable edge strategy using Regions and Local Zones to meet Federal Wire Act requirements while accelerating expansion into 26 US states.

HMC316 | Chalk talk | Address digital sovereignty with hybrid cloud solutions

Monday, Dec 1 | 1:30PM – 2:30PM PST

Mandalay Bay | Lower Level North | South Pacific D

Learn how to choose the best AWS infrastructure option for your sovereign needs and architect applications for data residency and resiliency. Discover how to implement security controls to regulate how data can be stored, processed, and transferred, and how to prevent unauthorized data access.

Theme 4: Optimizing for low and ultra-low latency

Systems like online ticketing, real-time threat detection, manufacturing control, and financial trading require network latency ranging from double-digit milliseconds to low double-digit microseconds. The Hybrid Cloud track discusses how AWS brings cloud services closer to end users and data generation points to satisfy various latency profiles.

SPF206 | Breakout session | Ticketmaster: Enhancing live event experience for fans with AWS Local Zones

Thursday, Dec 4 | 2:30PM – 3:30PM PST

Caesars Forum | Level 1 | Sports Forum | Mainstage

Discover how Ticketmaster delivers superior live event experiences by bringing its online ticket sales platform closer to fans using Local Zones.

HMC216 | Lightning talk | LSEG: Modernizing critical financial systems with multicloud and hybrid cloud

Tuesday, Dec 2 | 3:30PM – 3:50PM PST

Mandalay Bay | Level 2 South | Oceanside C | Content Hub | Lightning Theater

Learn how LSEG is transforming its global PriceStream FX trading platform, implementing a sophisticated architecture for ultra-low latency with managed services. Additionally, explore how LSEG is modernizing clearing systems as critical national infrastructure, balancing regulatory compliance, operational excellence, and business continuity with strict RPO/RTO requirements.

HMC215 | Lightning talk | AWS Local Zones – Sophos’ new edge in the global race against cyber-attacks

Tuesday, Dec 2 | 3:00PM – 3:20PM PST

Mandalay Bay | Level 2 South | Oceanside C | Content Hub | Lightning Theater

Discover how AWS Hybrid and Edge solutions transform how organizations deliver low-latency applications at the edge. Learn how Local Zones extends Regions and services closer to population centers, fitting use cases from media streaming to real-time gaming and financial trading.

HMC213 | Lightning talk | AWS Local Zones & Outposts: Verifone’s Global Edge Computing Strategy

Tuesday, Dec 2 | 11:30AM – 11:50AM PST

Mandalay Bay | Level 2 South | Oceanside C | Content Hub | Lightning Theater

Hear from Verifone on how it transformed its global payment ecosystem, managing 35 million terminals with innovative edge computing. Dive into strategies for deploying point-of-sale (POS) software using multi-tier architectures.

HMC402 | Chalk talk | Meet ultra-low latency and high throughput needs with AWS Outposts

Wednesday, Dec 3 | 10:00AM – 11:00AM PST

Mandalay Bay | Level 2 South | Lagoon G

Dive deep into the new category of accelerated networking Amazon EC2 instances on Outposts, purpose-built for modernizing ultra-low latency and high-throughput mission-critical workloads.

Theme 5: Architecture considerations for hybrid cloud

Running applications outside of Regions requires special architectural considerations to address space, power, and networking constraints. We will share best practices and implementation guidance on improving security, resiliency, and availability.

HMC327 | Lightning talk | Nasdaq: Build resilient infrastructure for global financial services

Tuesday, Dec 2 | 11:00AM – 11:20AM PST

Mandalay Bay | Level 2 South | Oceanside C | Content Hub | Lightning Theater

This session discusses how Nasdaq modernizes its mission-critical capital markets infrastructure while upholding the highest level of resiliency. Learn how Nasdaq integrates Outposts and multi-Region deployments into its core trading and surrounding systems, balancing cloud flexibility with performance and reliability.

HMC328 | Lightning talk | Build resilient and low-latency hybrid telecom infrastructure at scale

Thursday, Dec 4 | 5:00PM – 5:20PM PST

Mandalay Bay | Level 2 South | Oceanside C | Content Hub | Lightning Theater

Discover how Liberty Latin America (LLA) built telecom infrastructure in a hybrid cloud architecture for millions of subscribers. Learn how LLA created a resilient, low-latency network with over 180 VPCs.

HMC314 | Chalk talk | Deploying for resilience: HA/DR strategies for AWS Outposts and AWS Local Zones

Monday, Dec 1 | 1:30PM – 2:30PM PST

MGM | Level 1 | Boulevard 169

In this chalk talk, learn how to plan and implement resilient deployments to deliver high availability and disaster recovery, especially for business-critical or mission-critical workloads.

HMC315 | Chalk talk | Deep dive on AWS hybrid and edge networking architectures

Tuesday, Dec 2 | 1:30PM – 2:30PM PST

MGM | Level 1 | Boulevard 156

This chalk talk covers patterns such as private vs. public connectivity, service link and on-premises connectivity, and designing resilient Multi-AZ architecture across Outposts and Local Zones.

HMC403 | Code talk | Build and optimize edge architecture for resiliency with AI

Wednesday, Dec 3 | 2:30PM – 3:30PM PST

MGM | Level 3 | Chairman’s 356

This live coding session explores how to automate edge infrastructure operations with AI. Discover how to build resilient architectures with the latest Outposts and Local Zones APIs and Strands Agents.

HMC301 | Workshop | Build and operate resilient and performant distributed applications [REPEAT]

HMC301-R: Monday, Dec 1 | 3:00PM – 5:00PM PST | MGM | Grand 117

HMC301-R1: Thursday, Dec 4 | 3:00PM – 5:00PM PST | MGM | Premier 318

This workshop explores how to design and implement applications for multi-geo operations while meeting data residency and performance requirements. You will learn how to design fault-tolerant, latency-sensitive applications across distributed locations with limited hardware resources.

Activities in the Expo

Beyond sessions, join us in the re:Invent Expo (The Venetian, Level 2, Hall B) to meet with AWS experts and learn through interactive demos.

AWS Village (Booth #750)

Connect with AWS experts in the Hybrid Cloud kiosk and AWS Global Infrastructure kiosk to discuss your hybrid cloud and edge computing needs. Watch demos on the following topics and ask questions:

  • Consistent Amazon EKS experience across distributed locations and how it can simplify GPU resource orchestration
  • How to optimize and deploy SLMs locally for AI inference with low latency or data residency needs
  • How to build agentic AI workflows at the edge for manufacturing automation

Stop by the Migration & Modernization area of the AWS Village to see hardware innovations inside the latest generation of Outposts up close and personal.

AWS for Industries Pavillion (Booth #111)

Explore how AWS Hybrid and Edge services unlock new use cases and improve operational efficiency across multiple industries through immersive experiences:

  • AWS for Telecom – Modernizing telecom infrastructure while meeting data sovereignty and performance requirements
  • AWS for Public Sector – Accelerating tactical edge deployments to improve mission outcomes
  • AWS for Automotive – Advancing global R&D of embedded hardware and software

Partner booths

Discover how AWS Hybrid and Edge services and AWS Partner solutions unlock additional use cases through demos:

  • Pure Storage (Booth #1756) – Simplifying cloud migration with on-premises dependencies through Outposts integration with Pure Storage FlashArray
  • Intel (Booth #1010): – Powering physical AI and agentic systems for real-time operations in manufacturing
  • Seagate (Booth #159) – Facilitating data ingestion and pre-processing at the edge

Ready for re:Invent 2025?

We hope this guide to hybrid cloud and edge computing helps you maximize your learning experience at re:Invent. Not able to attend in-person? Register for the virtual-only event offered at no additional cost to livestream keynotes and innovation talks, and access on-demand session content. See you in Las Vegas or virtually!

AWS Weekly Roundup: Amazon Bedrock, AWS Outposts, Amazon ECS Managed Instances, AWS Builder ID, and more (October 6, 2025)

Post Syndicated from Prasad Rao original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-bedrock-aws-outposts-amazon-ecs-managed-instances-aws-builder-id-and-more-october-6-2025/

Last week, Anthropic’s Claude Sonnet 4.5—the world’s best coding model according to SWE-Bench – became available in Amazon Q command line interface (CLI) and Kiro. I’m excited about this for two reasons:

First, a few weeks ago I spent 4 intensive days with a global customer delivering an AI-assisted development workshop, where I experienced firsthand how Amazon Q CLI boosts developer productivity. During the workshop, the customer was able to add a new feature in their application within a day using Amazon Q CLI, which would have traditionally taken them at least a couple of weeks. With Anthropic’s Claude Sonnet 4.5 in Amazon Q CLI, I know developer productivity will be enhanced further.

Second, I’ve started preparing for my code talk at AWS re:Invent 2025, where my co-speaker and I will show live coding to modernize a legacy codebase using Kiro. I can’t wait to use Anthropic’s Claude Sonnet 4.5 in Kiro to create a live demo. If you want to see this demo and over a thousand other sessions on cloud and AI, join us at AWS re:Invent 2025 in Las Vegas from December 1–5.

Last week’s launches
Here are some launches that got my attention:

  • Availability of Claude Sonnet 4.5 in Amazon Bedrock – Anthropic’s most intelligent model, best for coding and complex agents, is now available in Amazon Bedrock. By using Claude Sonnet 4.5 in Amazon Bedrock, developers gain access to a fully managed service that not only provides a unified API for foundation models (FMs) but keeps their data under complete control with enterprise-grade tools for security, and optimization.
  • AWS Outposts supports third-party storage integration with Dell and HPE – AWS Outposts third-party storage integration now includes Dell PowerStore and HPE Alletra Storage MP B10000 systems, joining the list of existing integrations with NetApp on-premises enterprise storage arrays and Pure Storage FlashArray. This integration serves three key purposes. First, it helps you maintain your existing storage infrastructure while migrating VMware workloads to AWS. Second, it helps you meet strict data residency requirements by keeping your data on premises while using AWS services. Third, it means you can use AWS Outposts with third-party storage arrays through AWS tooling.
  • Amazon ECS Managed Instances now available – Amazon ECS Managed Instances for containerized applications is a new fully managed compute option for Amazon ECS designed to eliminate infrastructure management overhead while giving you access to the full capabilities of Amazon EC2. ECS Managed Instances helps you quickly launch and scale your workloads while enhancing performance and reducing your total cost of ownership.
  • Application map is now generally available for Amazon CloudWatch – Amazon CloudWatch now helps you monitor large-scale distributed applications by automatically discovering and organizing services into groups based on configurations and their relationships. With this new application performance monitoring (APM) capability, you can quickly visualize which applications and dependencies to focus on while troubleshooting your distributed applications.
  • Amazon Bedrock AgentCore Model Context Protocol (MCP) server now available – With built-in support for runtime, gateway integration, identity management, and agent memory, the AgentCore MCP server is purpose-built to speed up creation of components compatible with Bedrock AgentCore. You can use the AgentCore MCP server for rapid prototyping, production AI solutions, or to scale your agent infrastructure.

Additional Updates
Here are some additional news items and blog posts that I found interesting:

  • AWS Builder ID now supports Sign in with Google – You can now create an AWS Builder ID using sign in with Google. AWS Builder ID is a personal profile that provides access to AWS applications including Kiro, AWS Builder Center, AWS Training and Certification, AWS re:Post and AWS Startups.
  • AWS API MCP Server v1.0.0 release – AWS API MCP server acts as a bridge between AI assistants and AWS services enabling foundation models to interact with any AWS API through natural language by creating and executing syntactically correct CLI commands. The AWS API MCP Server is open-source and available now on AWS Labs GitHub repository.
  • AWS Knowledge MCP Server now generally available – The AWS Knowledge server gives AI agents and MCP clients access to authoritative knowledge, including documentation, blog posts, What’s New announcements, and Well-Architected best practices, in an LLM-compatible format. With this release, the server also includes knowledge about the regional availability of AWS APIs and CloudFormation resources.
  • AWS Transform now enables Terraform for VMware network automation – AWS Transform now offers Terraform as an additional option to generate network infrastructure code automatically from VMware environments. The service converts your source network definitions into reusable Terraform modules, complementing current AWS CloudFormation and AWS Cloud Development Kit (CDK) support.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

  • AWS AI Agent Global Hackathon – This is your chance to dive deep into our powerful generative AI stack and create something truly awesome. From September 8th to October 20th, you have the opportunity to create AI agents using AWS suite of AI services, competing for over $45,000 in prizes and exclusive go-to-market opportunities.
  • AWS Gen AI Lofts – You can learn AWS AI products and services with exclusive sessions, meet industry-leading experts, and have valuable networking opportunities with investors and peers. Register in your nearest city: Paris (October 7–21), London (Oct 13–21), and Tel Aviv (November 11–19).
  • AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Munich (October 7), Budapest (October 16).

You can browse all upcoming AWS events and AWS startup events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

Prasad

Announcing AWS Outposts third-party storage integration with Dell and HPE

Post Syndicated from Micah Walter original https://aws.amazon.com/blogs/aws/announcing-aws-outposts-third-party-storage-integration-with-dell-and-hpe/

Since announcing second-generation AWS Outposts racks in April with breakthrough performance and scalability, we’ve continued to innovate on behalf of our customers at the edge of the cloud. Today, we’re expanding AWS Outposts third-party storage integration program to include Dell PowerStore and HPE Alletra Storage MP B10000 systems, joining our list of existing integrations with NetApp on-premises enterprise storage arrays and Pure Storage FlashArray. This program makes it easy for customers to use AWS Outposts with third-party storage arrays through AWS native tooling. The solution integration is particularly important for organizations migrating VMware workloads to AWS who need to maintain their existing storage infrastructure during the transition, and for those who must meet strict data residency requirements by keeping their data on-premises while using AWS services.

Outposts compute rack_Gen2_front_45This announcement builds upon two significant storage integration milestones we achieved in the past year. In December 2024, we introduced the ability to attach block data volumes from third-party storage arrays to Amazon EC2 instances on Outposts directly through the AWS Management Console. Then in July 2025, we enabled booting Amazon EC2 instances directly from these external storage arrays. Now, with the addition of Dell and HPE, customers have even more choice in how they integrate their on-premises storage investments with AWS Outposts.

Enhanced storage integration capabilities

Our third-party storage integration supports both data and boot volumes, offering two boot methods: iSCSI SANboot and Localboot. The iSCSI SANboot option enables both read-only and read-write boot volumes, while Localboot supports read-only boot volumes using either iSCSI or NVMe-over-TCP protocols. With this comprehensive approach, customers can centrally manage their storage resources while maintaining the consistent hybrid experience that Outposts provides.

Through the Amazon EC2 Launch Instance Wizard in the AWS Management Console, customers can configure their instances to use external storage from any of our supported partners. For boot volumes, we provide AWS-verified AMIs for Windows Server 2022 and Red Hat Enterprise Linux 9, with automation scripts available through AWS Samples to simplify the setup process.

Support for various Outposts configurations

All third-party storage integration features are supported on Outposts 2U servers and both generations of Outposts racks. Support for second-generation Outposts racks means customers can combine the enhanced performance of our latest EC2 instances on Outposts—including twice the vCPU, memory, and network bandwidth—with their preferred storage solutions. The integration works seamlessly with both our new simplified network scaling capabilities and specialized Amazon EC2 instances designed for ultra-low latency and high throughput workloads.

Things to know

Customers can begin using these capabilities today with their existing Outposts deployments or when ordering new Outposts through the AWS Management Console. If you are using third-party storage integration with Outposts servers, you can have either your onsite personnel or a third-party IT provider install the servers for you. After the Outposts servers are connected to your network, AWS will remotely provision compute and storage resources so you can start launching applications. For Outposts rack deployments, the process involves a setup where AWS technicians verify site conditions and network connectivity before the rack installation and activation. Storage partners assist with the implementation of the third-party storage components.

Third-party storage integration for Outposts with all compatible storage vendors is available at no additional charge in all AWS Regions where Outposts is supported. See the FAQs for Outposts servers and Outposts racks for the latest list of supported Regions.

This expansion of our Outposts third-party storage integration program demonstrates our continued commitment to providing flexible, enterprise-grade hybrid cloud solutions, meeting customers where they are in their cloud migration journey. To learn more about this capability and our supported storage vendors, visit the AWS Outposts partner page and our technical documentation for Outposts servers, second-generation Outposts racks, and first-generation Outposts racks. To learn more about partner solutions, check out Dell PowerStore integration with AWS Outposts and HPE Alletra Storage MP B10000 integration with AWS Outposts.

Multi-rack and multiple logical AWS Outposts architecture considerations for resiliency

Post Syndicated from Brianna Rosentrater original https://aws.amazon.com/blogs/compute/multi-rack-and-multiple-logical-aws-outposts-architecture-considerations-for-resiliency/

AWS Outposts rack offers the same Amazon Web Services (AWS) infrastructure, AWS services, APIs, and tools to virtually any on-premises data center or colocation space for a truly consistent hybrid experience. A logical Outpost (hereafter referred to as an Outpost) is a deployment of one or more physically connected Outposts racks managed as a single entity under one Amazon Resource Name (ARN). An Outpost provides a pool of AWS compute and storage capacity at one of your sites as a private extension of an Availability Zone (AZ) in an AWS Region. Several AWS services that support Outposts offer deployment options that improve your workload’s fault tolerance. However, certain Outposts configuration requirements have to be met in order to use them.

In this post, we explore the architecture considerations that come into play when deciding between a multi-rack logical Outposts rack, or using multiple Outposts racks to support your highly available workloads.

Amazon EC2 on AWS Outposts rack

The following sections cover Amazon Elastic Compute Cloud (Amazon EC2) on Outposts rack

Multi-rack logical Outposts

When using a multi-rack logical Outpost, you can use a rack level spread Amazon EC2 placement group. A rack level spread placement group can have as many partitions as you have racks in your Outpost deployment, and this allows you to spread out your instances to improve the fault tolerance of your workloads. In the following example, we have C5 instances in an Amazon EC2 Auto Scaling group that uses a launch template specifying a rack level spread placement group strategy should be used. This multi-rack Outpost has four racks, thus the instances are spread across the four racks as evenly as possible.

Rack level spread EC2 placement group example

Figure 1: Rack level spread Amazon EC2 placement group example

This placement group strategy can make your workloads more resilient to rack or host failures, but it would not be useful in mitigating an AZ failure. EC2 instances on Outposts are statically stable to network disconnects. Therefore, workloads would continue running during an AZ failure, but mutating actions would be unavailable. Read on to see how this strategy can be used with multiple Outposts to create a multi-AZ resilient architecture.

Multiple Outposts racks

If you have more than one logical Outpost in the same Region, we recommend connecting each Outpost to a different AZ. This would allow you to create multi-AZ resilient architectures, and when used in combination with features such as Intra-VPC communication between your Outposts, you can stretch an Amazon EC2 Auto Scaling group across two or more Outposts in the same VPC. If each Outpost is a single rack deployment, then this can be combined with a host level spread placement group specified in your instance launch template. A host level spread placement group can have as many partitions as you have hosts of that instance type in your Outpost, and would improve your workload’s resiliency to host failures.

For the highest level of spread and resiliency, consider using multiple multi-rack logical Outposts. This would allow you to use rack level spread placement groups, and intra-VPC communication between Outposts, as shown in the following figure. Having more than one multi-rack Outpost allows you to create application architectures that are resilient toward hardware and AZ level failures by spreading your workload across as many fault domains as possible.

Intra-VPC communication between two multi-rack logical Outposts using an EC2 auto scaling group with rack level spread

Figure 2: Intra-VPC communication between two multi-rack logical Outposts using an Amazon EC2 Auto Scaling group with rack level spread

Amazon RDS on AWS Outposts rack

The following sections cover Amazon Relational Database Service (Amazon RDS) on Outposts rack.

Multi-rack logical Outposts

Amazon RDS on Outposts rack supports read replicas, which use the MySQL and PostgreSQL database engines’ built-in asynchronous replication functionality to create a read replica from a source database instance. Read replicas on Amazon RDS on Outposts can be located on the same Outpost or another Outpost in the same VPC as the source database instance, as shown in the following figure. Furthermore, these can be used to scale out beyond the capacity constraints of a single database instance for read-heavy database workloads. They can also be used to maintain a second copy of your database, which can be used in the event of a host failure to improve workload resiliency. The process to promote a read replica to primary must be manually initiated, and your DNS records must be updated to the new primary instance. However, this is a good option to improve database durability if you only have one logical Outpost. Multiple read replicas can be created for a single database instance for added resiliency. You can also create an Amazon RDS read replica for a single rack Outpost to improve your resiliency to host failures. However, having a multi-rack Outpost would allow you to spread your read replica to another rack within your Outpost.

RDS read replicas used with a multi-rack Outpost

Figure 3: Amazon RDS read replicas used with a multi-rack Outpost

Multiple Outposts racks

Multi-AZ Amazon RDS deployments are supported on Outposts rack for MySQL and PostgreSQL database instances, as shown in the following figure. Using your Outposts Local Gateway and synchronous data replication, Amazon RDS creates a primary database instance on one Outpost, and maintains a standby database instance on a different Outpost. Failover to a multi-AZ Amazon RDS standby instance is automatic, and the DNS records are also automatically updated as part of the failover process. Using this deployment option protects you from AZ, host, and Outpost failures. You can also use multi-AZ Amazon RDS in combination with read replicas spread across different hosts on the same rack, or across multiple racks if using two multi-rack Outposts to provide more database durability.

Multi-AZ RDS on Outposts using read replicas for added durability.

Figure 4: Multi-AZ Amazon RDS on Outposts using read replicas for added durability

Amazon EKS on Outposts rack

The following sections cover Amazon Elastic Kubernetes Service (Amazon EKS) on Outposts rack.

Multi-rack logical Outposts

Outposts rack supports two Amazon EKS deployment methods: EKS extended cluster, and EKS local cluster, as shown in the following figure. Go to our documentation for help deciding which method is right for your workload. Using the rack level placement group strategy discussed earlier in this post allows you to spread your EKS instances (worker and control plane depending on the deployment model used) across multiple racks within your Outpost. Amazon EKS control plane instances are automatically replaced in the event of an instance, host, or rack failure, and self-managed worker node instances are typically placed in an Amazon EC2 Auto Scaling group. Therefore, when they’re used with a rack level spread placement group, you can increase your Amazon EKS resiliency and use automation to handle failures.

EKS local cluster with rack level spread placement group and auto scaling

Figure 5: EKS local cluster with rack level spread placement group and auto scaling

Multiple Outposts racks

When using multiple Outposts racks, you’re unable to spread EKS control plane instances across two disparate Outposts. Go to Deploy an Amazon EKS cluster across AWS Outposts with Intra-VPC communication for more information on how to stretch an EKS extended cluster across multiple Outposts racks. If EKS local cluster is a requirement for your workload, you could use an external load balancer and deploy one instance of EKS local cluster on each Outpost in an active/active or active/passive configuration, and use the load balancer to direct incoming traffic to each respective EKS cluster. If your EKS cluster is using persistent storage, then you should consider whether each cluster needs access to the other clusters data, and centralized storage or replication should be used if needed.

Alternatively, if you are using EKS local cluster with two single rack Outposts, then you can also choose to only spread your EKS worker node instances across both of your Outposts. Furthermore, you can use host level spread on your primary Outpost to provide host level resiliency for your control plane instances. This would provide some added durability in the event of a host failure, and you could withstand the failure of your secondary Outpost that is only running some of your worker node instances. If you have two multi-rack Outposts, even though you couldn’t spread your control plane instances across Outposts, you can still use a rack level spread placement group to spread them across racks within your primary multi-rack Outpost. This would provide resiliency against instance, host, rack, and AZ level failures, and you could withstand the failure of your secondary multi-rack Outpost that isn’t running your EKS control plane instances as well.

EKS local cluster using two multi-rack Outposts and rack level spread

Figure 6: EKS local cluster using two multi-rack Outposts and rack level spread

Amazon S3 on Outposts rack

The following sections cover Amazon S3 on Outposts rack.

Multi-rack logical Outposts

Amazon S3 on Outposts supports object replication, either across distinct Outposts, or between buckets on the same Outpost to help meet data-residency needs. The Outpost or bucket you’re replicating to can be in the same AWS account, or a different account. If you have a multi-rack Outpost, then you can replicate your S3 objects to another bucket on the same Outpost to create a copy of your data locally for added resiliency.

S3 replication between buckets on the same Outpost

Figure 7: Amazon S3 replication between buckets on the same Outpost

Multiple Outposts racks

Moreover, if you have multiple Outposts, then you can replicate S3 objects between buckets on each Outpost, as shown in the following figure. Connect each Outpost to a unique AZ to create a multi-AZ resilient architecture, and store a copy of your data on each Outpost. You can combine this with Amazon S3 replication to a bucket on the same Outpost as well, and have multiple replicas managed through Amazon S3 automation for the highest availability. AWS DataSync also supports Amazon S3 on Outposts, and can be used to replicate S3 objects to the Region your Outpost is connected to if you want to store a copy of your data in the cloud, or use Amazon S3 in the Region for data tiering. Refer to Automate data synchronization between AWS Outposts racks and Amazon S3 with AWS DataSync for more information.

S3 replication across two multi-rack Outposts

Figure 8: Amazon S3 replication across two multi-rack Outposts

Further considerations

  • When using multiple Outposts, we recommend connecting each Outpost to a unique availability zone to use multi-AZ deployment options.
  • Outposts are designed to be a connected service, and network outages could cause workflow disruptions. AWS can help you design for continued operations during network outages. We recommend creating a redundant service link connection to support workloads on Outposts with high availability requirements. Go to AWS Direct Connect Resiliency Recommendations for guidance on how to create a highly available service link connection through AWS Direct Connect, and Satellite Resiliency for AWS Outposts.
  • Outposts have a finite amount of compute resources based on the physical configuration chosen, and the logical capacity configuration on your Outpost can be changed at any time using a capacity task. If the Amazon EC2 compute requirements for your workload change over time, then your Outposts capacity configuration can be updated to meet these requirements non-disruptively. Go to Dynamically reconfigure your AWS Outposts capacity using Capacity Tasks for more information.

Conclusion

This post explores the architecture options and considerations for deciding between a multi-rack Outpost, and using multiple Outposts to support your highly available workloads. For more information on how to design highly available architecture patterns for Outposts, go to the AWS Outposts High Availability Design and Architecture Considerations whitepaper. Reach out to your AWS account team, or fill out this form to learn more about Outposts and self-service capacity management.

Improving network observability with new AWS Outposts racks network metrics

Post Syndicated from Adam Duffield original https://aws.amazon.com/blogs/compute/improving-network-observability-with-new-aws-outposts-racks-network-metrics/

With AWS Outposts racks, you can extend AWS infrastructure, services, APIs, and tools to on-premises locations. Providing performant, stable, and resilient network connections to both the parent AWS Region as well as the local network is essential to maintaining uninterrupted service.

The release of two new Amazon CloudWatch metrics, VifConnectionStatus and VifBgpSessionState, gives you greater visibility into the operational status of the Outpost network connections. In this post, we discuss how to use these metrics to quickly identify network disruptions, using additional data points that can help reduce time to resolution.

Outposts network connectivity overview

When connecting an Outposts rack to your chosen data center location, network connections are made between the Outpost Networking Devices (ONDs) and Customer Network Devices (CNDs). These network connections support both the Service Link connectivity back to the chosen anchor Region and connectivity to the on-premises local network through the Local Gateway. First-generation Outposts racks include a minimum of two network devices to provide resilience, with second-generation Outposts racks including four network devices.

Virtual interfaces (VIFs) are used to establish IP network connectivity between the Outpost and CNDs, using Border Gateway Protocol (BGP) for dynamic routing. You can view the details for these VIFs on the Outposts console by choosing Link aggregation groups (LAGs) in the navigation pane and drilling down to find the specific service link and local gateway VIF information. For each connection between an OND and CND, two BGP sessions are established: one to support service link traffic and the other to support local gateway traffic.

The following diagram shows an example of this connectivity for a first-generation Outposts rack.

Figure 1: First-Generation Outposts Rack network connections

Figure 1: First-Generation Outposts Rack network connections

In this configuration, a total of four VIFs are configured into two link aggregation groups (LAGs): one on each OND for the service link and local gateway VIFs.

Understanding the new CloudWatch metrics for Outposts

Observability into the operational status of Outposts rack, including the status and performance of network connectivity, is important for you to be able to quickly identify and investigate potential issues. With the addition of the VifConnectionStatus and VifBgpSessionState Outposts metrics in CloudWatch, you have greater visibility into the connection status of the Outposts rack to your CNDs. The VifConnectionStatus metric is provided on a per-VIF level, available for both the local gateway and service link VIFs. It provides an indication on the status of the VIF using two possible values:

  • A value of 1 indicates that the VIF is successfully connected to the CND with established BGP sessions and able to transmit traffic
  • A value of 0 indicates that the VIF is not in an operational state due to an underlying issue

The VifBgpSessionState metric goes deeper into the BGP connectivity status between each Outposts VIF and CND. A BGP session can be in one of multiple states, each providing insight into where a potential issue might be. To reflect this, the CloudWatch metric value shown relates to the following BGP states:

  1. IDLE – The initial state; the ONDs are waiting for a start event
  2. Connect – The Outposts rack is waiting for the TCP connection to be complete
  3. Active – The Outposts rack is trying to initiate a TCP connection
  4. OpenSent – The router has sent an OPEN message and is waiting for a response
  5. OpenConfirm – The router has received an OPEN message and is waiting for a KEEPALIVE response
  6. Established – The BGP connection is fully established and the ONDs and CNDs can exchange routing information

With these metrics now available in CloudWatch, you can configure Amazon CloudWatch alarms to alert when the metric values indicate potential issues. You can combine existing CloudWatch metrics for Outposts racks with these new metrics to give additional context and visibility into network connectivity status.

Using CloudWatch metrics to investigate Outposts network connectivity issues

In the event of network connectivity issues, it’s important to understand how to use these metrics to assist with investigations and understand potential causes when seeing network impairment. To start with, the Configuration state of the VIFs should be checked. For each VIF, there are four possible states:

  • Pending – A VIF is in this state from the time that it is created within a VIF group until the VIF becomes active on the OND
  • Available – A VIF is active on ONDs
  • Deleting – A VIF is in this state immediately after requesting deletion
  • Deleted – A VIF is deleted

To check the state of an individual VIF on the Outposts console, choose Networking followed by Link aggregation groups (LAGS) in the navigation pane. The service link and local gateway VIFs associated with a specific LAG are shown, and when you choose a specific LAG, the configuration state of the associated VIFs are visible.

Figure 2: AWS Outposts console showing VIF configuration details

Figure 2: AWS Outposts console showing VIF configuration details

You can also retrieve these details programmatically. For example, use the following AWS Command Line Interface (AWS CLI) command to specifically check the configuration state of a service link VIF with ID sl-vif-087faf21db43ba723:

aws ec2 describe-service-link-virtual-interfaces \
--service-link-virtual-interface-id sl-vif-087faf21db43ba723
{
    "ServiceLinkVirtualInterfaces": [
        {
            "ServiceLinkVirtualInterfaceId": "sl-vif-087faf21db43ba723",
            "ServiceLinkVirtualInterfaceArn": "arn:aws:ec2:us-west-2:111122223333:service-link-virtual-interface/sl-vif-087faf21db43ba723",
            "OutpostId": "op-07f6f537e0607d3f1",
            "OutpostArn": "arn:aws:outposts:us-west-2:111122223333:outpost/op-07f6f537e0607d3f1",
            "OwnerId": "280066404755",
            "LocalAddress": "XX.XX.XX.XX/XX",
            "PeerAddress": " XX.XX.XX.XX/XX ",
            "PeerBgpAsn": 65000,
            "Vlan": 2006,
            "OutpostLagId": "op-lag-03782b844d7da1afc",
            "Tags": [],
            "ConfigurationState": "available"
        }
    ]
}

After confirming the Configuration state, you can use the VifConnectionStatus metric to determine the network connectivity status of individual VIFs. When operating and processing traffic in a healthy state, the value of this metric is 1. If this value changes to 0, it indicates a connectivity problem for that VIF between the Outpost and CNDs.

To further understand the potential cause of the VifConnectionStatus value, you can use the VifBgpSessionState metric. Under normal operational status, this metric value is 6, indicating that the BGP session is established and traffic can be sent and received. However, if this metric value changes to 1–5, then it is indicative of an issue. To start investigating the cause of this, you should review VIF configuration both on the Outposts console and programmatically. This includes the values set on the OND for VLAN, local and peer addresses, and BGP ASN. These values can be validated against the configuration on your on-premises CNDs if required. Furthermore, you can use the VifBgpSessionState metric value to determine the potential cause:

  • If the value is 1, validate the values for BGP ASN and peer addresses
  • If the value is 2, this might indicate port or IP address issues
  • If the value is 3, this might indicate BGP version mismatches
  • If the value is 4 or 5, this refers to networking path problems

By using a combination of these metrics, you can gain a clearer understanding of the potential network issue without having to engage with AWS or third-party support teams.

You can view and query these metrics on the CloudWatch console. In the navigation pane, choose All metrics, followed by Outposts under the AWS namespaces section. The Outposts namespace can only be viewed by the Outposts owner account, unless CloudWatch cross-account observability is configured. The new VifConnectionStatus and VifBgpSessionState metrics can be found under the OutpostsID, VirtualInterfaceGroupId, VirtualInterfaceId dimension.

Figure 3: Amazon CloudWatch metrics for AWS Outposts

Figure 3: Amazon CloudWatch metrics for AWS Outposts

For more information on working with metrics, see Metrics in Amazon CloudWatch. For creating alerts based upon these new metrics and their values, refer to Using Amazon CloudWatch alarms.

The resilient design of using multiple ONDs for both service link and local gateway traffic allows workloads to continue to run in the event of connectivity issues for single VIFs. For example, a single service link VIF might report as being down, but the remaining service link VIFs might be unaffected and remain available. In this scenario, the service link itself would remain functional and connected, albeit with potentially lower resilience and capacity. This can be validated throught the ConnectedStatus metric which would have a value of 1.

Conclusion

This post provided details on the newly released CloudWatch metrics for Outposts racks, VifConnectionStatus and VifBgpSessionState, and how you can use them to investigate potential connectivity issues. For more information on Outposts rack networking patterns, see the Networking section of the Outposts High Availability Design and Architecture Considerations whitepaper. For more information about additional CloudWatch metrics that are available, check out the CloudWatch metrics for AWS Outposts documentation for second-generation Outposts racks and first-generation Outposts racks.

Reach out to your AWS account team, or fill out this form to learn more about observability for Outposts.

Deploying external boot volumes with AWS Outposts

Post Syndicated from Mark Nguyen original https://aws.amazon.com/blogs/compute/deploying-external-boot-volumes-with-aws-outposts/

Building on our previous announcement, AWS Outposts third-party storage integration for data volumes, AWS is expanding its collaboration with third-party storage solutions by introducing support for boot volumes backed by external storage arrays. In this post we show you how to boot Amazon Elastic Compute Cloud (Amazon EC2) instances on Outposts directly from NetApp on-premise enterprise array and Pure Storage FlashArray, providing greater flexibility to align your workload needs for storage.

With this enhancement, you can now:

  • Launch EC2 instances using boot volumes backed by compatible third-party storage arrays
  • Use existing storage management workflows and tools for both boot and data volumes
  • Take advantage of advanced features like storage efficiencies and cloning for cost reduction
  • Migrate compute of existing workloads that rely on external boot volumes to Outposts
  • Employ High Availability (HA) and Disaster Recover (DR) architectures with technologies such as NetApp’s SnapMirror or Pure Storage’s ActiveDR, enabling quick and easy recovery from failures and streamline DR processes.

The boot volume integration is enabled through AWS-provided launch Amazon Machine Images (AMIs) and sample automation scripts. Two methods are supported:

  • Storage Area Network boot (SANboot), which uses Pre-boot eXecution Environment (iPXE) and Internet Small Computer System Interface (iSCSI) to attach an external volume when booting.
  • Outposts localboot, which hydrates a local Non-Volatile Memory Express (NVMe) volume using iSCSI or NVMe-over-TCP, prior to booting.

The added third-party boot volume support further strengthens the commitment of AWS to providing flexible, enterprise-grade storage options for you while maintaining the automation tenants of the AWS experience.

SANboot solution overview

SANboot is a method of booting an EC2 instance from a block volume stored on a remote storage device. iPXE is an open source boot firmware that allows computers to boot from a network location, and when combined with iSCSI, it enables the start up of an operating system (OS) from a remote iSCSI target. In this case, the iSCSI target is a third-party storage system. The EC2 instance boots from the remote block volume as if it were a local disk. Read/write operations within the OS are written directly to the external storage array.

The instance can be shutdown and the data remains persistent on the third-party storage device. iPXE is stateless by design, thus subsequent boots must go through the full SANboot process. Snapshots of the volumes can be performed by the external storage device.

SANboot prerequisites

The following prerequisites are necessary for SANboot:

  1. An Outposts rack or Outposts 2U server. Outposts 1U server is not supported.
  2. An external storage system with IP reachability to the Outposts local network through a local gateway (LGW) or local network interface (LNI).
  3. An Amazon EC2 bootable image stored as a block volume on the external storage system.
    1. The block volume should be configured with an iSCSI IQN
    2. At the time of this post, only Windows 2022 (or newer) and Red Hat Linux 9 are supported operating systems
  4. (optional) Authentication credentials stored in AWS Secrets Manager if authentication is needed.
    1. If Secrets Manager is used, your EC2 instance profile needs the following minimum required permissions:
      {
          "Version": "2012-10-17",
          "Statement": [
              {
                   "Sid": "SecretsManagerPermissions",
                   "Effect": "Allow",
                   "Action": [
                        "secretsmanager:GetSecretValue"
                   ],
                   "Resource": "<secret name>"
              }
          ] 
      }

  5. (optional) Use Outposts SANboot script from AWS Samples downloaded onto your management computer. Your computer must be capable of running Python scripts and connected to both the external storage system and the AWS Region.

SANboot deployment overview

The following diagram shows the workflow when launching an EC2 instance using SANboot.

Figure 1 - SANboot workflow

Figure 1 – SANboot workflow

  1. The Outposts must have network connectivity to the external storage system through the Outposts local network connection. Specifically, the EC2 instances must communicate to the storage system. This should be a high speed connection with low latency (recommended 10Gbps or more, and under 10ms round trip). The Outposts and storage system do not have to be on the same subnet, instead they just need IP reachability.
    1. For Outposts racks, the EC2 instance connects through the LGW.
    2. For Outposts 2U servers, the EC2 instance connects through the LNI.
  2. The boot volume must be an Amazon EC2 bootable image. This includes the appropriate settings and drivers for the respective OS.
  3. Run the SANboot automation script and follow the prompts to enter the relevant parameters for your boot volume. This information is used to populate the Multipurpose Internet Mail Extension (MIME) multipart user data that iPXE uses for its initial configuration. See the following section on SANboot script walkthrough for more information.
  4. (optional) If iSCSI authentication is enabled, the SANboot automation script retrieves the credentials of the iSCSI target stored in Secrets Manager to be later populated to the iPXE script.
  5. The iPXE boot AMI launches its firmware image and executes the iPXE script. This script contains the necessary information to mount the remote iSCSI block volume. This includes the iSCSI intiator settings, the IP address of the iSCSI target, the credentials, the iSCSI port, and the iSCSI IQN. The iPXE boot AMI is a relatively small image that is pulled down from the Region. The time it takes to launch the iPXE image depends on the bandwidth of the Outposts service link connection to the Region.
  6. The iPXE firmware establishes the iSCSI connection to the target and boots the OS from the remote storage device over the local network. If applicable, then more user data information can be passed to the booting OS. The time it takes for the OS to boot depends on the speed of the local area network.

SANboot sample script walkthrough

The following diagram shows the workflow for executing the SANboot sample script.

These are the questions you step through as you run the SANboot automation script

Figure 2 – SANboot automation script walkthrough

Outposts localboot solution overview

Outposts localboot, using iSCSI or NVMe-over-TCP, is a method of booting an EC2 instance using a copy of a boot volume retrieved from a remote storage device. The original source image is not modified and remains unchanged. First, a localboot AMI is launched as a helper instance to facilitate the copying of the boot volume. The localboot instance acts as the iSCSI initiator or NVMe-over-TCP host, and mounts the remote boot volume from the third-party storage system. Then, the block volume is copied to local instance storage on the EC2 instance. The EC2 instance reboots using the local instance storage volume.

Localboot uses instance store volumes, which are ephemeral in nature. The boot volume is deleted when the EC2 instance that it is attached to is terminated or stopped. This is suitable for temporary guest desktop access or read-only workloads. Data volumes from external storage can also be mounted during the localboot process, which can be used to store data persistently.

Localboot prerequisites

The following prerequisites are necessary for localboot:

  1. An Outposts rack or Outposts 2U server.
  2. An external storage system with IP reachability to the Outposts local network through an LGW or LNI.
  3. An Amazon EC2 bootable image stored as a block volume on the external storage system.
    1. The block volume should be configured with an iSCSI IQN or NVMe-over-TCP NQN.
    2. At the time of this post, Red Hat Linux 9 is the only supported operating system.
  4. If using Outposts rack, instance types must have local NVMe drives to support instance store volumes. These include the C/M/R “d” type instances such as c5d, m5d, or r7d.
  5. Boot volumes on third-party storage systems must fit within the instance store of the instance type chosen. For example, an m5d.xlarge has one 150GB NVMe SSD for the instance store. The boot volume must be under 150GB to fit within an m5d.xlarge instance store. If the boot volume is larger, then a larger instance type must be chosen, such as an m5d.2xlarge (which has a 300GB NVMe SSD).
  6. (optional) Authentication credentials are stored in Secrets Manager if authentication is needed.
    1. If Secrets Manager is used, your EC2 instance profile needs the following minimum required permissions:
      {
          "Version": "2012-10-17",
          "Statement": [
              {
                   "Sid": "SecretsManagerPermissions",
                   "Effect": "Allow",
                   "Action": [
                        "secretsmanager:GetSecretValue"
                   ],
                   "Resource": "<secret name>"
              }
          ] 
      }

  7. (optional) Outposts localboot script from AWS Samples downloaded onto your management computer. Your computer must be capable of running Python scripts and connected to both the external storage system and the Region.

Localboot deployment overview

The following diagram shows the workflow when launching an EC2 instance using localboot.

Figure 3 - Localboot workflow

Figure 3 – Localboot workflow

Steps 1 and 2 are the same as SANboot deployment, see instructions above. Starting with step 3 for localboot:

  1. Run the localboot automation script and follow the prompts to enter the relevant parameters for your boot volume. This information is used to populate the MIME multipart user data that the localboot instance uses to mount the external boot volume. Go to the following localboot script walkthrough. The user data contains the necessary information to mount the remote iSCSI or NVMe-over-TCP block volume.
  2. (Optional) The localboot automation script retrieves the credentials of the external storage appliance stored in Secrets Manager.
  3. The localboot AMI is downloaded from the Region and launched. This can take a few minutes depending on the speed of the Outposts service link connection to the Region. When booting, the localboot AMI runs the user data script to attach the boot volume from the third-party storage system.
  4. The mounted boot volume is then copied to the local instance storage of the EC2 instance and configured as the boot partition. The time it takes to copy depends on the speed of your local network. When the copying is completed, the EC2 instance reboots using the newly configured boot partition.
  5. A copy of the boot volume is booted as a new EC2 instance. If applicable, then more user data information (such as mounting a data volume) can be passed to the booting OS.

Outposts localboot sample script walkthrough

The following diagram shows the workflow for running the localboot sample script.

These are the questions you step through as you run the localboot automation script

Figure 4 – Localboot automation script walkthrough

Conclusion

The introduction of third-party boot volume support allows you to continue getting value from your existing investments in external shared storage by connecting these arrays with Amazon EC2 on Outposts. Both the SANboot and localboot methods allow you to integrate your existing storage solutions with AWS Outposts, enabling them to boot EC2 instances directly from NetApp on-premise enterprise storage arrays and Pure Storage FlashArray volumes. This capability not only enhances storage options for EC2 instances running on Outposts but also streamlines workload migration and offers advanced features such as storage efficiencies for boot volumes. Automated configuration scripts and AWS-provided helper AMIs allow you to maintain the seamless and automated experience expected from AWS, while also giving you the flexibility to use your preferred enterprise storage solutions. Combined with the previously announced support for data volumes, this development further demonstrates the commitment of AWS to offering flexible solutions that can integrate into your existing hybrid environments.

To speak to an Outposts expert and learn more, submit this form.

To learn more about how NetApp and Pure Storage solutions work with Outposts, read the NetApp on-premises storage arrays for AWS Outposts blog post and the Pure Storage FlashArray for AWS Outposts blog post.

Running and optimizing small language models on-premises and at the edge

Post Syndicated from Chris McEvilly original https://aws.amazon.com/blogs/compute/running-and-optimizing-small-language-models-on-premises-and-at-the-edge/

As you move your generative AI implementations from prototype to production, you may discover the need to run foundation models (FMs) on-premises or at the edge to address data residency, information security (InfoSec) policy, or low latency requirements. For example, customers in regulated industries, such as financial services, healthcare, and telecom, may want to leverage chatbots that support customer queries, optimize internal workflows for complex reporting, and automatically approve requests—while keeping their data in-country. Similarly, some organizations choose to deploy their own small language models (SLMs) to align with stringent internal InfoSec requirements. As another example, manufacturers may want to deploy SLMs right inside their factories to analyze production data and provide real-time equipment diagnostics. To address users’ data residency, latency, and InfoSec needs, this post provides guidance on deploying generative AI FMs into AWS Local Zones and AWS Outposts. The goal is to present a framework for running a range of SLMs that address local data processing requirements-based customer engagements.

Generative AI deployment options

The growth in generative AI deployments and experimentation has accelerated with two business deployment options. The first is the use of a large language model (LLM) to support enterprise needs. LLMs are incredibly flexible: one model can perform completely different tasks, such as answering questions, coding, summarizing documents, translating languages, and generating content. LLMs have the potential to disrupt content creation and the way people use search engines and virtual assistants. The second deployment model is the use of SLMs, addressing a specific use case. SLMs are compact transformer models that primarily use decoder-only or encoder-decoder architectures, with typically less than 20 billion parameters, though this definition is evolving as larger models become available. SLMs can achieve comparable or even superior performance when fine-tuned for specific domains or tasks, making them an excellent alternative for specialized applications.

In addition, SLMs offer faster inference times, lower resource requirements, and are suitable for deployment on a wider range of devices, making them particularly valuable for specialized applications and edge computing where space and power supply are limited. While SLMs are limited in scope and accuracy compared to LLMs, you can enhance their performance for specific tasks through Retrieval Augmented Generation (RAG) and fine-tuning. This combination produces an SLM capable of responding to queries relating to a specific domain with a comparable level of accuracy to an LLM, while reducing hallucinations. SLMs offer effective solutions that balance user needs with cost efficiency.

Architecture overview

The solution discussed in this blog post uses Llama.cpp, an optimized framework written in C/C++ to efficiently run a range of SLMs. Llama.cpp can run efficiently in a wide range of computing environments, enabling generative AI models to operate in Local Zones or on Outposts without the large Graphics Processing Unit (GPU) clusters typically associated with LLMs in their native frameworks. This framework expands your model choice and increases model performance when deploying SLMs to Local Zones and Outposts.

The architecture provides a template for deploying a range of SLMs supporting chatbot or generation use cases. The solution consists of a front-end application receiving user queries, formatting the prompts for presentation to the model, and delivering the resulting responses. To support a scalable solution, the application servers and Amazon EC2 G4dn GPU-enabled instances are front-ended with an Application Load Balancer (ALB).

For a deployment where the incoming prompts exceed the ability of SLMs to service the requests, a message queue can be deployed to the front end of the SLMs. As an example, you could deploy a RabbitMQ cluster to serve as your queue manager.


Figure 1: Architecture overview

Solution deployment

The following instructions show how to launch an SLM using Llama.cpp in Local Zones or on Outposts. Although the preceding architecture overview shows a complete solution with multiple components, this post focuses specifically on the steps necessary to deploy the SLM in an EC2 instance using Llama.cpp.

Prerequisites

For this solution, you need the following prerequisites:

  1. An AWS account that is either allowlisted for Local Zones or has a logical Outpost installed, configured, and running
  2. Access to G4dn instances in your account at the chosen location (check availability in AWS Service Quotas)
  3. A VPC created to host the environment
  4. Public and private subnets to support the environment in the VPC
  5. A security group that is associated with your EC2 instance
  6. AWS Identity and Access Management (IAM) role with AWS Systems Manager Session Manager permissions

1. Launch a GPU instance for your SLM

Sign in to the AWS Management Console, open the Amazon EC2 console, and launch a g4dn.12xlarge EC2 instance in your Local Zone or Outposts environment. Configure it with:

  1. Red Hat Enterprise Linux 9 (HVM), SSD Volume Type
  2. A private subnet associated with your Local Zone or Outposts rack
  3. 30 GiB gp2 root volume and an additional 300 GiB gp2 EBS volume
  4. The IAM role configured with permissions required for Systems Manager
  5. SSM Agent to connect to the instance (follow the instructions in Install SSM Agent on RHEL 8.x and 9.x in the Systems Manager User Guide)

For detailed EC2 launch instructions, see Launch an EC2 instance using the launch instance wizard in the console or Launch an instance on your Outposts rack.


Figure 2: SLM instance launched

2. Install NVIDIA drivers

  1. Connect to the SLM instance using Systems Manager. You can follow the instructions in Connect to your Amazon EC2 instance using Session Manager in the Amazon EC2 User Guide.
  2. Install kernel packages and tools.
    sudo su –
    
    dnf update -y
    
    subscription-manager repos --enable codeready-builder-for-rhel-9-x86_64-rpms
    
    dnf install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm
    
    dnf install -y ccache cmake gcc-c++ git git-lfs htop python3-pip unzip wget
    
    dnf install -y dkms elfutils-libelf-devel kernel-devel kernel-modules-extra \
    libglvnd-devel vulkan-devel xorg-x11-server-Xorg
    
    systemctl enable --now dkms
    
    reboot
    

  3. Install Miniconda3 in the /opt/miniconda3 folder or install a compatible package manager to manage Python dependencies.
  4. Install the NVIDIA drivers.
    dnf config-manager --add-repo \
    http://developer.download.nvidia.com/compute/cuda/repos/rhel9/x86_64/cuda-rhel9.repo
    
    dnf module install -y nvidia-driver:latest-dkms
    
    dnf install -y cuda-toolkit
    
    echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc
    
    echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
    
    source ~/.bashrc

3. Download and install Llama.cpp

  1. Create and mount the filesystem of the Amazon EBS volume you created earlier in the /opt/slm folder. The instructions are available in Make an Amazon EBS volume available for use in the Amazon EBS User Guide.
  2. Run the following commands to download and install Llama.cpp.
    cd /opt/slm
    
    git clone -b b4942 \
    https://github.com/ggerganov/llama.cpp.git
    
    cd llama.cpp
    
    cmake -B build -DGGML_CUDA=ON
    
    cmake --build build --config Release -j$(nproc)
    
    conda install python=3.12
    
    pip install -r requirements.txt
    
    pip install nvitop

4. Download and convert the SLM model

To run the SLM efficiently with Llama.cpp, you need to convert the model format to GGUF (GPT-Generated Unified Format). This conversion optimizes the model for better performance and memory usage on edge deployments with limited resources. GGUF is specifically designed to work with Llama.cpp’s inference engine.The following instructions show how to download SmolLM2 1.7B and convert it to the GGUF format:

mkdir /opt/slm/models
cd /opt/slm/models

git lfs install
git clone https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct

cd /opt/slm/llama.cpp

python3 convert_hf_to_gguf.py --outtype f16 \
--outfile /opt/slm/llama.cpp/models/SmolLM2-1.7B-Instruct-f16.gguf \
/opt/slm/models/SmolLM2-1.7B-Instruct/

echo 'export PATH=/opt/slm/llama.cpp/build/bin:$PATH' >> ~/.bashrc

echo 'export LD_LIBRARY_PATH=/opt/slm/llama.cpp/build/bin:$LD_LIBRARY_PATH' \
>> ~/.bashrc

source ~/.bashrc

You can also download other publicly available models from Hugging Face as needed, following the same conversion process.

SLM operation and optimization

The deployment of SLMs through Llama.cpp provides operational flexibility through a significant range of environment customization options, allowing you to tailor model behavior to your specific use cases. With Llama.cpp, you can select a range of parameters that can be used to optimize the use of system resources and model operation. This enables models to efficiently use the available resources without consuming unneeded resources or negatively impacting their operation. The following parameters are commonly supported when running Llama.cpp to provide control over how the model runs.

  • -ngl N, --n-gpu-layers N: When compiled with GPU support, this option allows you to offload some layers to the GPU for computation. This generally results in increased performance.
  • -t N, --threads N: Sets the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores that your system has.
  • -n N, --n-predict N: Sets the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.
  • -sm, --split-mode: Specifies how to split the model across multiple GPUs when running in a multi-GPU environment. Consider testing the ‘row’ splitting mode as it may provide better performance in some scenarios compared to the default layer-based splitting.
  • --temp N: Temperature is a parameter that provides control over an SLM’s randomness in terms of its outputs. Changing the value from the default of 0.8 changes the distribution of the model’s predictions. Lower values sharpen and deterministically enhance the model’s output, preferring high-probability phrases. On the other hand, a higher temperature fosters more inventiveness and unpredictability, allowing for a wider range of diverse responses. (default: 0.8).
  • -s SEED, --seed SEED: Provides a method to control the randomness of the SLM’s response. Setting a specific seed value produces consistent responses across multiple runs, while using the default value (-1) generates varied responses. (default: -1, -1 = random seed).
  • -c, --ctx-size N: Context size is defined as the number of tokens an FM can process as one prompt (in tokens) into the model. This value reserves memory in the host for the maximum size context length. Reducing the context size can reduce the amount of RAM needed to load and run a model. Models with larger context sizes (>50k) consume more RAM, and in some models, a larger context size can impact accuracy. For example, with Phi-3, it’s recommended to reduce the context size to 8k or 16k. The command to achieve this is –ctx-size XXXX, where XXXX is the context size.

This section demonstrates how to optimize SLM performance for specific use cases using Llama.cpp. We’ll cover two common scenarios: chatbot interactions and text summarization.

Chatbot Use Case Example

Token Size Requirements

For chatbot applications, typical token size requirements include input sizes ranging from 50-150 tokens, which support user questions up to a couple of sentences, and output sizes of approximately 100-300 tokens, allowing the model to provide detailed but concise responses.

Sample Command

./build/bin/llama-cli -m ./models/SmolLM2-1.7B-Instruct-f16.gguf \
-ngl 99 -n 512 --ctx-size 8192 -sm row --temp 0 --single-turn \
-sys "You are a helpful assistant" -p "Hello"

Figure 3: Chatbot example

Command Explanation

  • -m ./models/SmolLM2-1.7B-Instruct-f16.gguf: Specifies the model file to use
  • -ngl 99: Allocates GPU layers for optimal performance
  • -n 512: Sets maximum output tokens to 512, sufficient for the 100-300 tokens typically needed
  • --ctx-size 8192: Defines context window size, allowing for complex conversation history
  • -sm row: Splits the rows across GPUs
  • --temp 0: Sets the temperature to zero to be less creative
  • --single-turn: Optimizes for single-response interactions
  • -sys "You are a helpful assistant": Sets the system prompt to define assistant behavior
  • -p "Hello": Provides the user input prompt

Text summarization example

This command line shows SmolLM2-1.7B running to support a summarization activity.

PROMPT_TEXT="Summarize the following text: Amazon DynamoDB is a serverless, 
NoSQL database service that allows you to develop modern applications 
at any scale. As a serverless database, you only pay for what you use 
and DynamoDB scales to zero, has no cold starts, no version upgrades, 
no maintenance windows, no patching, and no downtime maintenance. 
DynamoDB offers a broad set of security controls and compliance 
standards. For globally distributed applications, DynamoDB global 
tables is a multi-Region, multi-active database with a 99.999% 
availability SLA and increased resilience. DynamoDB reliability is 
supported with managed backups, point-in-time recovery, and more. 
With DynamoDB streams, you can build serverless event-driven applications."

./build/bin/llama-cli -m ./models/SmolLM2-1.7B-Instruct-f16.gguf \
-ngl 99 -n 512 --ctx-size 8192 -sm row --single-turn \
-sys "You are a technical writer" \
--prompt "$PROMPT_TEXT"

Figure 4: Summarization example

Cleaning up

To avoid ongoing charges, take these steps to delete the resources you created by following this post, if they are no longer needed.

  • Terminate your EC2 instance to avoid unnecessary charges. Furthermore, verify that the 300 GiB EBS volume you attached is properly deleted by checking the Volumes section under Elastic Block Store and manually delete any remaining volumes by choosing them and choosing Actions > Delete volume.

Conclusion

This post took you through the steps of deploying SLMs into AWS on-premises or edge environments to address local data processing needs. The post first discussed business benefits of SLMs, including faster inference time, reduced operational costs, and improved model outcomes. SLMs launched using Llama.cpp and optimized for specific use cases can effectively deliver user services from the edge in a scalable manner. The optimization parameters described in this post provide multiple ways to configure these models for different deployment use cases. You can follow the deployment steps and techniques in this blog post to implement generative AI capabilities that align with your specific data residency, latency, or InfoSec compliance needs while operating efficiently within the resource constraints of edge environments. Visit the following pages to learn more about AWS Local Zones and AWS Outposts.

Control instance placement using Asset Level Capacity Management for AWS Outposts

Post Syndicated from Brianna Rosentrater original https://aws.amazon.com/blogs/compute/control-instance-placement-using-asset-level-capacity-management-for-aws-outposts/

AWS Outposts supports self-service capacity management at the entire Outpost level, or at the individual asset level, making it easy for you to view and manage compute capacity on your Outposts. This feature supports both Outposts rack (such as the recently announced second-generation Outposts rack) and Outposts server. A default capacity configuration for each new Outpost is determined during the ordering process. This default configuration can subsequently be modified to create a range of Amazon Elastic Compute Cloud (Amazon EC2) instance sizes and quantities to meet your changing business needs. For more information on performing Outposts level multi-asset reconfigurations, go to Dynamically reconfigure your AWS Outposts capacity using Capacity Tasks.

The release of Asset Level Capacity Management allows you to control the configuration of specific assets within your Outpost, which can be useful when planning strategies for EC2 Auto Scaling groups and host-level high availability. An Outpost asset can be a single server within an Outposts rack, or an Outposts server. This post focuses on how to use Asset Level Capacity Management to perform single-host reconfigurations, and how this can be used with Amazon EC2 placement groups to control instance placement on your Outpost.

Overview

When you place an Outposts order, you determine the capacity configuration of each Outpost based on the anticipated workload requirements. You can scale your Outposts up or out as needed during your commitment term. For further details on Outpost capacity planning including best practices, refer to the Capacity Planning – AWS Outposts High Availability Design and Architecture whitepaper. We recommend planning spare capacity for N+M host availability per instance family when making modifications to your Outpost capacity configuration for workloads that need to be highly available. To calculate, take the number of assets (N) you need to run all your workloads, and then add (M) additional assets to meet your requirements for server availability during failure and maintenance events.

You also need to plan for instance level high availability when deciding to reconfigure particular assets. For example, say you have two C5 assets, and each one is configured homogeneously to provide C5.2xlarge instances. If you have an Auto Scaling group that specifies C5.2xlarge in its launch template, and you perform an asset level reconfiguration of one of your C5 assets so that it only offers C5.4xlarge instances, then your Auto Scaling group can only launch instances on the one C5 host configured to provide C5.2xlarge instances. If that host fails, then the Auto Scaling group is unable to launch new C5.2xlarge instances on the other host unless the Auto Scaling group launch template is modified. Understanding failure scenario behavior and how much capacity you want to reserve for high availability is key to capacity management and disaster recovery planning. For highly available workloads, we recommend spreading your instances across as many assets as possible.

Understanding EC2 placement groups on AWS Outposts

Outposts rack supports EC2 placement groups, and two placement group options are available only on Outposts: rack level spread, and host level spread. This allows you to spread out instances across underlying hardware on an Outpost at your site. To use a rack level spread placement group, you must have two or more physical Outpost racks. Each spread strategy can be used to create resilient Outposts architectures that can withstand a rack or host failure depending on the respective strategy used.

Rack level spread

Figure 1: Outposts rack showing a rack level spread EC2 placement group

Figure 1: Outposts rack showing a rack level spread EC2 placement group

Using a multi-rack Outpost, you can spread your EC2 instances across multiple racks with a rack level spread EC2 placement group. When used with Auto Scaling groups, this allows you to withstand an individual rack or multi-asset failure. When your Auto Scaling group detects you’ve lost instances on one of your racks, it automatically relaunches the instances using the assets on your other racks if you have available capacity. To use this strategy to increase your workload resiliency, each rack would need to have assets that can support the instance type (C5 is used in the preceding figure) and size used in your Auto Scaling group launch template. The expanded functionality that asset level capacity management brings to capacity tasks allows you to configure your Outpost so that each rack has at least one asset that can support the instances used in your Auto Scaling groups. Configure your assets on each rack to meet your resiliency goals for host failure tolerance as well. This configuration can be done in an on-demand, self-service fashion to meet the needs of your evolving workloads if instance requirements change over time.

Host level spread

Figure 2: Outposts rack showing a host level spread EC2 placement group

Although rack level spread EC2 placement groups need a multi-rack Outpost, host level spread EC2 placement groups can be used within a single rack Outpost to provide resiliency for your workloads at the asset level. When used with Auto Scaling groups, this allows you to withstand an individual asset or multi-asset failure depending on your Outpost configuration. When your Auto Scaling group detects you’ve lost instances on one of your assets due to a hardware failure, it automatically relaunches the instances using your other assets on your Outpost if you have available capacity. To use this strategy to increase your workload resiliency, you would need to have at least two assets within your Outposts rack that can support the instance type (R5 and M5 are used in the preceding figure) and size used in your Auto Scaling group launch template. Outposts also supports using attribute-based instance type selection if multiple instance types meet your workload needs based on some minimum resource requirements. With the expanded functionality that asset level capacity management brings to capacity tasks, you can configure your Outposts rack so that each asset type can support the instance size used in your Auto Scaling groups. This configuration can be done in an on-demand, self-service fashion to meet the needs of your evolving workloads if instance requirements change over time.

Using asset level capacity tasks

Asset Level Capacity Management allows you to target a specific Outpost asset to change its capacity configuration directly, allowing granular control over instance capacity pool configurations. Outpost assets are referred to by a unique ten-digit Asset ID. The first step in this process is identifying a suitable asset on which to perform the capacity task. To do this, you can use the rack view within the Outposts console page to view each asset, its current capacity configuration, and its current usage. Choosing an asset with fewer running instances may increase the chances of the capacity task being successful without needing instances to be stopped.

In the following example, the rack view has been filtered by the R5 family resulting in the two R5 assets being displayed. The Show instance details option has also been chosen to show the instance IDs of the running instances on our Outposts rack.

Figure 3: Rack view of the Outposts console

When you have identified the asset to target for the capacity task, you can either choose the Modify option in the top right of the asset itself or go to Capacity Tasks from the console menu and choose the asset ID directly from the dropdown menu.

Figure 4: Capacity tasks console experience

From here, you have the option to use the capacity configuration builder to interactively modify your Outposts capacity layout, or you can upload a capacity configuration plan JSON document with the necessary configuration. When building the capacity task, you have two options to choose from when handling instances that are blocking the task from executing. The default option is set to fail the capacity task if this occurs. However, this can be set to wait for the instances to be stopped so that the task can continue. If this option is chosen, then the asset is placed into an isolated state until either the capacity task completes or is cancelled, thus preventing any further instances launches on the impacted asset.

If there are instances on the asset that can’t be stopped to complete the capacity task, then they can be chosen from the Instances to keep as-is section. Only the instances running on the impacted asset are listed. If a capacity task can’t be completed while leaving the chosen instances running, the capacity task fails.

In the following example, the capacity configuration requested for the asset results in the removal of one r5.4xlarge and two r5.2xlarge instances, which creates sufficient space for the creation of 12 r5.large instances. This asset also has three instances running on it which have all been chosen to keep as-is during the execution of the task.

Figure 5: Capacity task example showing r5 asset level capacity management

You can also execute capacity tasks programmatically If you prefer through CLI or API calls. For example, using the start-capacity-task CLI to submit the same configuration would look as follows:

aws outposts start-capacity-task \
--outpost-id op-07f6f537e0607d3f1 \
--asset-id 1702928095\
--instances-to-exclude '{
    "Instances": ["i- 03f53189ffedcc72c", "i-044383b9051299b50", "i-0dfd88574237a68a4"],
    "AccountIds": ["450360193046", "450360193046", "450360193046"],
    "Services": ["EC2", "EC2", "EC2"]
}' \
--task-action-on-blocking-instances FAIL_TASK \
--instance-pools '[
    {
        "InstanceType": "r5.large",
        "Count": 12
    },
    {
        "InstanceType": "r5.xlarge",
        "Count": 6
    },
    {
        "InstanceType": "r5.2xlarge",
        "Count": 4
    },
    {
        "InstanceType": "r5.4xlarge",
        "Count": 1
    }
]'

After defining the capacity task, you are presented with an overview of the requested changes before submitting the task for execution. When it’s submitted, the task first enters a Requested status while the configuration is evaluated, before either being moved to In Progress if the task is valid or Failed if it’s invalid or blocked by running instances.

When the capacity task has successfully completed and the capacity pools for the asset are updated, you can validate this by returning to the rack view within the Outpost console, or by using the CLI/API. The following is an example using the list-assets CLI command:

aws outposts list-assets --outpost-identifier op-07f6f537e0607d3f1 --query "Assets[?AssetId=='1702928095']"

[
    {
        "AssetId": " 1702928095",
        "RackId": "1702928115",
        "AssetType": "COMPUTE",
        "ComputeAttributes": {
            "State": "ACTIVE",
            "InstanceFamilies": [
                "R5"
            ],
            "InstanceTypeCapacities": [
                {
                    "InstanceType": "r5.2xlarge",
                    "Count": 4
                },
                {
                    "InstanceType": "r5.4xlarge",
                    "Count": 1
                },
                {
                    "InstanceType": "r5.xlarge",
                    "Count": 6
                },
                {
                    "InstanceType": "r5.large",
                    "Count": 12
                }
            ],
            "MaxVcpus": 96
        },
        "AssetLocation": {
            "RackElevation": 27.0
        }
    }
]

Only a single capacity task for an asset can be executing at any given time. If you attempt to create a second capacity task for the same asset while the original is still in a Requesting or In Progress status, then the submission of the task fails. However, you can submit multiple capacity tasks for unique assets within the same Outpost. For example, using the CLI commands, you could execute a single script to change the capacity configuration of all assets within an Outpost through individual asset level capacity tasks.

Considerations

  • Make sure that if you’re specifying instance type in your launch templates, then this instance type is available on multiple assets if your workload needs to be resilient against host failures.
  • Understand which failure scenarios could exist within your environment, and plan for how each one should be handled. Failure planning is essential for maintaining workload uptime in production environments.
  • Capacity tasks can only be executed from the AWS account that owns the Outpost. If Outpost resources are shared to workload accounts through AWS Resource Access Manager (AWS RAM), then these accounts can’t submit capacity tasks.
  • You can manipulate your capacity configuration to control instance placement at launch. If only certain assets support the instance size and type you want to deploy, then your instance must be launched on one of those assets.
  • If executing capacity tasks through CLI commands, make sure that your CLI has been updated to the latest version. We have updated our CLI with this feature release to include commands for capacity tasks, and they fail if running on outdated versions.

Conclusion

This post demonstrates how to use Asset Level Capacity Management with your AWS Outposts, and reviews considerations for maintaining a highly available capacity configuration. For more information on how to manage and monitor your capacity configuration on Outposts, see the Capacity management for AWS Outposts user guide and the Capacity planning section of the Outposts High Availability Design and Architecture Considerations whitepaper. Reach out to your AWS account team, or fill out this form to learn more about Outposts and self-service capacity management.

AWS Weekly Roundup: Amazon Nova Premier, Amazon Q Developer, Amazon Q CLI, Amazon CloudFront, AWS Outposts, and more (May 5, 2025)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-nova-premier-amazon-q-developer-amazon-q-cli-amazon-cloudfront-aws-outposts-and-more-may-5-2025/

Last week I went to Thailand to attend the AWS Summit Bangkok. It was an energizing and exciting event. We hosted the Developer Lounge, where developers can meet, discuss ideas, enjoy lightning talks, win SWAGs at AWS Builder ID Prize Wheel, take a challenge at Amazon Q Developer Coding Challenge, or learn Generative AI at Learn Amazon Bedrock booth.

Here’s a quick look:

Thank you to AWS Heroes, AWS Community Builders, AWS User Group leaders and developers for your collaboration.

Coming up next in ASEAN is AWS Summit Singapore—make sure you don’t miss it by registering now.

Last Week’s Launches
Here are some launches last week that caught my attention:

  • Amazon Nova Premier Now Generally Available — Amazon Nova Premier, our most capable model for complex tasks and teacher for model distillation, is now generally available in Amazon Bedrock. It excels at complex tasks requiring deep context understanding and multistep planning, while processing text, images, and videos with a 1M token context length. With Nova Premier and Amazon Bedrock Model Distillation, you can create highly capable, cost-effective, and low-latency versions of Nova Pro, Lite, and Micro, for your specific needs.

  • Amazon Q Developer elevates the IDE experience with new agentic coding experience — This new interactive, agentic coding experience for Visual Studio Code allows Q Developer to intelligently take actions on behalf of the developer. Amazon Q Developer introduces an interactive coding experience in Visual Studio Code, offering real-time collaboration for coding, documentation, and testing. It provides transparent reasoning, and supports automated or step-by-step changes in multiple languages.

  • New Foundation Models in Amazon Bedrock — Amazon Bedrock expands its model offerings with two significant additions:
    • Writer’s Palmyra X5 and X4 models feature extensive context windows (1M and 128K tokens respectively) and excel in complex reasoning for enterprise applications. They support multistep tool-calling and adaptive thinking with high reliability standards.
    • Meta’s Llama 4 Scout 17B and Maverick 17B models offer natively multimodal capabilities using mixture-of-experts architecture for enhanced reasoning and image understanding. They support multiple languages and extended context processing, with simplified integration through the Bedrock Converse API.
  • Second-Generation AWS Outposts Racks Released AWS announces the general availability of second-generation Outposts racks with significant enhancements including the latest x86 EC2 instances, simplified networking, and accelerated networking options. These improvements deliver doubled vCPU, memory, and network bandwidth, 40% better performance, and support for ultra-low latency workloads, making them ideal for demanding on-premises deployments.

  • Amazon CloudFront SaaS Manager Launches — Amazon CloudFront SaaS Manager helps SaaS providers and web hosting platforms efficiently manage content delivery across multiple customer domains. The service dramatically reduces operational complexity while providing high-performance content delivery and enterprise-grade security for every customer domain.

  • Amazon Aurora Now Supports PostgreSQL 17 — Amazon Aurora now supports PostgreSQL 17.4, offering community improvements and Aurora-specific enhancements like optimized memory management and faster failovers. The release includes new features for Babelfish, security fixes, and updated extensions, available in all AWS Regions.
  • CloudWatch Introduces Tiered Pricing for Lambda Logs — Amazon CloudWatch launches tiered pricing for AWS Lambda logs and new delivery destinations. Pricing in US East starts at $0.50/GB for CloudWatch and $0.25/GB for S3 and Firehose, both tiering down to $0.05/GB. This update enhances flexibility in log management across all supporting Regions.
  • RDS for MySQL Updates Minor VersionsAmazon RDS for MySQL now supports minor versions 8.0.42 and 8.4.5, delivering security fixes, bug fixes, and performance improvements. Users can upgrade automatically during maintenance windows or use Blue/Green deployments for safer updates.
  • Amazon Bedrock Model Distillation Generally AvailableAmazon Bedrock Model Distillation is now generally available, supporting new models like Amazon Nova and Claude 3.5. It enables smaller models to accurately predict function calling for Agents, delivering up to 500% faster responses and 75% lower costs with minimal accuracy loss for RAG use cases. The service includes automated workflows for data synthesis and student model training.
  • AI Search Flow Builder for Amazon OpenSearch Service Amazon OpenSearch Service now offers an AI search flow builder for OpenSearch 2.19+ domains. This low-code designer enables creation of sophisticated AI-enhanced search flows using AWS and third-party services, supporting use cases like RAG, query rewriting, and semantic encoding.

From Community.AWS
Here’s my personal favorites posts from community.aws:

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

  • AWS Summit — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Poland (6 May), Bengaluru (May 7 – 8), Hong Kong (May 8), Seoul (May 14-15), Singapore (May 29), and Sydney (June 4–5).
  • AWS re:Inforce – Mark your calendars for AWS re:Inforce (June 16–18) in Philadelphia, PA. AWS re:Inforce is a learning conference focused on AWS security solutions, cloud security, compliance, and identity. You can subscribe for event updates now!
  • AWS Partners Events – You’ll find a variety of AWS Partner events that will inspire and educate you, whether you are just getting started on your cloud journey or you are looking to solve new business challenges.
  • AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Yerevan, Armenia (May 24), Zurich, Switzerland (May 25), and Bengaluru, India (May 25).

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!


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Announcing second-generation AWS Outposts racks with breakthrough performance and scalability on-premises

Post Syndicated from Micah Walter original https://aws.amazon.com/blogs/aws/announcing-second-generation-aws-outposts-racks-with-breakthrough-performance-and-scalability-on-premises/

Today we’re announcing the general availability of second-generation AWS Outposts racks, which marks the latest innovation from AWS for edge computing. This new generation includes support for the latest x86-powered Amazon Elastic Compute Cloud (Amazon EC2) instances, new simplified network scaling and configuration, and accelerated networking instances designed specifically for ultra-low latency and high-throughput workloads. These enhancements deliver greater performance for a broad range of on-premises workloads, such as core trading systems of financial services and telecom 5G Core workloads.

Customers like athenahealth, FanDuel, First Abu Dhabi Bank, Mercado Libre, Liberty Latin America, Riot Games, Vector Limited, and Wiwynn are already using Outposts racks for workloads that need to stay on-premises. The second-generation Outposts rack can provide low latency, local data processing, or data residency needs, such as game servers for multi-player online games, customer transaction data, medical records, industrial and manufacturing control systems, telecom Business Support Systems (BSS), and edge inference of a variety of machine learning (ML) models. Customers can now take advantage of the latest generation of processors and more advanced configurations of Outposts racks to support faster processing, higher memory capacity, and increased network bandwidth.

Latest generation EC2 instances

We’re excited to announce local support for the latest generation (7th generation) of x86-powered Amazon EC2 instances on AWS Outposts racks, starting with C7i compute-optimized instances, M7i general-purpose instances, and R7i memory-optimized instances. These new instances deliver twice the vCPU, memory, and network bandwidth while providing up to 40% better performance compared to C5, M5, and R5 instances on previous generation Outposts racks. They are powered by 4th Gen Intel Xeon Scalable processors and are ideal for a broad range of on-premises workloads requiring enhanced performance such as larger databases, more memory-intensive applications, advanced real-time big data analytics, high-performance video encoding and streaming, and CPU-based edge inference with more sophisticated ML models. Support for more latest generation EC2 instances, including GPU-enabled instances, is coming soon.

Simplified network scaling and configuration

We’ve completely reimagined networking in our latest Outposts generation, making it simpler and more scalable than ever. At the heart of this upgrade is our new Outposts network rack, which acts as a central hub for all your compute and storage traffic.

This new design brings three major benefits to the table. First, you can now scale your compute resources independently from your networking infrastructure, giving you more flexibility and cost efficiency as your workloads grow. Second, we’ve built in network resilience from the ground up, with the network rack automatically handling device failures to keep your systems running smoothly. Third, connecting to your on-premises environment and AWS Regions is now a breeze – you can configure everything from IP addresses to VLAN and BGP settings through straightforward APIs or our updated console interface.

Image of an AWS Outposts rack device

Specialized Amazon EC2 instances with accelerated networking

We’re introducing a new category of specialized Amazon EC2 instances on Outposts racks with accelerated networking. These instances are purpose built for the most latency-sensitive, compute-intensive, and throughput-intensive mission-critical workloads on-premises. To deliver the best possible performance, in addition to the Outpost logical network, these instances feature a secondary physical network with network accelerator cards connected to top-of-rack (TOR) switches.

First in this category are bmn-sf2e instances, designed for ultra-low latency with deterministic performance. The new instances run on Intel’s latest Sapphire Rapids processors (4th Gen Xeon Scalable), delivering 3.9 GHz sustained performance across all cores with generous memory allocation – 8GB of RAM for every CPU core. We’ve equipped bmn-sf2e instances with AMD Solarflare X2522 network cards that connect directly to top-of-rack switches.

For financial services customers, especially capital market firms, these instances offer deterministic networking through native Layer 2 (L2) multicast, precision time protocol (PTP), and equal cable lengths. This enables customers to meet regulatory requirements around fair trading and equal access while easily connecting to their existing trading infrastructure.

Instance Name vCPUs Memory (DDR5) Network Bandwidth NVMe SSD Storage Accelerated Network Cards Accelerated Bandwidth (Gbps)
bmn-sf2e.metal-16xl 64 512 GiB 25 Gbps 2 x 8 TB (16 TB) 2 100
bmn-sf2e.metal-32xl 128 1024 GiB 50 Gbps 4 x 8 TB (32 TB) 4 200

The second instance type, bmn-cx2, is optimized for high throughput and low latency. This instance features NVIDIA ConnectX-7 400G NICs physically connected to high-speed top-of-rack switches, delivering up to 800 Gbps bare metal network bandwidth operating at near line rate. With native Layer 2 (L2) multicast and hardware PTP support, this instance is ideal for high-throughput workloads like real-time market data distribution, risk analytics, and telecom 5G core network applications.

Instance Name vCPUs Memory (DDR5) Network Bandwidth NVMe SSD Storage Accelerated Network Cards Accelerated Bandwidth (Gbps)
bmn-cx2.metal-48xl 192 1536 GiB 50 Gbps 4 x 4 TB (16 TB) 2 800

Bottom line, the new generation of Outposts racks deliver enhanced performance, scalability, and resiliency for a broad range of on-premises workloads, even for mission-critical workloads with the most stringent latency and throughput requirements. You can make your selection and initiate your order from the AWS Management Console. The new instances maintain consistency with regional deployments by supporting the same APIs, AWS Management Console, automation, governance policies, and security controls in the cloud and on-premises, improving developer productivity and IT efficiency.

Things to know

At launch, second-generation Outposts racks can be shipped to US and Canada and be parented back to 6 AWS Regions including US East (N. Virginia and Ohio), US West (Oregon), EU West (London and France) and Asia Pacific (Singapore). Support for more countries and territories and AWS Regions is coming soon. At launch, second-generation Outposts racks locally support a subset of AWS services found in previous generation Outposts racks. Support for more EC2 instance types and more AWS services is coming soon.

To learn more, visit the AWS Outposts racks product page and user guide. You can also talk to an Outposts expert if you are ready to discuss your on-premises needs.

— Micah;


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Maintaining spare capacity during host failures on AWS Outposts with dynamic monitoring

Post Syndicated from Adam Duffield original https://aws.amazon.com/blogs/compute/maintaining-spare-capacity-during-host-failures-on-aws-outposts-with-dynamic-monitoring/

AWS Outposts Rack is a fully managed service that extends AWS infrastructure, services, and APIs to user managed locations. Although you may be used to the seemingly infinite capacity that AWS offers in region, those using Outposts rack for their workloads are limited to the capacity that they order. You will need to closely manage and monitor usage of the available resources as part of capacity management. It is also important to make sure that there is sufficient available capacity in the event of an impactful hardware failure. Although spare capacity is often planned for in the initial Outposts rack configuration order, scaling events and deployments of new workloads can often lead to capacity shortages that only become visible during a failure event.

In this post, we review best practices for capacity management and fault tolerance with Outposts rack followed by an example of how the Outposts API can be used to build an automated monitoring and alerting system to highlight potential resiliency issues.

Planning for failures

The AWS Outposts High Availability Design and Architecture whitepaper discusses the principals of capacity planning within Outposts rack, such as how instance families are mapped to hosts through capacity planning.

When looking to determine resiliency levels, we refer to having N+M capacity, where N represents the number of deployed hosts of a particular instance family (such as C5 or M5), and M represents the number of hosts that can fail while still meeting workload capacity requirements.

The capacity configuration that is applied to each host will impact the necessary recovery process in the event of a failure, depending on the number of configured or running instances. With this in mind, there are three potential recovery scenarios that can apply in the event of a host hardware failure:

  1. Sufficient capacity exists within all instance pools to tolerate the failure of M hosts. This is the most ideal operational position to be in because, in the event of a failure, instances can be recovered to free capacity quickly either through automated features, such as EC2 Auto Scaling groups and instance recovery, or through manual stop/start of the instances.
  2. The required instance type is not available within the available instance pools, however, there is sufficient vCPU available to execute capacity tasks to create the required instance capacity to fulfill the shortfall. As this requires changes to existing capacity, this results in a longer recovery time overall
  3. Insufficient capacity within the Outpost at both the instance pool and vCPU level means that either workloads need to be stopped to fit within the available capacity, or more Outpost hardware needs to be added. This further extends the recovery time for workloads.

Consider the following example of an Outpost configured with four M5 hosts that have been designed with an N+1 resiliency model.

Figure 1: Example configuration with sufficient instance pool capacity

In this example, there are five configured instance pools with the following usages:

Instance size Total instance pool capacity Total free instance pool capacity Max configured instances per host
M5.large 16 6 4
M5.xlarge 8 3 2
M5.2xlarge 8 3 2
M5.4xlarge 8 3 2
M5.8xlarge 4 2 1

For all instance pools, the number of available instances is greater than the maximum number of instances configured on a single host. Therefore, in the event of a failure of any host, instances can be moved to the existing available capacity without any reconfiguration.

We can consider another scenario of running instances on the same set of hosts:

Figure 2: Example configuration with sufficient vCPU capacity

With the usage as shown, four of the configured instance pools have sufficient available capacity. However, the m5.4xlarge instance pool only has one available instance placement, resulting in no tolerance to a single host failure. A single m5 host has a total of 96 vCPU, and in this example the overall capacity of the available slots is 156 vCPU. This means that, with the execution of a capacity task to rebalance the available slots, instances could be restarted after a host failure.

Automating a capacity observability solution

With the release of the capacity task functionality for Outposts, details of instance placement and slot configuration per host are now available within both the AWS Management Console and through the API. With the addition of capacity tasks for Outposts, an automated solution can be created to query this data and provide notifications when the N+M resiliency requirements for your workloads are at risk.

The following diagram shows an example solution to achieve this, with the sample code provided in the AWS Samples GitHub repository. The solution is deployed using an AWS Serverless Application Model (AWS SAM) template.

Figure 3: Sample code architectural diagram

  1. Amazon EventBridge scheduler initiates an AWS Lambda function on a user defined time basis.
  2. The Lambda function evaluates the Outpost rack capacity, creates and updates Amazon CloudWatch alarms, and initiates regular reporting.
  3. An Amazon Simple Notification Service (Amazon SNS) Topic sends the report to user defined endpoints such as email or Slack.
  4. CloudWatch alarms continually monitor for changes to Outpost capacity.
  5. In the event of alarm thresholds being breached, a Lambda function is invoked to send notifications via SNS to the user defined endpoints.

At the core of the solution are two Lambda functions:

Monitoring stack manager: This Lambda function sets up the dynamic monitoring of the desired N+M resiliency level. It achieves this by creating and updating CloudWatch alarms based on the current capacity configuration of the Outposts being monitored, and the capacity usage for each instance family and type. The function generates detailed reports for each Outpost, identifying any potential resiliency issues for each instance family based on the M value that is specified at the time of deployment.

The detailed report, which is issued via the configured SNS topic, starts with an overall summary that clearly details the status of each instance family and the resiliency status.

Figure 4: Resiliency report summary section

Following the overall summary section, a more detailed analysis is provided for each instance family, looking at resiliency from both instance type and vCPU capacity perspectives. As part of this detailed analysis, the level of risk for each capacity pool is provided alongside a review of available instance capacity and suggested mitigation options.

Figure 5: Resiliency report instance pool analysis section

Figure 6: Resiliency report vCPU analysis section

This summary report is generated on every execution of the Monitoring Stack Manager function, with the default configuration that is triggered by the EventBridge Scheduler set to daily.

Process alarm: When the alarm that is configured by the Monitoring Stack Manager Lambda triggers, the Process Alarm Lambda analyzes Outpost capacity, checking for available free vCPUs within the hosts running the affected instance family. Then, a report is sent via SNS to immediately draw attention to the capacity risk, providing guidance if the resiliency risk can be mitigated through the application of an alternate capacity configuration.

Figure 7: Resiliency alarm notification report

Similar to the report generated by the Monitoring Stack Manager function, a more detailed breakdown of the capacity issue is provided that allows for easy identification of any necessary follow up actions. These actions are recommendations for manual resolution of the issue and require you to take action to implement.

When the available capacity returns to a level that matches the N+M resiliency requirements you defined, a further notification report is sent to confirm this, and the alarm is reset.

You may also prefer to integrate notifications into platforms such as Slack or Microsoft Teams. One option for this is to use a Lambda function to rewrite the Amazon SNS notification to publish the message through a Webhook. For more information on this, go to How do I use webhooks to publish Amazon SNS messages to Amazon Chime, Slack, or Microsoft Teams?. Alternatively, for sending messages to Slack, users can use Slack’s email-to-channel integration, which allows Slack to accept email messages and forward them to a Slack channel. For more information, go to Configure Amazon SNS to send messages for alerts to other destinations.

Considerations for deploying this solution

The sample solution provided has been designed to work for users who are operating Outposts at any scale. However, there are some considerations for deploying:

  1. The solution is deployed within the AWS account that owns the Outpost, rather than workload/consumer accounts that might be using Outposts resources through AWS Resource Access Manager (AWS RAM)
  2. The deployment is AWS Region-specific. Therefore, it would need to be deployed in each AWS Region you’re using Outposts in.
  3. Each stack deployment supports dedicated N+M configuration monitoring, allowing you to create separate deployments to match the desired resilience requirements across multiple Outposts.

Cleaning up

Because this solution is implemented through AWS SAM, the only clean up required is to execute the AWS SAM deployment using the cleanup parameter as documented in the code repository readme file.

Conclusion

In this post, we reviewed how to calculate N+M resilience for Outposts rack deployments, and provided a sample solution that can dynamically monitor and report on capacity constraints. Making sure that there is sufficient available capacity within an Outpost rack to tolerate failures is critical to running resilient applications and minimizing any potential downtime. Combining good capacity management practices with service functionality, such as EC2 Auto Scaling, automatic instance recovery, and placement groups, gives you several options to make sure workloads can continue to run even during failure events. If you need any assistance calculating your Outposts rack resiliency, or further information on deploying and running fault tolerant workloads, reach out to your AWS Account team.

Powering generative AI/ML solutions with AWS Outposts Servers at Edge locations

Post Syndicated from Art Baudo original https://aws.amazon.com/blogs/compute/powering-generative-ai-ml-solutions-with-aws-outposts-servers-at-edge-locations/

This post is written by Brian Daugherty, Principal Solutions Architect, Leonardo Queirolo, Senior Cloud Support Engineer, and Reet Kundu, Senior Cloud Support Engineer

Powering generative AI/ML solutions with AWS Outposts Servers at Edge locations

Many organizations are vigorously pursuing generative AI initiatives in the Amazon Web Services (AWS) cloud today because generative AI drive advances in productivity, efficiency, and innovation.

However, for some organizations, industries, and use-cases, there is a compelling need to deploy generative AI not only in the cloud, but also at the edge due to factors such as application latency and proximity to critical data.

AWS Outposts can help these organizations address this need by extending AWS services to the edge, such as generative AI services, while maintaining the same tooling and orchestration capabilities found in AWS Regions.

Industrial and manufacturing use-cases are a primary focus of AWS Outposts Servers, which can be deployed on-premises to minimize latency and make sure of stable connectivity between orchestration and control applications such as Manufacturing Execution Systems (MES) or Supervisory, Control, and Data Acquisition (SCADA) systems and the industrial processes they control.

This post explores how to use Outposts Servers to power generative AI solutions at the edge. The example use-case demonstrates real-time anomaly detection for industrial processes and an edge-based human machine interface including a small language model (SLM) with Retrieval-Augmented Generation (RAG) to guide operators on best practices for problem resolution. Although the use case is specific, the tools and methods can be applied to many other edge generative AI use cases.

For a hands-on experience to implement this solution using Outposts Servers, fill out this form with your contact information and we will get back to you with lab access. A detailed step-by-step guide to develop the hands-on example is available in this link.

Architecture overview

As depicted in the following diagram, the solution is distributed in three modules. The first module (1) guides you to establish low-latency, local connectivity to an MQTT broker within the same on-premises network as your lab Amazon Elastic Compute Cloud (Amazon EC2) instance. You configure essential AWS infrastructure (Amazon S3, AWS Secrets Manager, AWS Identity and Access Management (IAM)) to manage the deployment, authentication, and permissions of AWS IoT Greengrass components. You deploy a component to the existing Greengrass core device on your lab EC2 instance to retrieve synthetic Arduino sensor data from the broker using its Local Network Interface (LNI).
Figure 1 – Architectural diagram of the solution to perform low-latency, local inference through generative AI and ML models running on Outposts Servers

Figure 1 – Architectural diagram of the solution to perform low-latency, local inference through generative AI and ML models running on Outposts Servers

In the second module (2), you deploy a component that detects anomalies in sensor data in real-time. This component runs on the Outposts Server EC2 instance hosting the AWS IoT Greengrass core device, performing inference directly at the edge. You use synthetic Arduino sensor data to generate anomalies and observe them being detected by the model. You configure an IoT rule to send the anomaly count to the Amazon CloudWatch Dashboard in the Region. This provides centralized monitoring, while making sure that the raw data and any sensitive data remains processed locally at the edge where latency and connectivity are assured.

In the third module (3), you deploy a comprehensive edge computing solution to enhance operational visibility and decision-making capabilities at the local level. The solution includes a local dashboard that provides a real-time telemetry to display raw sensor data and detect anomalies. A Virtual Assistant is integrated with SLM to provide context-aware response from the factory data and forecasting capability to predict future anomaly trends.

Outposts Server

Outposts Servers provide fully managed AWS infrastructure, services, APIs, and tools for edge use-cases . Two form factors are available: 1U servers are AWS Graviton based, and 2U servers are third-generation Intel Xeon Scalable processor based.

Enabling anomaly detection at the edge

Outposts Servers allow local sensor data processing for low-latency anomaly detection and resilience against external connectivity issues, as shown in the following figure. The example uses synthetic Arduino devices with gyroscope sensors data, simulating industrial sensors sending data to an MQTT Broker on an EC2 instance in the Outposts Server. Gyroscope data is used in various monitoring systems, such as motion control systems, orientation detection, stability, and balance mechanism. The Lab EC2 instance fetches sensor data through the MQTT client and processes it using a machine learning (ML) model for anomaly detection.

Figure 2 – Architectural diagram showing data flow from Arduino sensors through MQTT broker and EC2 on Outposts Server to perform local inference

Figure 2 – Architectural diagram showing data flow from Arduino sensors through MQTT broker and EC2 on Outposts Server to perform local inference

Outposts server LNI

Local communication between synthetic Arduino sensor data, MQTT broker, and the Lab EC2 instance uses LNI, providing Layer 2 presence on the local network. The setup necessitates creating an Elastic Network Interface (ENI) on an Outposts subnet with the LNI enabled, attaching it to the Lab EC2 Instance, and verifying connectivity through the MQTT Broker’s LNI IP using the command ping -c 5 <MQTT_BROKER_LNI_IP> . This enables direct, low-latency communication between components crucial for this edge computing scenario.

AWS IoT Greengrass

AWS IoT Greengrass is an open source edge runtime and cloud service for device software management and deployment supported on Outposts Server. This hybrid approach combines the benefits of edge computing with centralized management, such as:

  • Centralized artifact management: store and version component artifacts in Amazon S3, enabling consistent deployment across multiple edge locations.
  • Secure configuration: use Secrets Manager to handle sensitive information and credentials unique to each edge location.
  • Fleet monitoring: use CloudWatch for centralized monitoring and logging across your distributed edge deployment.
  • Automated updates: deploy software updates and model improvements across your edge fleet through AWS IoT Greengrass component management.

AWS IoT Greengrass components, such as the one used for the anomaly detection, can be deployed to EC2 instances running on Outposts Servers. After configuring the Lab EC2 instance with Greengrass, you can download components from an S3 bucket. The first component deploys a subscriber for receiving synthetic Arduino sensor data through MQTT broker configuration, as shown in the following configuration line.

{
    "broker": "<MQTT_BROKER_LNI_IP>",
    "port": 1883,
    "client_id": "OutpostsServerMLEdge_<workshop-id>",
    "sensor_name": "ArduinoSensor_<arduino-id>",
    "topic": "arduino/ArduinoSensor_<arduino-id>/3-axis-rotation",
    "thing_name": "OutpostsServerMLEdge_Sub",
    "mqttauth_creds": "<ARN_SECRET_MQTT_CREDENTIALS>"
}

The second component is the Anomaly Detector artifact that processes sensor data in real-time, detects anomalies using a pre-trained model, and sends anomaly counts to AWS IoT Core. Key components include:

  • edge_application.py: script for processing sensor data, performing local inference using pre-trained model in ONNX format, and publishing anomaly counts to AWS IoT Core. It is used for local inference, so that the raw data is not exposed outside the Edge location.
  • model: directory storing “arduino.onnx”, a pre-trained Autoencoder model for anomaly detection.
  • statistics: directory storing the values of different statistical functions (for example, mean and standard deviation) from the training phase and used by edge_application.py for inference.
  • functions: directory storing the code of the functions, such as the code to publish to the AWS IoT Core.

After deployment of subscriber and detector components, the Lab EC2 instance processes synthetic gyroscope data from Arduino sensors, detecting anomalies during X, Y, or Z axis movement:

Real-time Dashboard showing sensor data and anomaly count

Real-time anomaly detection results from gyroscope sensor data across X, Y, and Z axes.

Building upon the foundation of Outposts Server, Local Network Interface (LNI), and AWS IoT Greengrass, this solution extends beyond anomaly detection to deliver comprehensive edge AI capabilities. These core components work together to enable advanced generative AI applications at the edge, as demonstrated in the following sections.

Edge generative AI applications with Outposts Server

The solution demonstrates the implementation of key edge generative AI capabilities:

  • Contextual virtual assistance: providing on-site personnel with AI-powered guidance and troubleshooting using local operational data, SOPs, and technical documentation.
  • Predictive insights: using foundational models (FMs) to forecast future trends based on historical data, enabling proactive planning and optimization.
  • Real-time operational dashboard: integrating sensor data visualization with AI-powered insights and forecasts in a unified local interface that maintains operations during connectivity interruptions.

1. Contextual virtual assistance at the edge

The solution implements the virtual assistant through an AWS IoT Greengrass component. The following is a snippet from the component recipe showing the key configuration parameters:

{
    "ComponentConfiguration": {
        "DefaultConfiguration": {
            // Workshop defaults, SLM runs locally on same EC2 instance
            "SLM_endpoint": "http://localhost:8080",  
            "embedding_model": "all-MiniLM-L6-v2",    
            "knowledge_base_directory": "Factory_Data" 
        }
    }
    // Additional component recipe configurations...
}

Although the solution demonstrates a streamlined setup with the SLM running on the same EC2 instance as the AWS IoT Greengrass component, the architecture enables flexible deployment options through the SLM_endpoint configuration. Organizations can:

  • Deploy the SLM on a dedicated resource in their on-premises network (for example "http://<LNI-IP-DEDICATED-RESOURCE>:8080")
  • Use existing hardware infrastructure accessible through LNI
  • Scale SLM compute resources independently from the AWS IoT Greengrass component
  • Maintain low-latency communication through local network interfaces

The implementation showcases a streamlined approach to RAG at the edge through three main components:

Knowledge base management: the solution uses Amazon S3 for document storage (PDFs, Markdown, text) with automatic edge deployment through AWS IoT Greengrass. Alternatively, you can also choose to store the documents in a local storage. A vector database, such as ChromaDB, handles local vector storage and similarity search, enabling efficient knowledge base updates with centralized control.

Flexible query processing: the implementation provides a streamlined interface for RAG management, allowing users to load site-specific knowledge bases and switch between basic SLM and RAG-enhanced responses with local context:

if prompt := st.chat_input("Question"):
if "db" in st.session_state:
        prompt = augmentPrompt(prompt, st.session_state["db"])
response = getStreamingAnswer(prompt, SLM_MODEL_ENDPOINT)

Modular SLM integration: The solution uses a standardized chat completion API, which allows for integration with different SLM deployments while maintaining a consistent interface across the edge fleet:

def getStreamingAnswer(question: str, endpoint: str):    
    chat_template = '<|user|>\n{input} <|end|>\n<|assistant|>'
    payload = {
        'messages': [{'content': f'{chat_template.format(input=question)}'}],
        'stream': True
    }
    SLM_URL = endpoint + '/v1/chat/completions'

This flexible architecture can be adapted for many industrial use-cases where latency and proximity to local data-sources and processes are critical.

2. Predictive insights using local models

The solution demonstrates forecasting capabilities using Chronos, a small and efficient time series forecasting model that can run entirely at the edge. The following solution implementation shows how to process historical data and generate predictions using Chronos on the AWS IoT Greengrass component deployed on Outposts Server:

# Load Chronos model locally on the Outposts Server
pipeline = ChronosPipeline.from_pretrained(
    "amazon/chronos-t5-small",
    device_map="cpu",
    torch_dtype=torch.bfloat16,
)
# Generate forecasts with confidence intervals
def predict_anomaly_count_data():
    forecast = pipeline.predict(
        context = torch.tensor(df["total_anomalies"]),
        prediction_length = pred_length,
        num_samples = n_samples,
        top_k = 50,
        top_p = 1.0,
    )
    
    # Calculate confidence bounds
    low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0)

Although the solution uses sample data for the demonstration, this architecture allows organizations to process complex, real-time data at each edge location. Companies can choose to upload only aggregated metrics to CloudWatch or Amazon QuickSight for fleet monitoring and BI analysis, making sure that sensitive raw data remains secure at the edge.

3. Real-time operational dashboard

The solution showcases a resilient monitoring solution where all inter-component communication occurs within the local network and processing happens on the Outposts server, making sure of full functionality during external network interruptions. The dashboard is accessible through the LNI of the Outposts server, allowing local clients to maintain access through the LNI IP address even when connectivity to the Region is lost.

Through a unified interface, the dashboard provides:

  • Real-time visualization of sensor readings
  • Anomaly detection results from the local ML component
  • AI-powered insights from the local SLM
  • Trend forecasting from the Chronos model

Real-time Dashboard showing sensor data and anomaly count

Real-time Dashboard showing sensor data and anomaly count

Virtual Assistant leveraging Factory Data to provide contextualized answers

Virtual Assistant leveraging Factory Data to provide contextualized answers

Chronos forecasting anomaly count based on historical data

Chronos forecasting anomaly count based on historical data

Conclusion

The implementation demonstrates how AWS Outposts Server enables organizations to use both traditional ML and advanced generative AI capabilities at the edge for a variety of industrial and manufacturing use-cases where low-latency and proximity to sensitive or real-time data are business- and process-critical.

To get started with AWS Outposts and explore use cases like this edge AI solution, fill out this form and our team will contact you with lab access and additional guidance. For a detailed walkthrough of this specific edge AI example, refer to this step-by-step guide. For more information about AWS Outposts Server, see the AWS Outposts Server User Guide.

Anchoring AWS Outposts servers with AWS Direct Connect

Post Syndicated from Art Baudo original https://aws.amazon.com/blogs/compute/anchoring-aws-outposts-servers-with-aws-direct-connect/

This post is written by Perry Wald, Principal GTM SA, Hybrid Edge, Eric Vasquez Senior SA Hybrid Edge, and Fernando Galves Gen AI Solutions Architect, Outposts

AWS Outposts is a fully managed service that extends AWS infrastructure, services, APIs, and tools to customer premises. Outposts servers launched in 2022, a 1U or 2U rack-mountable host, with the ability to run Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic Container Service (Amazon ECS), as well as other appropriate smaller scale edge services such as AWS IoT Greengrass. This version of Outposts is primarily focused on bringing lower latency, AWS compute capabilities to the edge at many user locations.

During Outposts provisioning, you or AWS creates a service link connection that connects your Outposts server to your chosen AWS Region or home Region. Outposts depends on regional connectivity “to reach out to home,” needing very little in terms of networking. Looking at the network requirements, it needs:

  • DHCP, to assign an IP address and a default gateway
  • Public DNS, to resolve the name of the initial regional endpoint, to allow automated setup, and
  • Internet access, so that when the regional endpoint has been resolved, the Outpost can reach that endpoint. With a minimum of 500 Mbps or and a max of 175 ms round trip latency

User challenges with internet connectivity at the edge

When you order an Outposts server, you are responsible for installing the server. Outposts servers are self-provisioning and need a service link connection between your Outposts and the AWS Region (or home Region). This connection allows for the management of Outposts and the exchange of traffic to and from the AWS Region. Server deployment can be broken down into the following steps: installing the Outposts servers, powering them on, and providing authentication details through a command line. Then, the Outpost servers reach out to the regional endpoint, and provision themselves. Your Outpost status will show as Active when the process has completed, it could take a few hours depending on service link bandwidth.

Although this has been suitable for the vast majority of use cases, there are some locations that can’t provide internet connectivity in their environments. This has mostly been in use cases where there is a strong security reason for not having an internet connection (such as financial services kiosks, small manufacturing facilities, and defense), so as to avoid risks such as DDoS attacks and potential hack attempts, or to meet requirements for receiving an authority to operate (ATO).

These locations either have some form of direct connect, or more commonly have a centralized direct connect link to AWS, and an MPLS network linking all their remote sites to a central one. In both of these scenarios, the requirement is to allow the Outpost servers to resolve and reach the public endpoint for setup, and subsequently the public anchor endpoint for management. This is done without needing to leave the AWS ecosystem, without needing to expose themselves unnecessarily to potential internet threats, and without adding more systems to manage themselves, but rather making use of AWS services.

To meet this requirement, we identified several key things that need to be provided if the user does not have internet connectivity at the remote location, as follows:

  1. DHCP, to provide the Outposts servers with an IP address, default gateway, and DNS servers.
  2. Public DNS access to resolve both the setup endpoint, and when live, the anchor endpoint.
  3. Public internet access, without exposing the user location to potentially harmful traffic from the internet.

Direct Connect VIF options

There are three different types of Virtual Interfaces (VIF) possible to configure on an AWS Direct Connect link:

  • Public VIF: A public VIF can access all AWS public services using public IP addresses.
  • Private VIF: A private VIF should be used to access an Amazon Virtual Private Cloud (Amazon VPC) using private IP addresses.
  • Transit VIF: A transit VIF should be used to access one or more Amazon VPC Transit Gateways associated with Direct Connect gateways.

Transit VIF option

A transit VIF can be used to solve both of these issues. First, a transit VIF deploys an ENI within a VPC (known as an attachment), so that traffic coming from the transit VIF into a VPC can be routed. This is because it follows the rule that, for non-transitive VPC routing, the traffic has to either be sourced or targeted for an ENI in the VPC.

If the traffic is forwarded to a regional VPC through the transit gateway, then it can be forwarded to the internet through an NAT gateway. This is an enhancement of the architecture to use a transit gateway to provide a single egress point for multiple VPCs to the internet. For more information, see Creating a single internet exit point from multiple VPCs Using AWS Transit Gateway. In this case, instead of the transit gateway routing multiple VPCs to the internet, it’s routing to an on-premises connection.

Using a transit gateway to forward traffic to an NAT gateway allows you to provide internet connectivity for the Outposts servers without managing virtual appliances, because NAT gateway provides this as a service. NAT gateways also only allow outbound access, so they provide security against any attempted external access by a bad actor from the internet. This works for Outposts servers since they only need outbound access. Outposts always initiate communication to an anchor or service endpoint, and they never receive communication except as a response.

Architectural diagram showing the use of a Transit VIF and NAT gateway in a Region reaching regional endpoints

Figure 1. Architectural diagram showing the use of a Transit VIF and NAT gateway in a Region reaching regional endpoints

DNS provisioning

Although the preceding architecture solves the challenge of how we provide a path for IP packets to transit between the Outposts servers and the public endpoints needed, it doesn’t solve the issue of resolving DNS names. If the remote site is isolated from the internet, then it has no clear way to resolve DNS.

Amazon Route53 resolver endpoints allow you to deploy an IP address within a VPC subnet, which provides DNS resolution. There are two types of resolver endpoints: outbound and inbound.

Outbound resolver endpoints are used by AWS to send DNS queries to your on-premises DNS servers. Inbound resolver endpoints are used by your DNS servers (and hosts) to resolve addresses within Route 53.

Route 53 can resolve public DNS names, so the Outposts service endpoint outposts.<region-name>.amazonaws.com becomes resolvable by an inbound resolver endpoint.

Configuring the Outposts egress VPC

  1. Set up service link egress VPC, build subnets, deploy a NAT gateway, and transit gateway.
  2. Create Route 53 resolver inbound endpoint.
  3. Configure DHCP on the switch, and make sure that the DNS value matches resolver endpoint.
  4. Configure Transit VIF on the switch, build a BGP peer, and attach to your transit gateway.
  5. Confirm propagation settings on transit gateway and default routes.
  6. Confirm routes on subnets to allow traffic out to the internet, and back to your Outpost servers.
  7. Test name resolution (dig) and https (curl) test to service endpoint.
  8. If needed, install your Outpost servers.

Public VIF option

Using a public VIF allows you to provide an internet connection directly to the on-premises site. In turn, this means you need to implement firewalls and security functions on this connection, adding more layers of operational overhead. A public VIF also means that the on-premises end of the VIF can be accessed by any public IP on the AWS public network, regardless of the instance to which IP is mapped. A public VIF is a public IP endpoint on the AWS public network. You should treat public VIF traffic as internet-based traffic. This can become cumbersome for firewalls teams if they have to allow-list known AWS IP ranges and manage the stateful firewall for a long range of AWS IPs.

Furthermore, even if the user is happy to implement and manage a firewall on the end of that public VIF, there is still a question of how the Outpost would resolve DNS in this setup, and subsequent anchor endpoints. Unless the private network already has DNS resolution to a public DNS, then there are no DNS servers that DHCP can point to in order to allow the Outposts servers to get name resolution. This is because there is no public DNS endpoint within the AWS public network. Traffic from a user’s public VIF can access the AWS public network, but it can’t exit it to other public networks. For example, if the you had configured DHCP to point to one of the well-known DNS servers (such as 8.8.8.8), then, since this DNS servers lives outside of the AWS public network, requests originating from the on-premises side of a public VIF would be dropped as it hit the border of the AWS autonomous system.

The only way for a DNS request to be resolved would be to build a bind forwarding service within a VPC, provide it with a public IP address, and point the DHCP DNS values at this IP address.

This network configuration introduces complexity, and won’t be possible for those with highly regulated workloads. You would need to manage a firewall on-premises, allow a public network to reach the on-premises location, and manage a bind servers setup within a VPC. For these reasons, a public VIF is generally not an option unless the user is already running one, and is familiar with the steps to secure it.

Figure 2. Architectural diagram showing traffic flow using a public VIF and AWS Outposts

Private VIF option

A private VIF whether connected directly to a virtual private gateway (VGW), or through a Direct Connect gateway. VPCs do not support transitive routing. To explain this another way, any traffic following a routing rule in a subnet route table has to either originate from, or be destined for, an IP address (or to be more explicit, an Elastic Network Interface (ENI)) inside that VPC.

Virtual private gateways do not have an ENI associated with them, but are pointed to as a next hop within a subnet routing table. If we take this example and look at what the Outposts servers would be trying to pass as traffic, then it would send a packet with a source address of the Outposts servers, and a destination address of the Outposts service public endpoint (assuming that it could resolve it). When this packet reaches the VPC, then neither the source nor destination address would belong to an ENI within the VPC. Therefore, VPC routing would drop the packet.

Even if there was a routing rule on the subnet pointing the next hop for all traffic to a NAT gateway (ideal for internet egress), the routing still wouldn’t work. This is because the packet from the Outposts servers doesn’t have a destination of the NAT gateway, but instead a destination of the setup endpoint in the internet.

It’s possible to use a combination of ingress routing and transparent proxies to ingest the traffic and pass it to an instance running a proxy service to forward to the internet. However, this adds complexity having to manage and maintain proxy servers. For these reasons, a private VIF is generally not recommended.

Architectural diagram showing VGW and packet drops because of transitive routing not being supported

Figure 3. Architectural diagram showing VGW and packet drops because of transitive routing not being supported

Conclusion

In this post, we discussed architecture patterns you can use to provision your Outposts when public internet connectivity is unavailable. To get started with Outpost servers please visit our Server User Guide. For more information, contact us to learn more.

Implementing network traffic inspection on AWS Outposts rack

Post Syndicated from Art Baudo original https://aws.amazon.com/blogs/compute/implementing-network-traffic-inspection-on-aws-outposts-rack-2/

This post is written by Arun Kumar N C, Technical Account Manager; Debapriyo Jogi, Technical Account Manager; and Ashish Nagaraj, Cloud Support Engineer 2

Organizations are increasingly adopting hybrid cloud architectures that combine the scalability of cloud computing with the control and compliance benefits of on-premises infrastructure. AWS Outposts extends AWS infrastructure, AWS services, APIs, and tools to on-premises locations for workloads that require low latency, local data processing, or data residency. Outposts comes in a variety of form factors, from 42U Outposts racks to 1U and 2U Outposts servers. This post will focus on implementing network traffic inspection on Outposts rack.

Comprehensive security is critical for organizations deploying production workloads on Outposts. Network traffic inspection serves as a crucial security control, protecting against threats while enabling secure communication between different network segments. This post provides guidance on how to implement effective network traffic inspection across your hybrid cloud infrastructure using Outposts rack.

Overview

In the coming sections we will cover strategies for network traffic inspection on Outposts rack, focusing on outbound internet access and communication with on-premises networks. We explore AWS native services and third-party tools, offering a comprehensive overview of your options. We will cover architectural patterns, implementation guides, and best practices to help build a strong security posture for your hybrid cloud environment.

Securing internet-facing applications

Securing internet-facing applications on Outposts requires a robust, multi-layered approach for high availability and comprehensive security. We will explore two key architectural patterns that ensure enterprise-grade security for your workloads below.

Amazon CloudFront with AWS WAF integration

This architecture uses multiple AWS services including AWS Shield and AWS WAF for multi-layered security, Amazon CloudFront for global content delivery, and an Application Load Balancer (ALB) on Outposts for on-premises traffic management. Applications are deployed on Outposts, with CloudFront as the content delivery network. AWS WAF rules on CloudFront protect against web exploits, while the ALB distributes requests to application instances within Outposts.

This diagram illustrates AWS CloudFront with WAF integration connecting AWS cloud services to a customer data center through the internet. The setup includes CloudFront protected by WAF and Shield, EC2 instances in a VPC, and an Outpost deployment in the customer data center with ALB and EC2 instances.

Figure 1 – Amazon CloudFront with AWS WAF integration

  1. User sends a request via web browser or mobile app to access the application.
  2. The request is received by the CloudFront in AWS Edge Location, performing content-based routing.
  3. CloudFront integrates with AWS WAF to filter web traffic and block common attack patterns.
  4. ALB routes it to the appropriate targets.
  5. The application on Outposts processes the request and generates a response.

This flow ensures secure and efficient handling of user requests using both cloud and on-premises resources.

ALB with AWS WAF


This architecture offers more control over traffic routing while using AWS WAF for security. Applications are deployed on Outposts, but the ALB is in the parent Region, as AWS WAF cannot be associated with Outposts ALBs. The regional ALB handles incoming traffic, with AWS WAF providing firewall capabilities. After passing through AWS WAF, traffic is routed to Outposts applications. This configuration allows advanced WAF features but may introduce latency, as traffic must first reach the regional ALB. This trade-off between security and latency should be considered based on application needs.

Note: A critical dependency exists on the service link connection, as application traffic routing relies on the regional ALB. Service link failures will disrupt workload operations, making connection resilience essential for this architecture.

Figure 2 – ALB with AWS WAF

  1. User sends a request via web browser or mobile app for a webpage, API call, or service.
  2. The ALB in the AWS Region receives the request and performs Layer 7 content-based routing.
  3. ALB integrates with AWS WAF for security inspection.
  4. If the request passes, ALB routes it to the appropriate target in Outposts, selecting a specific instance or service.
  5. The application on Outposts processes the request, generates a response, and returns it.
  6. The response travels back through Outposts ALB to the regional ALB, which forwards it to the user’s browser or app.

Inspection between the Outpost subnet and regional subnet

Network traffic inspection between the Outpost and regional subnets is vital for security in hybrid cloud deployments. It makes sure traffic between Outposts and the parent Region complies with security policies and requirements. Two main architectural approaches exist for implementing this inspection:

  1. Using a third-party firewall in the Outpost subnet.
  2. Using AWS Network Firewall in an AWS Region.

Both approaches support various connectivity (service link) options between Outposts and the Region, including AWS Direct Connect.

Using third-party firewall in the Outpost subnet

This architecture uses a third-party firewall in the Outposts subnet, routing all traffic between the Outposts and regio0nal subnets through it. This setup enables local traffic inspection, reducing latency while enforcing security policies before traffic leaves the Outposts.

This diagram illustrates AWS Region and customer data center connected via service-linked VPN. Outpost deployment includes third-party firewall EC2 instance and target EC2 instances in Outpost subnet.

Figure 3 – Third-party firewall in the Outpost subnet

Traffic can originate from either Outposts or AWS regional subnet.

  1. Traffic originating from the Outpost to AWS Region:

a. Traffic is sent to the third-party firewall in the Outpost.
b. The firewall inspects the traffic and applies security policies.
c. If allowed, the firewall forwards traffic to the Region.
d. Traffic travels via service link connectivity (Direct Connect or public internet) to the regional subnet.

  1. Traffic originating from AWS Region to the Outpost:

a. Traffic originates in the regional subnet.
b. Traffic travels via service link connectivity (Direct Connect or public internet).
c. Upon reaching the Outpost, the traffic is sent to the third-party firewall.
d. The firewall inspects packets and applies security policies.
e. If allowed, the firewall forwards traffic to the Outpost subnet destination.

Using AWS Network Firewall in an AWS Region

In this architecture, a Network Firewall is deployed in the regional VPC, routing all traffic between the Outpost and regional subnets through it. This centralized approach ensures consistent policy enforcement with AWS native tools. The firewall inspects all traffic between Outposts and the AWS infrastructure in the Region.

This diagram illustrates AWS Network Firewall in a Region connected to customer data center via service-linked VPN. Includes VPC with Network Firewall, and EC2 in Outpost routing the traffic through the AWS Network Firewall endpoint.

Figure 4 – AWS Network Firewall in an AWS Region

Traffic can originate from either the Outposts subnet or AWS regional subnet.

All traffic is routed to the Network Firewall in the AWS Region.

  1. The firewall applies configured rules, including:
  • Custom rules for specific security needs.
  • Managed AWS rule groups for common threats.
  • Third-party rule groups for specialized protection.
  1. If traffic passes all rules, it is forwarded to its destination (Outpost or Region).
  2. Return traffic follows the same path, all traffic is inspected by the Network Firewall.

Inspection between on-premises and Outposts through Local Gateway

Network traffic inspection between on-premises networks and Outposts via Local Gateway (LGW) is essential for securing hybrid environments. It helps you make sure safe communication is happening between Outposts workloads and on-premises infrastructure.
Two primary architectural approaches are available explained below. The choice depends on infrastructure, security needs, and operational preferences.

Using third-party firewall on Outposts

For more details on implementing network traffic inspection between on-premises networks and Outposts via LGW, refer to Implementing network traffic inspection on AWS Outposts rack.

This post expands on the preceding blog by offering detailed guidance on architectural options and traffic flows for inspecting network traffic between on-premises environments and Outposts via LGW.

Using your on-premises router/firewall

This approach uses the existing firewall capabilities of your on-premises router/firewall. The network is configured to route all traffic between the on-premises environment and Outposts through this router/firewall. The LGW on your Outpost connects directly to your router/firewall, which handles the firewall functions. This setup uses the on-premises security infrastructure and policies, ensuring continuity in security management while integrating Outposts into the broader network security strategy.

Traffic flow:

  1. Traffic originates from on-premises network
  2. Passes through your router with the firewall
  3. Router inspects the traffic
  4. If allowed, traffic is sent to Outposts through the LGW
  5. Outbound inspection to the internet from Outposts instances

Outbound inspection to the internet from Outposts instances

Outbound internet traffic inspection for Outposts instances is useful for security and controlling access to external resources. Three architectural approaches are available for implementing this inspection, which are discussed in the following sections.

Using Customer-Owned IP (CoIP) with on-premises firewall

In this architecture, Outposts instances are assigned Customer-Owned IP (CoIP) addresses, with all outbound internet traffic routed through the on-premises network and firewall. The LGW connects the Outposts environment to the on-premises network. This setup enables organizations to leverage existing on-premises security and internet connectivity while ensuring consistent IP addressing across their hybrid environment.

This diagram illustrates Customer-Owned IP (CoIP) implementation in a customer data center, where Outpost EC2 instances use CoIP addresses, routing through LGW to an on-premises firewall for inspection.

Figure 5 – Customer-Owned IP (CoIP) with on-premises firewall

  1. An Outposts instance with a CoIP address initiates outbound internet traffic.
  2. The traffic is routed to the LGW on the Outpost.
  3. The LGW forwards the traffic to the on-premises network.
  4. The traffic reaches the on-premises firewall and inspects the traffic, applying security policies and rules.
  5. If allowed, the firewall forwards the traffic to the internet through the on-premises connection.
  6. Return traffic follows the reverse path, being inspected by the firewall before reaching the Outposts instance.

Using CoIP with third-party firewalls on Outposts

Using this configuration, you would assign a CoIP addresses to your Outposts instances and deploy a third-party firewall appliance directly on the Outposts rack. Outbound internet traffic from these instances is routed through the local firewall running on EC2 before reaching the internet via the LGW. This approach ensures local traffic inspection while preserving the advantages of CoIP addressing, enabling seamless integration with existing IP management systems.

This diagram illustrates third-party firewalls on Outposts as EC2 instances. Customer data center contains Outpost subnet with EC2 instances using CoIP, connected to LGW. Traffic routes through Customer Edge Router/Firewall before reaching Internet

Figure 6 – CoIP with third-party firewalls on Outposts

  • An Outposts instance with a CoIP address initiates outbound internet traffic.
  • The traffic is routed to the third-party firewall deployed on the Outpost.
  • The firewall performs deep packet inspection, applying security policies and rules.
  • If allowed, the firewall forwards the traffic to the LGW.
  • The LGW sends the traffic to the internet through the on-premises connection.
  • Return traffic follows the reverse path, being inspected by the firewall before reaching the Outposts instance.

Using Internet Gateway (IGW) with Network Firewall in the Region

This architecture provides secure outbound internet access for Outposts workloads by using services in the parent Region. The VPC extends to include the Outposts rack, with internet-bound traffic routed via the service link to the AWS Region. In the Region, the Network Firewall inspects the traffic before forwarding it to the Internet Gateway (IGW) for internet access.

Traffic flow:

  1. Traffic is sent to the parent Region via the service link.
  2. In the Region, traffic is routed to the Network Firewall.
  3. The Network Firewall inspects the traffic and applies rules.
  4. If allowed, traffic is forwarded to the IGW via the NAT Gateway.
  5. The IGW sends the traffic to the internet.
  6. Return traffic follows the reverse path, inspected before reaching Outposts.

Conclusion

Implementing effective network traffic inspection for AWS Outposts requires a strategic approach balancing security, efficiency, and architectural complexity. We’ve explored multiple architectural patterns for implementing network traffic inspection with Outposts rack.

Reach out to your AWS account team or AWS support to learn more about inspection in Outpost.

Migrating your on-premises workloads to AWS Outposts Rack

Post Syndicated from Art Baudo original https://aws.amazon.com/blogs/compute/migrating-your-on-premises-workloads-to-aws-outposts-rack-2/

This post is written by Craig Warburton, Senior Solutions Architect, Hybrid; Sedji Gaouaou, Senior Solutions Architect, Hybrid; and Brian Daugherty, Principal Solutions Architect, Hybrid.

Migrating workloads to AWS Outposts Rack offers you the opportunity to gain the benefits of cloud computing while keeping your data and applications on premises.

For organizations with strict data residency requirements, by deploying AWS infrastructure and services on premises, you can keep sensitive data and mission-critical applications within your own data centers or facilities, helping ensure compliance with data sovereignty laws and regulatory frameworks.

On the other hand, if your organization does not have stringent data residency requirements, you may opt for a hybrid approach, using both Outposts Rack and the AWS Regions. With this flexibility, you can process and store data in the most appropriate location based on factors such as latency, cost optimization, and application requirements.

In this post, we cover options to migrate your workloads to an Outposts Rack, taking into account your specific data residency requirements. We explore strategies, tools, and best practices to enable a successful migration tailored to your organization’s needs.

Overview

AWS has several services to help you migrate and rehost workloads, including AWS Migration Hub, AWS Application Migration Service, AWS Elastic Disaster Recovery. Alternatively, you can use backup and recovery solutions provided by AWS partners.

At AWS, we use the 7 Rs framework to help organizations evaluate and choose the appropriate migration strategy for moving applications and workloads to the AWS Cloud. The 7 Rs represent:

  1. Rehosting (rehost or lift and shift)
  2. Replatforming (lift, tinker, and shift)
  3. Repurchasing (republish or re-vendor)
  4. Refactoring (re-architecting)
  5. Retiring
  6. Retaining (revisit)
  7. Relocating (remigrate).

This post focuses on rehosting and the services available to help rehost on-premises applications to Outposts Rack.

Before getting started with any migration, AWS recommends a three-phase approach to migrating workloads to the cloud (AWS Region or Outposts Rack). The three phases are assess, mobilize, and migrate and modernize.

Figure 1: Diagram showing the three migration phases of assess, mobilize, and migrate and modernize

Figure 1: Diagram showing the three migration phases of assess, mobilize, and migrate and modernize

This post describes the steps that you can take in the migrate and modernize phase. However, the assess and mobilize phases are also critical to allow you to understand what applications are migrated, the dependencies between them, and the planning associated with how and when migration occurs.

Workload migration to Outposts Rack: With staging environment in a Region

After deploying an Outposts Rack to your desired on-premises location, you can perform migrations of on-premises systems and virtual machines using either Application Migration Service and AMI creation or third-party backup and recovery services. Both scenarios are described in the following sections.

Scenario 1: Using Application Migration Service with AMI creation

Application Migration Service is able to lift and shift a large number of physical or virtual servers without compatibility issues, performance disruption, or long cutover windows.

In this scenario, at least one Outposts Rack is deployed on premises with the following prerequisites:

  • An AWS Replication Agent installed on each source server
  • At least one Outposts Rack installed and activated
  • VPC in an AWS Region
  • Staging subnet for staging migrated instances
  • Cutover subnet to validating migrated instances
  • Extended VPC spanning Region to the Outposts Rack
  • Migrated resources subnet where instances will be deployed from AMIs

The following diagram shows the solution architecture including the prerequisites and the on-premises servers that will be migrated to the Outposts Rack.

Figure 2: Architecture diagram showing migration with Application Migration Service

Figure 2: Architecture diagram showing migration with Application Migration Service

Step 1: Outposts Rack configuration

You can work with AWS specialists to size your Outposts for your workload and application requirements. In this scenario, you don’t need additional Outposts Rack capacity for migration because the staging area will be deployed in the Region (see 1 in Figure 2).

Step 2: Prepare Application Migration service

Set up Application Migration Service from the console in the Region to which your Outposts Rack is anchored. If this is your first setup, then choose Get started on the Application Migration Service console. When creating the replication settings template, ensure that your staging area is using subnets in the anchor Region (see 2 in Figure 2).

Step 3: Install the AWS Replication Agent to the source servers or machines

For large migrations, source servers may have a wide variety of operating system versions and may be distributed across multiple data centers. Application Migration Service offers the MGN connector, a feature that allows you to automate running commands on your source environment. Finally, ensure that communication is possible between the agent and Application Migration Service (see 3 in Figure 2).

In the following image, there is an example of deploying the AWS Replication Agent providing the necessary parameters (AWS Region, AWS access key and AWS secret access key).

Figure 2: Architecture diagram showing migration with Application Migration Service

When the AWS Replication Agent is installed, the server is added to the Application Migration Service console. Next, it undergoes the initial syncronization process, which is completed when showing the Ready for testing lifecycle state in the Application Migration Service console.

Step 4: Configure launch settings

Prior to testing or cutting over an instance, you must configure the launch settings by creating Amazon Elastic Compute Cloud (Amazon EC2) launch templates, ensuring that your cutover subnet is selected and that you choose an available instance type (see 4 in Figure 2). The instance type right-sizing feature allows AWS Application Migration Service to launch a test or cutover instance type that best matches the hardware configuration of the source server, by selecting the Basic option, AWS Application Migration Service will launch a test or cutover AWS instance type that best matches the OS, CPU, and RAM of your source server.

Step 5: Install AWS Systems Manager Agent on your cutover instances. When the launch settings are defined, you must activate the post-launch actions for either a specific server or all the servers. You must leave the Install the Systems Manager agent and allow executing actions on launched servers option toggled on in order for post-launch actions to work. Untoggling the option would disallow Application Migration Service to install the AWS Systems Manager Agent on your servers, and post-launch actions would no longer be executed (see 5 in Figure 2).

Figure 3: Post-launch actions on the Application Migration Service console

Figure 3: Post-launch actions on the Application Migration Service console

Step 6: Testing and cutover in Region

When you have configured the launch settings for each source server, you are ready to launch the servers as test instances. Best practice is to test instances before cutover.

Figure 4: Application Migration Service console ready to launch test instances

Figure 4: Application Migration Service console ready to launch test instances

Finally, after completing the testing of all the source servers, you are ready for cutover (see 6 on Figure 2). Prior to launching cutover instances, check that the source servers are listed as Ready for cutover under Migration lifecycle and Healthy under Data replication status.

Figure 5: Application Migration Console ready for cutover

Figure 5: Application Migration Console ready for cutover

To launch the cutover instances, choose the instances you want to cutover and then choose Launch cutover instances under Cutover (see Figure 5). The Application Migration Service console indicates Cutover finalized when the cutover has completed successfully the chosen source servers’ Migration lifecycle column shows the Cutover complete status, the Data replication status column shows Disconnected, and the Next step column shows Mark as archived. The source servers have now been successfully migrated into AWS. You can now archive your source servers that have launched cutover instances.

Step 7: Create a Migration AMI

After migrating all your workloads in the region where the Outposts is anchored to, create Amazon Machine Images (AMI). When you create an AMI from an instance, Amazon EC2 powers down the instance before creating the AMI to make sure that everything on the instance is stopped and in a consistent state during the creation process. If you are confident that your instance is in a consistent state appropriate for AMI creation, you can tell Amazon EC2 not to power down and reboot the instance.

This step can be automated using an existing Post Launch Action.

Step 8: Launch instances on AWS Outposts

The final part is to launch your created AMIs to your Outposts. To identify the EC2 instances configured on your Outpost you can use the following AWS Command Line Interface (AWS CLI):

Outposts get-outpost-instance-types \

–outpost-id op-abcdefgh123456789

The output of this command lists the instance types and sizes configured on your Outpost:

InstanceTypes:

– InstanceType: c5.xlarge

– InstanceType: c5.4xlarge

– InstanceType: r5.2xlarge

– InstanceType: r5.4xlarge

With knowledge of the instance types configured, you can now determine how many of each are available. For example, the following AWS CLI command, which is run on the account that owns the Outpost, lists the number of c5.xlarge instances available for use:

aws cloudwatch get-metric-statistics \

–namespace AWS/Outposts \

–metric-name AvailableInstanceType_Count \

–statistics Average –period 3600 \

–start-time $(date -u -Iminutes -d ‘-1hour’) \

–end-time $(date -u -Iminutes) \

–dimensions \

Name=OutpostId,Value=op-abcdefgh123456789 \

Name=InstanceType,Value=c5.xlarge

This command returns:

Datapoints:

– Average: 10.0

Timestamp: ‘2024-04-10T10:39:00+00:00’

Unit: Count

Label: AvailableInstanceType_Count

The output indicates that there were (on average) 10 c5.xlarge instances available in the specified time period (one hour). Using the same command for the other instance types, you discover that there are also 20 c5.4xlarge, 10 r5.2xlarge, and 6 r5.4xlarge available for use in completing the necessary EC2 launch templates.

Scenario 2: Using partner backup and replication solutions

You may already be using a third-party or AWS Partner solution to create on-premises backups of bare-metal or virtualized systems. These solutions often use local disk-arrays or object stores to create tiered backups of systems covering restore-points going back years, days, or just a few hours or minutes.

These solutions may also have inherent capabilities to restore from these backups directly to the AWS. This enables migration of on-premises systems to EC2 instances deployed to Outposts Rack.

In the scenario illustrated in Figure 6, the partner backup and replication service (BR) creates backups (see 1 in Figure 6) of virtual machines to on-premises disk or object storage repositories. Using the service’s AWS integration, virtual machines can be restored (see 2 in Figure 6) to an EC2 instance deployed on Outposts Rack, which is also on-premises. The restoration may follow a process that uses helper instances and volumes (see 3 in Figure 6) during intermediate steps to create Amazon Elastic Block Store (Amazon EBS) snapshots (see 4 in Figure 6) and then AMIs of the systems being migrated (see 5 in Figure 6), which are ultimately deployed (see 6 in Figure 6) to Outposts Rack.

Figure 6: Architecture diagram of the partner backup and replication scenario

Figure 6: Architecture diagram of the partner backup and replication scenario

When deploying an AMI created from a restored instance you must specify the target VPC and subnet. These should be the VPC being extended to the Outpost and a subnet that has been created in that VPC on the Outpost. You also need to specify an EC2 instance type that is available on the Outpost, which can be discovered using the process described in the previous section.

Workload migration to Outposts Rack using AWS Elastic Disaster Recovery (DRS)

Data residency can be a critical consideration for organizations that collect and store sensitive information, such as personally identifiable information (PII), financial data, or medical records. AWS Elastic Disaster Recovery, supported on Outposts Rack, helps enable seamless replication of on-premises data to Outposts Rack and addresses data residency concerns by keeping data within your on-premises environment, using Amazon EBS and Amazon S3 on Outposts.

In this scenario, an Outpost Rack is deployed on-premises with the following prerequisites:

  • At least one Outposts Rack installed and activated
  • The Outposts Rack must be in Direct VPC Routing (DVR) mode
  • VPC extended to the Outposts Rack containing subnets for staging and target resources
  • Amazon S3 on Outposts (necessary for all Elastic Disaster Recovery replication destinations)
  • An AWS Replication Agent installed on each source server

The following diagram shows the solution architecture and includes the on-premises servers that are migrated from the local network to the Outposts Rack. It also includes the staging VPC used to deploy the replication servers on Outposts Rack, Amazon S3 on Outposts to store the local Amazon EBS snapshots, and the target VPC extended to Outposts Rack.

Figure 7: Architecture diagram for workflow migration to Outposts Rack

Figure 7: Architecture diagram for workflow migration to Outposts Rack

Step 1: Outposts Rack configuration

To use Elastic Disaster Recovery on Outposts Rack, you need to configure both Amazon EBS and Amazon S3 on Outposts to support continuous replication and point-in-time recovery for your workload needs (see 1 in Figure 7). Specifically, you need to size the Amazon EBS and Amazon S3 on Outposts capacity according to your workload capacity requirements and application interdependencies. To do this, you can define dependency groups: each dependency group is a collection of applications and their underlying infrastructure with technical or non-technical dependencies. A 2:1 ratio is recommended for the EBS volumes to be used for near-continuous replication, and a 1:1 ratio is recommended for the Amazon S3 on Outposts ratio for EBS snapshots. For example, to migrate 40 TB of workloads, you need to plan for 80 TB of EBS volumes and 40 TB of Amazon S3 on Outposts capacity.

Step 2: Extend VPC to your Outposts Rack

When your Outpost has been provisioned and is available, extend the necessary Amazon Virtual Private Cloud (Amazon VPC) connection to the Outpost from the Region by creating the desired staging and target subnets (see 2 in Figure 7).

Step 3: Prepare Elastic Disaster Recovery service

Prepare the Elastic Disaster Recovery service from the Console to set the default replication and launch settings. When defining these settings, make sure that the Outposts resources available are chosen for staging and target subnets and instance and storage type (see 3 in Figure 7).

Step 4: Install the AWS Replication Agent to the source servers or machines

The next phase is to install the AWS Replication Agent to the source servers and to make sure that communication is possible between the AWS Replication Agent and your Outposts replication subnet through the Outposts local gateway, which makes sure that replication traffic uses the local network (see 4 in Figure 7).

Step 5: Continuous block-level replication

Staging area resources are automatically created and managed by Elastic Disaster Recovery. When the AWS Replication Agent has been deployed, continuous block-level replication (compressed and encrypted in transit) occurs (see 5 in Figure 7) over the local network.

Step 6: Launch Outposts Rack resources

Finally, migrated instances can now be launched using Outposts Rack resources based on the launch settings defined previously (see 6 in Figure 7).

Conclusion

In this post, you have learned how to migrate your workloads from your on-premises environment to AWS Outposts Rack based on your specific data residency requirements. When you have the flexibility of using AWS Regional services, AWS migration services or partner solutions can be used with infrastructure already in place. If your data must stay on-premises, then using AWS Elastic Disaster Recovery allows you to migrate your data without using Regional services, allowing you to migrate to Outposts Rack without your data leaving the boundary of a certain geographic location.

To learn more about an end-to-end migration and modernization journey, visit the AWS Migration Hub.

Architecting for seamless on-premises connectivity with AWS Outposts servers

Post Syndicated from aostan original https://aws.amazon.com/blogs/compute/architecting-for-seamless-on-premises-connectivity-with-aws-outposts-servers/

This post is written by Mark Nguyen, Principal Solutions Architect, AWS and Ryan Fillis, Solutions Architect, AWS.

AWS Outposts brings native AWS services, infrastructure, and operating models to virtually any data center, co-location space, or on-premises facility. Deploying Outposts servers in your environment necessitates additional considerations regarding local network connectivity and Amazon Elastic Compute Cloud (Amazon EC2) instance networking. This post demonstrates the scalability of Outposts servers through automation and the deployment of Amazon EC2 network interfaces. This reduces the number of manual steps required to configure an Outposts server.

This post details physically connecting your servers to your Local Area Network (LAN) and the networking options available for EC2 instances running on Outposts. We cover the physical cabling options, virtual networking components such as VPCs and subnets, and walkthrough an example setup for an EC2 instance with a user-data script to route traffic locally over your on-premises network.

This post assumes that you have some familiarity with Outposts servers. If you would like a general refresher, observe What is AWS Outposts. For more information about how to provision your Outposts server, see Installing an AWS Outposts server.

Basic Amazon EC2 networking using a single interface

When launching an EC2 instance on an Outposts server a single interface is created for network connectivity. This default setting, depicted in the following diagram, is the most direct method for your instance to communicate externally.

Figure 1 Simple network connectivity on an Outposts server

Figure 1: Simple network connectivity on an Outposts server

When deploying an EC2 instance to an Outposts server, there are certain differences in using the default Elastic Network Interface (ENI) as compared to deploying in an AWS Region. Understanding these differences is critical before modifying the network configuration, which you do in the next step.

ENI differentiators between Outposts servers and the Region:

  • Primary interface: The primary interface is an ENI. This ENI is associated to a subnet within a VPC. This VPC is extended from the Region to the Outposts server.
  • IP address configuration: The primary network interface within the guest operating system (OS) of the EC2 instance must be configured to obtain an IP address through DHCP. The assigned IP address is from the IP address range of the VPC subnet associated with the Outposts server.
  • Security group: A security group is associated with the ENI. This security group falls within the VPC that is extended from the Region. The user must apply appropriate access control rules to permit access to the EC2 instance. You may reuse security groups that already exist within the VPC.
  • Outbound traffic: By default, an EC2 instance uses its ENI to direct outbound traffic toward the VPC subnet. Traffic flows according to the routing table associated with the Outposts server’s VPC subnet.
  • Inbound traffic: If you’re only using an ENI, then traffic destined to EC2 instances on Outposts servers must traverse through the service link. In the preceding diagram, the user communicates with the EC2 instance over the internet. Traffic from the internet reaches the Region through the Internet Gateway of the VPC. Then, the VPC forwards the traffic to the appropriate subnet of the Outposts server (through the service link) and reaches the EC2 instance. The user must configure the necessary VPC components (Internet Gateway and associated routing table entries) for internet connectivity.
  • Local network connectivity: There is no local network connectivity using the ENI. For local network connectivity, see the next section where we discuss the Outposts server Local Network Interface (LNI).

Local network connectivity for EC2 instances

Outposts servers allow you to communicate through the Local Network Interface (LNI) in addition to the ENI. The LNI is a logical networking component that connects the Amazon EC2 instances in your Outposts subnet to your on-premises network.

The Outposts server EC2 instance local communications characteristics:

  • Local network traffic needs the use of an LNI.
  • The subnets on Outposts servers must be enabled for LNIs. This is done by entering the following command:

aws ec2 modify-subnet-attribute \

--subnet-id subnet-1a2b3c4d \

--enable-lni-at-device-index 1

  • IP address assignment for the LNI can be DHCP or static.
  • You can’t apply VPC security groups to the LNI. To control traffic on the LNI, you can use an OS based firewall, external on-premises firewall, or other security devices.
  • Amazon CloudWatch metrics are produced for each LNI.
  • Outposts servers don’t tag VLAN traffic. If VLAN tags are needed, then the network interface settings inside the guest OS must apply the VLAN tags. Multiple VLAN interfaces can exist within the same LNI (in this case you would be using the LNI as a VLAN trunk).
  • Local traffic bandwidth performance depends on the instance type. The larger the instance type, the higher performance the throughput of the LNI. The maximum throughput is 10 Gbps.
  • EC2 instances that communicate locally always have at least two interfaces: one ENI and one or more LNIs. Therefore, the instance OS’s routing table must be configured based on the desired traffic behavior.

Example configuration: Local traffic for EC2 instance on Outposts server

Figure 2 Example scenario topology

Figure 2: Example scenario topology

In the example scenario, we want to launch an Amazon Linux 2023 instance and route all default traffic through the local network. Eth0 is the primary interface (ENI) and is used for traffic towards the Region. Eth1 is the LNI and is used for all other traffic. A user-data script is used to make the necessary routing changes at launch.

Here is a sample user-data script. These commands run as root so there is no need to prepend each command with sudo.

User data script (my_userdata.txt):

#!/bin/bash 
route add -net 172.31.0.0/16 gw 172.31.239.1 
route del default gw 172.31.239.1 
cp -RL /run/systemd/network/* /etc/systemd/network/ 
echo -e '\n[Route]\nDestination=172.31.0.0/16\nGateway=172.31.239.1\nGatewayOnLink=yes' >> /etc/systemd/network/70-ens5.network 
sed -i -e 's/UseGateway=true/UseGateway=false/g' /etc/systemd/network/70- ens5.network.d/eni.conf

We can break down this script to observe the intent of each command:

route add -net 172.31.0.0/16 gw 172.31.239.1 
route del default gw 172.31.239.1

When an instance is launched on Outposts server, the instance automatically has a default route that points toward the VPC through the ENI. In the example scenario, the desired configuration is to have all default traffic go through the LNI toward our on-premises LAN, not through the ENI. To accomplish this routing behavior for the ENI, we have to add a route toward the VPC and remove the default route. The first line adds a route through the VPC (172.31.0.0/16), using 172.31.239.1 as the gateway. The second line removes the default route that uses 172.31.239.1 (via the ENI) as the gateway.

Traffic not destined for the VPC routes through the LNI. This includes all local traffic and internet-bound traffic. The local network’s DHCP server provides a default-gateway in its DHCP lease. Therefore, there is already a default route assigned to the LNI. This steers any traffic without a more specific route, including internet traffic, toward the LNI.

Next, the user-data script makes the network settings persistent after reboot. The procedure varies depending on your OS. In the case of Amazon Linux 2023, it uses systemd-networkd.

cp -RL /run/systemd/network/* /etc/systemd/network/

This command copies the configuration files from the /run/systemd/network/ folder to /etc/systemd/network/. The configuration files in the /etc/systemd/network/ folder override the default settings and load during boot. The next is step is to modify the newly copied network configuration files.

echo -e '\n[Route] \nDestination=172.31.0.0/16 \nGateway=172.31.239.1 \nGatewayOnLink=yes' >> /etc/systemd/network/70-ens5.network

In this case the ENI is ens5. This line appends the static route section to the 70-ens5.network configuration file. This makes the static route added earlier in the script (route add -net 172.31.0.0/16 gw 172.31.239.1) persistent across reboots.

sed -i -e 's/UseGateway=true/UseGateway=false/g' /etc/systemd/network/70- ens5.network.d/eni.conf

Next, the user-script edits the configuration file, eni.conf, such that the default route isn’t used for the ENI at bootup. This is accomplished using sed to search and replace true with false for the UseGateway parameter.

Launching an instance with ENI and LNI

Now that the user-data script has been created, use the AWS Command Line Interface (AWS CLI) to launch an EC2 instance:

aws ec2 run-instances \
--image-id ami-051f8a213df8bc089 \
--count 1 \
--instance-type c6id.xlarge \
--key-name my_key \
--user-data file://my_userdata.txt \
--network-interfaces '[ \
  { "DeviceIndex":0, "SubnetId":"subnet-0ca6abe6b34adfcce", "Groups": ["sg-0a9f8c2200c0a56f1"] }, \
  { "DeviceIndex":1, "SubnetId":"subnet-0ca6abe6b34adfcce", "Groups": ["sg-0a9f8c2200c0a56f1"] }]' \
--tag-specifications '[{ "ResourceType":"instance","Tags":[ \
  { "Key":"Name", "Value":"server1" } ] }]'

We can break down the parameters used in the preceding command:

--image-id ami-051f8a213df8bc089 \

This specifies the Amazon Machine Image (AMI) ID. ami-051f8a213df8bc089 is the AMI ID for Amazon Linux 2023 in us-east-1.

--count 1 \

This specifies how many EC2 instances to launch. You can launch multiple at the same time.

--instance-type c6id.xlarge

This specifies the instance type. By default, Outposts 2U servers are slotted with the c6id.8xlarge instance type and Outposts 1U servers are slotted with the c6gd.8xlarge instance type. You can adjust the slotting assignment during the ordering process or you can change the slotting assignment later by using the Self-service Capacity Management feature for AWS Outposts.

--key-name my_key

This specifies the public RSA key that is added to your EC2 instance. This key must already be defined in the same Region of your AWS account.

--user-data file://my_userdata.txt

This specifies the filename that contains your user-data script (that was created previously).

{ "DeviceIndex":0, "SubnetId":"subnet-0ca6abe6b34adfcce", "Groups": ["sg-0a9f8c2200c0a56f1"] }, \

{ "DeviceIndex":1, "SubnetId":"subnet-0ca6abe6b34adfcce", "Groups": ["sg-0a9f8c2200c0a56f1"] }]' \

This specifies the network interface configuration. By default, a single network interface, the ENI, is created. This example calls for a second interface for the LNI. DeviceIndex:0 is for the ENI and doesn’t change. DeviceIndex:1 is for the LNI, which we defined when we enabled LNI for the subnet (--enable-lni-at-device-index 1). The SubnetId refers to the subnet that was created on the Outposts server. If you want to deploy to a different Outposts server, then change the SubnetId. Groups refer to the security group that you would like assigned to the ENI. Security groups aren’t supported for the LNI, thus the security group specified for DeviceIndex:1 is only to comply with the command syntax check. A security group will not be applied to the LNI.

--tag-specifications '[{ "ResourceType":"instance","Tags":[ \

{ "Key":"Name", "Value":"server1" } ] }]'

This assigns a name to the EC2 instance, which in this case is server1.

Conclusion

AWS Outposts servers allow you to run native AWS services on-premises by providing local compute. This supports workloads with low latency and data residency requirements through on-premises processing.

Although Outposts servers integrate seamlessly with the AWS cloud, there are some unique networking considerations when deploying in your data center environment. Amazon EC2 instances on the Outposts server can route traffic over the AWS global network, but you can also enable Local Network Interfaces (LNIs) to directly access your on-premises networks.

In this post we’ve demonstrated using user-data scripts during instance launch to automate hybrid cloud networking flows tailored to your requirements. With proper planning, you can use the benefits of consistent AWS services and tooling while maintaining connectivity to your existing on-premises infrastructure.

Ready to get started with hybrid cloud networking on Outposts servers? Check out the Outposts server documentation and best practices guide to begin planning your on-premises deployment.

Dynamically reconfigure your AWS Outposts capacity using Capacity Tasks

Post Syndicated from aostan original https://aws.amazon.com/blogs/compute/dynamically-reconfigure-your-aws-outposts-capacity-using-capacity-tasks/

This post is written by Brianna Rosentrater, Hybrid Edge Specialist SA and Adam Duffield, Senior Technical Account Manager.

AWS Outposts extends AWS infrastructure, AWS services, APIs, and tools to on-premises locations for workloads that require low latency, local data processing, or data residency. Outposts comes in a variety form factors, from 42U Outposts racks to 1U and 2U Outposts servers. Outposts now supports self-service capacity management, making it easy for you to view and manage compute capacity on your Outposts. A default capacity configuration for each new Outpost is determined during the ordering process. This default configuration can subsequently be modified to create a range of instance sizes and quantities to meet your changing business needs. To do so, you create a capacity task, specify the instance sizes and quantity, and run the capacity task to implement the changes. This post focuses on how to use capacity tasks to perform multi-host reconfigurations and view existing capacity configurations.

Overview

Amazon Elastic Compute Cloud (Amazon EC2) capacity on an Outpost is determined by the total volume of compute capacity within the Outpost when ordered. Outposts can also be scaled up or out as needed during your commitment term. For further details on Outpost capacity planning including best practices, refer to the Capacity Planning – AWS Outposts High Availability Design and Architecture whitepaper. We recommend planning spare capacity for N+M host availability when making modifications to your Outpost capacity configuration if your workloads need to be highly available. To calculate, take the number of hosts (N) you need to run all your workloads, and then add (M) additional hosts to meet your requirements for server availability during failure and maintenance events.

Viewing existing Outposts capacity configuration

Outposts users now have visibility into capacity configurations at both an instance family and host level. This gives greater insight into capacity usage and instance placements. Within the Outposts console, after choosing the Outpost ID on which you want to view the capacity configuration, two new views have been provided: the Instance view and the Rack view.

Instance view

The Instance view provides a granular breakdown of the currently deployed instances on the Outpost along with an overall view of instance family capacity pools and their usage, as shown in the preceding figure. The Instances section gives detailed information around the deployed instances, their associated instance ID, instance size, AWS managed service (if applicable), and asset ID of where the instance is running.

Figure 1 - Outposts Instance View

Figure 1 – Outposts Instance View

The Instance capacity distribution summary displays how the various instance sizes are allocated within each instance family, as shown in the following figure. Each host of the same instance family contributes its capacity to the overall pool, which is represented in this section as a percentage rather than number of instance slots. This shows the configured capacity, but it doesn’t reflect any level of usage.

Figure 2 - Outposts Instance Capacity Distribution Summary

Figure 2 – Outposts Instance Capacity Distribution Summary

The Instance capacity distribution details section, shown in the following figure, provides a more detailed breakdown of each instance family capacity pool. This section provides a view of the total available instance capacity, the number of used instances, and the number of instances that are unavailable you at that time (such as when a hardware failure occurs).

Figure 3 - Instance Capacity Distribution Details

Figure 3 – Instance Capacity Distribution Details

Rack view

The Rack view tab provides a more granular view of the overall configuration of each host on a per rack basis, as shown in the following figure. It allows you to analyze the spread and usage of the instance size allocations across each host (asset) and, when choosing the show instance details button, provides the instance ID of each used slot. Using the search box, you can filter by Instance Family or Instance Size to provide a more concise view. If you’re using Outposts server, the Rack view tab will show the capacity configuration of your server.

Figure 4 - Rack View

Figure 4 – Rack View

Obtaining a view of current configuration

Alongside these views, two buttons are available on each of the pages. The Export JSON button gives the ability to download a JSON formatted copy of the current configuration for an Outpost. This is especially useful if you’re looking to record current state, or wanting to use the JSON upload option when submitting a new capacity task. The JSON file structure provides the overall configuration of each capacity pool. However, it doesn’t provide any details in terms of usage. The second button, Modify Instance Capacity, provides a shortcut to creating a capacity task.

This level of capacity visibility is also now available through AWS Command Line Interface (AWS CLI)/Outposts API calls, which some may prefer over the console. For example, the list-assets CLI command can be used to obtain a breakdown of the capacity configuration of each Outpost host:

aws outposts list-assets --outpost-identifier outpost-arn

Figure 5 - list-assets CLI command sample output

Figure 5 – list-assets CLI command sample output

If you want to obtain details of running instances on an Outpost, such as the instance size, the asset ID on which an instance is running, and the related AWS service name (if relevant), then the list-asset-instances CLI command can be used:

aws outposts list-asset-instances --outpost-identifier outpost-arn

Figure 6 - list-asset-instances CLI command sample output

Figure 6 – list-asset-instances CLI command sample output

The list-asset-instances CLI command also allows you to filter through numerous dimensions, such as instance type or AWS service. For example, this can be particularly useful for quickly identifying all running instances of a certain type, such as the m5d.large instance type by using the following command:

aws outposts list-asset-instances --outpost-identifier outpost-arn --instance-type-filter m5d.large

Modifying the Outposts capacity configuration

Due to the finite nature of Outposts capacity, changing operational requirements often mean that adjustments need to be made to your Outposts capacity configuration over time as new workloads are identified or applications need scaling.

From the Outposts console page, choosing Capacity Tasks from the left-hand menu gives a list of previously run capacity tasks and their status. From here, you can choose Create Capacity Task to start the process. To create a capacity task there are two options available: using an interactive capacity configuration tool through the Modify an Outpost capacity configuration option, or uploading a JSON file containing the necessary configuration through the Upload a capacity configuration option.

Figure 7 - Capacity Tasks web form

Figure 7 – Capacity Tasks web form

The interactive Modify an Outpost capacity configuration option using the simple Auto-balance feature and UI is the easiest way for those unfamiliar with Outpost capacity management to get started with making changes. Using this option, you can also choose one of two methods for the task:

  • Run once: This results in the capacity task attempting to run a single time. If any instances block the successful application of the configuration, then the task fails.
  • Run periodically over 48 hours or less: In the event of blocking instances, the capacity task is paused until the instances are stopped. The task rechecks the status every 10 minutes until it can run. If instances aren’t stopped within 48 hours, then the task is cancelled.

To build out the capacity task, capacity pools are displayed, grouped by instance families, and automatically populated with the current configuration of instance sizes and corresponding vCPU allocation. From here, you can make necessary changes to the existing capacity. Specifically, you can add new instance sizes and amend instance quantities in the corresponding fields. The total vCPU count for each instance family will update automatically to reflect your changes. The Auto-balance allows you to automatically adjust the quantities of individual instance sizes to fit within the total vCPU capacity available for the host, which is reflected for each capacity pool at the end. In the event that a capacity pool is over- or under-used, a warning is displayed. You can under provision a host if you choose, but the unprovisioned capacity is unusable, and attempting to overprovision the host results in the capacity task failing to run.

Figure 8 - Modifying existing capacity configuration

Figure 8 – Modifying existing capacity configuration

When the necessary changes have been made to the capacity pools, the second part of the capacity task configuration is choosing instances that should not be impacted by the running of the capacity task. During the run, you may not be in a position to stop certain instances, such as databases or AWS managed services such as Elastic Load Balancing (ELB) or Amazon ElastiCache, due to the impact to production workloads. Choosing these instances allows the capacity task to automatically try to find a path that avoids impacting them. However, in some situations capacity tasks may fail if the chosen instances block the successful running of the task, and there is no possible solution that avoids all the chosen instances. For example, if a capacity task was to remove all c5.xlarge instances and an instance was chosen to ‘keep as-is’ that was running on this instance size, then the task would fail. To avoid this, make sure to include these instances in your capacity configuration. For example, if you have five critical m5.4xlarge instances that must remain running, then include 5 m5.4xlarge instances in your m5 capacity pool configuration.

Figure 9 - Instances to keep as-is

Figure 9 – Instances to keep as-is

After configuring the capacity pools and choosing the necessary instances to keep as-is, an overview of the changes is presented allowing you to validate the configuration prior to running. When you have reviewed the summary, select Create Task to trigger the execution of the capacity task using the chosen method. You can observe the status of a capacity task by choosing the capacity task ID. When it’s initially submitted, the status shows as Requested. During this time, the capacity task evaluates the necessary changes to determine if the task can proceed or if instances need stopping. If the Run once option was chosen and instances do need stopping, then the task moves to a cancelled status and provides details of the blocking instances. Alternatively, if Run periodically was chosen, the task remains in the Requested status until the listed instances have been stopped. In the event that the blocking instances can’t be stopped, the capacity task is cancelled after 48 hours. While a task is running, the Outpost hosts that are impacted by the configuration changes are placed into an isolated state. This means that capacity for new instance launches may be impacted. This isolation only lasts a few minutes while the capacity task is running, but it may impact auto scaling groups if a capacity task coincides with a scaling event.

Instead of using the interactive capacity configurator UI, you can also choose to upload a JSON file to the console containing the necessary configuration. Using this method, choosing instances to keep as-is isn’t available, and the method is automatically chosen as Run once. When a capacity task JSON file is uploaded, the resulting plan is displayed in the following text box and can be amended if needed. Alternatively, rather than uploading the file, the contents can be directly pasted into the text box. Choosing Next moves to the review screen where the remainder of the process continues in line with using the interactive capacity configurator UI.

Figure 10 - Upload a Capacity Configuration using JSON

Figure 10 – Upload a Capacity Configuration using JSON

You may also prefer using the AWS CLI/Outposts API for creating capacity tasks, and a number of new CLI/API actions are now available to support this:

  • cancel-capacity-task / CancelCapacityTask
  • get-capacity-task / GetCapacityTask
  • list-blocking-instances-for-capacity-task / ListBlockingInstancesForCapacityTask
  • list-capacity-tasks / ListCapacityTasks
  • start-capacity-task / StartCapacityTask

In addition to the same options available within the console, it is possible to request a dry run of the capacity task to determine if the instance type and instance size changes are above or below the available instance capacity. Requesting a dry run doesn’t make any changes to your plan.

For example, using the CLI to submit a capacity task to homogeneously slot an Outpost with 2 x m5 and 2 x c5 hosts (192 vCPU for each capacity pool) with xlarge instance sizes, and using periodic running could be achieved by running the following:

aws outposts start-capacity-task \

--outpost-identifier outpost-arn \

--instance-pools '[{"InstanceType":"c5.xlarge","Count":48},{"InstanceType":"m5.xlarge","Count":48}]' \

--task-action-on-blocking-instances WAIT_FOR_EVACUATION

Conclusion

This post demonstrated how to run a capacity task on Outposts and view your existing capacity configuration. For more information on how to manage and monitor your capacity configuration on Outposts, see Capacity management for AWS Outposts user guide and the Capacity planning section of the AWS Outposts High Availability Design and Architecture Considerations whitepaper, and the Modify AWS Outposts instance capacity – Outposts rack/Modify AWS Outposts instance capacity – Outposts server user guide sections for your respective environment. Reach out to your AWS account team to learn more about Outposts and self-service capacity management.

Implementing backup for workloads running on AWS Outposts servers

Post Syndicated from aostan original https://aws.amazon.com/blogs/compute/implementing-backup-for-workloads-running-on-aws-outposts-servers/

This post is written by Leonardo Queirolo, Senior Cloud Support Engineer and Tareq Rajabi, Senior Solutions Architect, Hybrid Cloud

AWS Outposts servers provide fully managed AWS infrastructure, services, APIs, and tools to on-premises and edge locations with limited space or small capacity requirements, such as retail stores, branch offices, healthcare provider locations, or factory floors. Outposts servers provide local compute and networking services.

Outposts servers come with internal NVMe SSD instance storage, supporting local storage used for data access and processing on premises, and for launching Amazon Elastic Block Store (Amazon EBS)-backed Amazon Machine Images (AMIs). The data on these volumes persists after an instance reboot but does not persist after an instance termination. In order for data to persist beyond the lifetime of the instance, it is important to back up your data to a persistent AWS storage, such as an Amazon Simple Storage Service (Amazon S3) bucket or an Amazon Elastic Block Store (Amazon EBS) volume.

In this post, we explore several approaches to back up the data stored in the instance storage volumes of your EC2 instances running on an Outposts server to a persistent storage solution from AWS, and explore their benefits and use cases.

Planning for failure

When evaluating a backup strategy, it’s important to understand the failure modes you are looking to recover from. Some examples are ransomware attacks, accidental data deletion, hardware failure, or a wide scale issue impacting the whole facility where your Outposts servers and on-premises devices (such as network switches, storage appliances) reside. These failures come in many forms and are often unplanned and unexpected events. Next, understand what is considered acceptable recovery for your business. For example, what are the Recovery Time Objective (RTO) and Recovery Point Objective (RPO) for your workload running on Outposts servers? These two values, defined by your organization, profile how long a service can be down during recovery and quantify the acceptable amount of data loss, helping you define the appropriate backup strategy.

Scenario 1: Backup to AWS storage in an AWS Region

Backup to an AWS Region enables data redundancy outside of the data center or facility where your Outpost resides, taking advantage of the durability, high availability, and scalability provided natively by the storage in the Region. This approach offers flexibility for restoration to the Region or to an Outposts server in a different edge location if the original data center/facility is impacted by an irrecoverable incident. However, when restoring the data back to an Outposts server, this approach could result in relatively high RTO, depending on the throughput of the service link and the amount of data to restore. In the following sections, we will cover using the AWS Elastic Disaster Recovery (AWS DRS) and an open source solution based on operating system tools and AWS Systems Manager (AWS SSM).

Option 1: AWS Elastic Disaster Recovery (AWS DRS)

You can use AWS DRS to perform a continuous replication of workloads that reside on the Outposts C6id server powered by Intel processors (C6gd are not supported, since only 64-bit operating systems built for the x86 system architecture are supported by AWS DRS) to a staging area subnet in the Region. AWS DRS provides nearly continuous, block-level replication in the Region and creates periodic EBS Snapshots according to the Point in Time (PIT) state schedule for AWS DRS.

The following diagram shows the continuous replication of the data in the instance store volumes through AWS DRS. The PIT EBS Snapshots are used to create Amazon EBS-backed AMIs as a backup of the EC2 instances running on the Outposts server.

Figure 1 - Continuous replication of the Instance Store Volumes data from the instances running Outpost Server to a staging area in the parent region through DRS

Figure 1 – Continuous replication of the Instance Store Volumes data from the instances running Outpost Server to a staging area in the parent region through DRS

Despite AWS DRS not supporting the failback from the Region to Outposts servers, you can use the EBS snapshots taken by AWS DRS to restore the data back to the Outposts server at the desired PIT following the steps described in this post.

Prerequisites

The following prerequisites are required to complete the walkthrough:

  1. The EC2 instance to restore running on the Outposts server has been added as a source server to AWS DRS by installing the AWS Replication Agent.
  2. The initial sync has been completed and the data replication status is showing as healthy.

Restore the entire EC2 instance on the same or a different Outposts server

  1. Use the describe-recovery-snapshots command to list the PIT Snapshots taken by AWS DRS for the source server to restore.$ aws drs describe-recovery-snapshots --source-server <source-server-id>

2. Based on the time in which you want to restore your data, retrieve the corresponding EBS Snapshots in the output of the command. The following is an example of the output:

{
    "items": [
       {
            "ebsSnapshots": [
                "snap-07bf348d58151a432"
            ],
            "expectedTimestamp": "2024-06-13T16:40:00+00:00",
            "snapshotID": "pit-a4877ff6fa68561bf",
            "sourceServerID": "s-a080ceb10af7275a7",
            "timestamp": "2024-06-13T16:46:56.645979+00:00"
        },
        {
            "ebsSnapshots": [
                "snap-0496020ff7f83486d"
            ],
            "expectedTimestamp": "2024-06-13T16:30:00+00:00",
            "snapshotID": "pit-aece827519e1b0fbb",
            "sourceServerID": "s-a080ceb10af7275a7",
            "timestamp": "2024-06-13T16:37:06.600323+00:00"
        },
        {
            "ebsSnapshots": [
                "snap-0d7ebd23e56346cea"
            ],
            "expectedTimestamp": "2024-06-13T16:20:00+00:00",
            "snapshotID": "pit-a56960f89ff12579e",
            "sourceServerID": "s-a080ceb10af7275a7",
            "timestamp": "2024-06-13T16:27:01.595791+00:00"
        },
…
…

3. Open the Amazon EC2 console. In the navigation pane, choose Snapshots and filter by the Snapshot ID chosen in the previous step: snap-07bf348d58151a432.

4. Choose Actions, Create image from snapshot, and specify the Image name. You can leave the other information as default or customize as desired.

5. To perform the restore, launch a new EC2 instance on the same or a different Outposts server from the Amazon EBS-backed AMI created in the previous step.

Note that since AMIs are downloaded from the Region with every instance launch on Outposts servers, this approach could result in an RTO spanning hours, depending on the throughput of the service link and the size of the local instance storage from which the Snapshot and AMI were taken by AWS DRS. Alternatively, if you need to restore only some files and directories, you can do so by launching the EC2 instance in the Region from the AMI taken in Step 4 and then transferring the desired data from that instance to the source server running on Outposts.

Option 2: Backup to the Region using an open source solution

In addition to AWS DRS, you can use open source solutions and/or operating system (OS) functions to back up data from local instance storage to a Region. Consider this approach when you want a highly-customizable solution for workloads where lack of commercial support is acceptable. The open source solution uses AWS Systems Manager Automation and OS functions to take an Amazon EBS-backed AMI in the Region from a Linux EC2 instance running on your Outposts server. The following diagram provides a high-level overview of the solution.

Figure 2 – Wokflow of the open source solution

Figure 2 – Workflow of the open source solution

  1. The Automation creates a helper instance and a baseline EBS volume attached to it in the Region, using an AWS CloudFormation
  2. The Automation executes commands on the OS of the EC2 instance running on the Outposts server to perform preliminary checks and start syncing data from the local instance store volume to the baseline EBS volume in the Region.
  3. The sync continues until the data has been transferred successfully.
  4. When the sync completes, the Automation takes an EBS Snapshot of the baseline EBS volume and then creates an Amazon EBS-backed AMI from it.

Create the Automation document

  1. Open the github page of the open source solution backup-outposts-servers-linux-instance.
  2. Follow the Installation Instructions to create the Systems Manager Automation document.

Back up an EC2 instance running on Outposts server

  1. After creating the Automation document, follow the Usage Instructions to execute the Automation and initiate the backup.
  2. Monitor the Execution status in the System Manager Automation console.

Restore the entire EC2 instance on the same or a different Outposts server

  1. Open the Amazon EC2 console. In the navigation pane, choose AMIs and filter by the AMI names that contain the InstanceId to restore.

2. Select the desired AMI to restore and note its AMI ID.

3. To perform the restore, launch a new EC2 instance on the same or a different Outposts server from the Amazon EBS-backed AMI identified in the previous step.

Considerations for data residency and service link bandwidth

Data residency is a critical consideration for organizations that need to collect and store data in their own data centers for regulatory or compliance reasons. In this case, users cannot back up their data to the Region and need to consider backing up to another on-premises system.

Another consideration is the impact on the service link connectivity when performing backup and restore operations between the Outposts and the Region. When implementing the solutions described in the “Backup to AWS storage in an AWS Region” scenario, both your backup/restore and management/monitoring operations for your Outpost rely on the service link connectivity. Although AWS DRS provides block-level replication, the open source solution we discuss in this post only replicates data, resulting in smaller snapshot sizes for users with lower service link bandwidth requirement.

If you foresee bandwidth constraints for your service link, consider backing up to another on-premises system that is reachable through the local network interface (LNI) of your Outposts server.

Scenario 2: Backup to AWS storage in your on-premises environment

For the preceding reasons, you may need to back up your workload running on Outposts server to a persistent AWS storage system within the same geo political boundary. To do so, you can use an AWS Outposts rack that resides in the same or a different physical location and is reachable through the LNI of your Outposts server.

Outposts rack with Amazon S3 on Outposts allows you to run AWS infrastructure, services, and object storage to your on-premises to meet local data processing and data residency needs while offering the AWS durable storage that can be used to store your backup.

Thanks to this, you can use the same approaches described in the “Backup to AWS storage in an AWS Region” section at a high level to back up your data, while the storage is hosted on the Outposts rack. When evaluating this approach, keep in mind these important considerations for local snapshots.

With this approach, you can store your backup on premises to meet your data residency requirements. This also keeps the network traffic for your backup and restore within your on-premises network, without impacting the service link.

Conclusion

In this post, we showed different approaches to design backup and restore strategies for your workloads running on Outposts servers. Implementing the right approach can help protect your organization’s data against loss or corruption while meeting your performance, RTO, RPO, and data residency needs, with backup destinations ranging from AWS storage in the Region, locally on Outposts rack, or in a hybrid architecture.

NEW: Simplifying the use of third-party block storage with AWS Outposts

Post Syndicated from Rachel Zheng original https://aws.amazon.com/blogs/compute/new-simplifying-the-use-of-third-party-block-storage-with-aws-outposts/

This post is written by Kate Sposato, Senior Solutions Architect, EC2 Edge Compute

AWS is excited to announce deeper collaboration with industry-leading storage solutions to streamline the use of third-party storage with AWS Outposts. You can now attach and use external block data volumes from NetApp® on-premises enterprise storage arrays and Pure Storage® FlashArray™ directly from the AWS Management Console.

Outposts is a fully managed service that extends AWS infrastructure, AWS services, APIs, and tools to customer premises. By providing local access to AWS managed infrastructure, Outposts allows you to build and run applications on premises using the same application programming interfaces (APIs) as in AWS Regions. Moreover, this is done while using local compute and storage resources to meet lower latency and local data processing needs. Outposts is available in various rack and server form factors.

Many of you have block storage systems running in your on-premises environments that provide advanced data storage and management features—such as snapshots, replication, and encryption—to protect data integrity and security. There are various uses cases that would predicate you needing to access data through these external volumes backed by external storage systems from an application running in Amazon Elastic Compute Cloud (Amazon EC2) instances on Outposts. These include: regulatory auditing requirements, government and local regulation compliance, high data durability and resiliency requirements, low-latency data access, and migration of on-premises applications that are tightly coupled with existing external storage systems. To make it easier for you to use external volumes with Outposts, AWS has validated a broad range of third-party storage solutions through the AWS Outposts Ready Program. With this program, you can easily identify storage solutions that are tested to run with Outposts.

Today, we are taking our integration with storage solutions from NetApp and Pure Storage to the next level. Outposts now has a simplified and automated way to launch EC2 instances with attached block storage from external infrastructure through the AWS Management Console. The new integration includes automated user script generation and attachment of data volumes to EC2 instances running on 42U Outposts racks and 2U Outposts servers. This integration reduces the friction associated with using the advanced data management and security features of external storage infrastructure in combination with Outposts, allowing you to create a resilient, compliant, and optimized storage and compute infrastructure.

Outposts rack storage and networking overview

Outposts racks support Amazon Elastic Block Store (Amazon EBS) volumes for EC2 instances, which provide persistent local block storage.

EC2 instances running on Outposts racks can access data stored on external block storage arrays over the Outposts local gateway (LGW). An LGW enables connectivity between the Outpost subnets, where EC2 instances run, and the on-premises network. It carries storage traffic between the EC2 instances running on the Outposts rack and the local network. The LGW is created by AWS as part of the Outposts rack installation process. Each Outposts rack supports a single LGW.

The following diagram shows an EC2 instance running on an Outposts rack with an elastic network interface (ENI) and LGW configured for instance connectivity. An external storage array communicates with the EC2 instance running on the Outposts rack through the Outpost network devices (ONDs). Customer Network Devices (CNDs) that connect to EC2 instances running on Outposts racks need to support the following:

  • Link aggregation: connections to the Outposts rack network devices are added to a link aggregation group (LAG).
  • VLANs: Virtual LANs (VLANs) are configured between each Outposts rack TOR device and any customer devices, including data stores.;
  • Dynamic routing: Border Gateway Protocol (BGP) is configured between the CND and the OND for each VLAN. Two total BGP sessions are shown in the following diagram between devices.

Figure 1. Outposts rack and Amazon EC2 networking architecture

Figure 1. Outposts rack and Amazon EC2 networking architecture

Outposts server storage and networking overview

Outposts servers come with internal NVMe SSD-based high-performance instance storage. Similar to AWS Regions, instance storage is allocated directly to the EC2 instance and follows the lifecycle of the instance. For example, if an EC2 instance is terminated, then the instance storage associated with the instance is also deleted. If you want data to persist after the instance is terminated, you can use external storage solutions to complement the instance storage included with Outposts servers.

Outposts servers have a local network interface (LNI). This logical networking component connects the EC2 instances running on the Outposts servers subnet to the on-premises network and allows communication to other on-premises storage, compute, and networking appliances.

To support the Amazon EC2 on Outposts to external storage array integration, an LNI must be created then added to the EC2 instance during instance launch. An LNI can only be created through the AWS Command Line Interface (AWS CLI) or the AWS software development toolkit (SDK) using the following command. The subnet id is the Outposts server subnet and the device index should be unique to the subnet.

aws ec2 modify-subnet-attribute --subnet-id <subnet id> --enable-lni-at-device-index <device index>

In the on-premises network, you must have a Network Interface Card (NIC) at the same device index that you specified when running the preceding CLI command.

Further detailed steps for this workflow are listed in the Outposts server user guide.

When the local network interfaces are enabled on an Outpost subnet, the EC2 instances in the Outpost subnet can be configured to include this LNI in addition to the ENI. The LNI connects to the on-premises network while the ENI connects to the VPC.

The following diagram shows an EC2 instance running on an Outposts server with both an ENI and LNI configured for instance connectivity. There is an external storage array connected to the Outposts server using a CND through NVMe-over-TCP or iSCSI protocol. Figure 2. Outposts server and Amazon EC2 networking architecture

Figure 2. Outposts server and Amazon EC2 networking architecture

Supported operating systems and AWS Support

The rest of this post covers the steps for how to launch an EC2 instance running on an Outposts 2U server or Outposts rack with a connected external block storage volume for local data access from within the EC2 instance. The current release of this feature supports EC2 instances running Microsoft Windows Server 2022 and Red Hat Enterprise Linux 9 (RHEL9) based operating systems.

Support for Outposts and all Outposts integration features, including this one, needs an active AWS Enterprise Support Plan or AWS Enterprise On-Ramp Support Plan. Support for external storage arrays and configurations can be obtained from the respective storage vendor and may need an additional support plan depending on the vendor and the storage solution implemented.

This post assumes you’re familiar with the basic functionality of Outposts servers and Outposts rack. If you would like to learn more about the Outposts family in general, then the user guide, What is AWS Outposts?, is a great place to start.

Solution deployment

The following sections outline the solution deployment.

Prerequisites:

  1. An Outposts 2U server or Outposts rack is provisioned, activated, and connected to the customer network.
  2. A block storage array is connected on the same network and accessible to Outposts subnets.
  3. A block data volume is configured and running on the storage array. The unique identifier for this volume is necessary for launching the EC2 instance on the Outpost. The volume must remain provisioned after initial provisioning on the storage array.
  4. The IP address and port number (optional for iSCSI connections) of the block storage volume, which is necessary for launching the EC2 instance on the Outpost.

Deployment architecture overview

The following deployment architecture shows the workflow attaching an external storage array to an Outpost, launching an EC2 instance through the AWS Management Console, and accessing the data on the external storage array from within the EC2 instance running on the Outpost.Figure 3. Third-party block storage on Outposts architecture overview

Figure 3. Third-party block storage on Outposts architecture overview

Deployment steps for NVMe-over-TCP connections

1. (Prerequisite) If there is no block data volume already running and configured on the compatible storage array, this must be completed in the storage solution’s interface before moving to Step 2.

a. Create an NVMe device, subsystem, and namespace for the block data volume.

b. Optionally, generate a host NQN that is used for the EC2 instance connection, and add it to the allow list for the appropriate subsystems.

c. The following pieces of information are used in later steps:

i. Host NQN: Unique identifier of the EC2 instance for attachment;

ii. Target IP: Address of the connected block volume host;

iii. Target Port Number: Port number of the connected block volume host.

You can learn more about launching and configuring external storage arrays in the Outposts family documentation or in the respective storage array vendor documentation.

2. In the Console, navigate to EC2 Launch Instance Wizard by choosing EC2, Instances, Launch instances.

a. Name the instance and add any desired tags to be applied at launch.

b. Choose the desired, compatible RHEL9 based Amazon Machine Image (AMI) from the list, or choose one from the AWS Marketplace.

c. Choose the desired EC2 Instance type.

d. Expand the Network settings section and select Edit. Choose the VPC and subnet of the target Outpost.

i. Outposts servers only: You must create an LNI in the Advanced Network settings before launching the instance.

e. Expand Advanced network configuration and select Add network device. Continue to add network devices until the Device index is equal to the volume index.

Figure 4. Advanced network configurationFigure 4. Advanced network configuration

f. Expand Configure storage and select Edit next to External storage volumes settings section and choose NVMe/TCP in Storage network protocol.

Figure 5. External storage volumes configuration

g. Enter the HostNQN in the format provided for the NVMe/TCP data volume. Make sure that the HostNQN used has been added to the storage array subsystem allow list.

h. Select Add NVMe/TCP Discovery Controller and enter the IP address and port of the controller from the storage array. Enter 4420 as the Target Port, if the target port is unknown.

i. (Optional) You can add more data volumes that use a different target discovery controller at this time by choosing the Add NVMe/TCP Data Volume button under the Target IP address. Repeat Steps 2.h for each data volume to be attached to the EC2 instance.

j. Expand the Advanced details and provide any additional Amazon EC2 behavior settings as appropriate.

k. At the bottom of the Advanced details section is the automatically generated User data. If you need to manually edit this data, you can do so by selecting Edit at the bottom.

Figure 6. Automatically generated user data file

l. When the configurations are set, choose the Launch instance button in the right-side column.

3. The EC2 Launch Instance Wizard now launches an EC2 instance configured as described on the Outpost and attaches the desired external data volume(s) to the EC2 instance.

4. Applications and users can access the data on the attached external volumes from within the EC2 instance. To verify this:

a. From within the launched EC2 instance, run sudo nvme list

b. The volumes are displayed as /dev/nvme1n1 with the number increasing for each attached volume. Local instance store volumes on Outposts servers and EBS boot volumes on Outposts racks are listed first. External volumes are listed after those with sequentially increasing node numbers.

5. External storage volume and array management, configuration, and backups continue to be managed through the storage vendor-provided toolkit. You can find more information on external storage management in the respective storage array vendor documentation.

Deployment steps for iSCSI connections

1. (Prerequisite) If there is no block data volume already running and configured on the compatible storage array, this must be completed in the storage solution’s interface before moving to Step 2.

a. Create an Initiator group (igroup) and add the Initiator IQN to the igroup. Then map the logical unit number (LUN) to the igroup.

b. Optionally, generate an initiator IQN that is used for the EC2 instance connection, and add it to the allow list for the appropriate subsystems.

c. The following pieces of information are used in later steps:

i. Initiator IQN: Unique identifier of the EC2 instance for attachment;

ii. Target IQNs: Unique identifier of the storage virtual machine (SVM);

iii. Target IP: Address of the connected block volume host;

iv. (Optional) Target Port Number: Port number of the connected block volume host.

You can learn more about launching and configuring external storage arrays in the Outposts family documentation or in the respective storage array vendor documentation.

2. In the Console, navigate to EC2 Launch Instance Wizard by choosing EC2, Instances, Launch instances.

a. Name the instance and add any desired tags to be applied at launch.

b. Choose the desired, compatible RHEL9 or Windows Server 2022 based AMI from the list, or purchase one from the AWS Marketplace.

c. Choose the desired EC2 Instance type.

d. Expand the Network settings section and choose the VPC and subnet of the target Outpost.

i. Outposts servers only: You must create an LNI in the Advanced Network settings before launching the instance.

e. Expand Advanced network configuration and select Add network device. Continue to add network devices until the Device index is equal to the volume index.

Figure 7. Advanced network configurationFigure 7. Advanced network configuration

f. Expand Configure storage and select Edit next to External storage volumes settings section and choose iSCSI in Storage network protocol.

Figure 8. External storage volumes configurationFigure 8. External storage volumes configuration

g. Enter the Initiator IQN for the iSCSI data volume in the format provided. Make sure that the Initiator IQN used has been added to the allow list for the volume.

h. Select Add iSCSI Target and enter the Target IP, Target Port, and Target IQN of the storage array. Enter 4420 for the Target Port, if the target port is unknown.

i. (Optional) You can add additional data volumes with a different Target IQN at this time by selecting the Add iSCSI Target button under the Target IP address. Repeat Steps 2.h for each data volume to be attached to the EC2 instance.

j. Expand the Advanced details and provide any additional Amazon EC2 behavior settings as appropriate.

k. At the bottom of the Advanced details section is the automatically generated User data. If you need to manually edit this data, you can do so by selecting Edit at the bottom.

Figure 9. Automatically generated user data fileFigure 9. Automatically generated user data file

l. When the configurations are set, choose the Launch instance button in the right-side column.

3. The EC2 Launch Instance Wizard now launches an EC2 instance configured as described on the Outpost and attaches the desired external data volume(s) to the EC2 instance.

4. Applications and users can access the data on the attached external volumes from within the EC2 instance. To verify this:

a. From within the launched EC2 instance, run iscsiadm -m session -P3

b. The volumes are displayed as /dev/sd0 with the number increasing for each attached volume.

5. External storage volume and array management, configuration, and backups continue to be managed through the storage vendor-provided toolkit. You can find more information on external storage management in the respective storage array vendor documentation.

Conclusion

This integration offers a streamlined workflow to attach and utilize external block data volumes on Outposts directly through the AWS Management Console, eliminating manual processes. It provides the full benefits of advanced data infrastructure from trusted storage providers in conjunction with the security, reliability, and scalability of AWS managed infrastructure. This helps you accelerate cloud migration with dependencies on third-party storage and realize the full potential of your on-premises data.

To learn more about this integration, visit the NetApp on-premises enterprise storage arrays for AWS Outposts solution page and the Pure Storage FlashArray for AWS Outposts blog post. To discuss your external storage needs with an Outposts expert, submit this form. If you are attending AWS re:Invent 2024, make sure to check out the NetApp booth (booth #1748) and Pure Storage booth (booth #454) to connect with our partner specialists.

Hosting containers at the edge using Amazon ECS and AWS Outposts server

Post Syndicated from aostan original https://aws.amazon.com/blogs/compute/hosting-containers-at-the-edge-using-amazon-ecs-and-aws-outposts-server/

This post is written by Craig Warburton, Hybrid Cloud Senior Solutions Architect and Sedji Gaouaou, Hybrid Cloud Senior Solutions Architect

In today’s fast-paced digital landscape, businesses are increasingly looking to process data and run applications closer to the source, at the edge of the network. For those seeking to use the power of containerized workloads in edge environments, AWS Outposts servers offer a compelling solution. This fully managed service brings the AWS infrastructure, services, APIs, and tools to virtually any on-premises or edge location, allowing users to run container-based applications seamlessly across their distributed environments. In this post, we explore how Outposts servers can empower organizations to deploy and manage containerized workloads at the edge, bringing cloud-native capabilities closer to where they’re needed most.

Solution overview

Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service that can be used with Outposts servers. This combination allows users to run containerized applications at the edge with the same ease and flexibility as in the AWS cloud.

By using Outposts server with Amazon ECS, users can effectively extend their container-based workloads to the edge, enabling new use cases and improving application performance for latency-sensitive operations.

The following diagram illustrates an example architecture where a user is looking to deploy a microservices based PHP web application and instance based MySQL database. Furthermore, a container based load balancer appliance is used to receive and distribute traffic to the web application container. The example application writes its data to a MySQL database, which is hosted on an external storage array. The application is deployed on the Outpost server, and can communicate with the database across the user data center network.

In this post we will show how users can deploy an example microservice based application. Each section of this post walks through Steps 1 through 4 shown in the following diagram.

Figure 1: Solution overview

Figure 1: Solution overview

Walkthrough

Prerequisites

Before deploying the sample application, you must have ordered, received, and successfully installed an Outposts server. The server is operational and visible in the AWS Management Console.

This walkthrough assumes you have access to Amazon Elastic Container Registry (Amazon ECR) that is used for the container repository.

You need the following AWS Identity and Access Management (IAM) role provisioned with the necessary permissions included in the policy to permit the load balancer to read the required Amazon ECS attributes. Refer to the user guide Create a role to delegate permissions to an IAM user section to help you through creating an IAM role and associated policy. The Amazon ECS task IAM role needs the following policy configuration to read the necessary Amazon ECS information:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "LoadBalancerECSReadAccess",
            "Effect": "Allow",
            "Action": [
                "ecs:ListClusters",
                "ecs:DescribeClusters",
                "ecs:ListTasks",
                "ecs:DescribeTasks",
                "ecs:DescribeContainerInstances",
                "ecs:DescribeTaskDefinition",
                "ec2:DescribeInstances",
                "ssm:DescribeInstanceInformation"
            ],
            "Resource": [
                "*"
            ]
        }
    ]
}

You also need the Amazon ECS task execution IAM role (ecsTaskExecutionRole) that will grant the Amazon ECS container service the necessary permissions to make AWS API calls on your behalf.

Step 1: Setting up Amazon ECS on Outposts server

Amazon ECS is used in this walkthrough to deploy our container workloads to the Outposts server. Before deploying workloads, an ECS cluster on Outposts needs to be created.

In this configuration, the Amazon ECS cluster targets the private subnets (10.0.1.0/24 and 10.0.2.0/24) and the Amazon Elastic Compute Cloud Amazon (EC2) instances configured on the Outpost server for deployments.

To assist in targeting the deployment of our Amazon ECS services to specific instances with an attached Local Network Interface (LNI), our Amazon EC2 instances are assigned a logical role using custom Amazon ECS container instance attributes. Custom attributes are used to configure task placement constraints, as shown in the following figure.

Figure 2: Amazon ECS container instances used for tasks

Figure 2: Amazon ECS container instances used for tasks

One of the container instances is assigned the role of loadbalancer, as shown in the following figure. Follow the developer guide section to Define which container instances Amazon ECS uses for tasks, and add the following custom attribute to one of your instances:

  • Name = role, Value = loadbalancer

Figure 3: Instance with the Custom Attibutes - loadbalancer

Figure 3: Instance with the Custom Attibutes – loadbalancer

The other container instance is assigned the role of webserver, as shown in the following figure. Add the following custom attribute to each of the remaining instance:

  • Name = role, Value = webserver

Figure 4: Instance with the Custom Attibutes - webserver

Figure 4: Instance with the Custom Attributes – webserver

Step 2: Deploying a load balancer with host mode to use LNI

In this section, you deploy a task for the load balancer as seen in Step 2 of the Solution overview.

First, you must enable the private subnet, where your load balancer is deployed, for LNIs:

aws ec2 modify-subnet-attribute \

    --subnet-id subnet-1a2b3c4d \

    --enable-lni-at-device-index 1

Now add an LNI to the container instance with the attibute “loadbalancer”. This instance can now access your local network.

To deploy the load balancer, create an Amazon ECS task definition named “task-definition-loadbalancer.json”, which describes the container configuration to implement the load balancer as followed:

{
    "containerDefinitions": [
        {
            "name": "loadbalancer",
            "image": "traefik:latest",
            "cpu": 0,
            "portMappings": [
                {
                    "containerPort": 80,
                    "hostPort": 80,
                    "protocol": "tcp"
                },
                {
                    "containerPort": 8080,
                    "hostPort": 8080,
                    "protocol": "tcp"
                }
            ],
            "essential": true,
            "command": [
                "--api.dashboard=true",
                "--api.insecure=true",
                "--accesslog=true",
                "--providers.ecs.ecsAnywhere=false",
                "--providers.ecs.region=<AWS_REGION>",
                "--providers.ecs.autoDiscoverClusters=true",
                "--providers.ecs.clusters=<YOUR_CLUSTER_NAME>",
                "--providers.ecs.exposedByDefault=true"
            ],
            "environment": [],
            "mountPoints": [],
            "volumesFrom": [],
            "systemControls": []
        }
    ],
    "family": "loadbalancer",
    "taskRoleArn": <TASK_ROLE_ARN>,
    "executionRoleArn": <EXECUTION_ROLE_ARN>,
    "networkMode": "host",
    "volumes": [],
    "placementConstraints": [
        {
            "type": "memberOf",
            "expression": "attribute:role == loadbalancer"
        }
    ],
    "requiresCompatibilities": [
        "EC2"
    ],
    "cpu": "256",
    "memory": "128",
    "tags": []
}

Replace the string <TASK_ROLE_ARN> with the Amazon Resource Name (ARN) of the IAM role configured with the LoadBalancerECSReadAccess policy and the string <EXECUTION_ROLE_ARN> with the ARN of the IAM role configured with the ecsTaskExecutionRole policy as configured in the Prerequisites section, <AWS_REGION> with the AWS Region where you deployed your ECS cluster, <YOUR_CLUSTER_NAME> with your cluster name.

Some points to consider:

  • The Amazon ECS Network mode is set to “host”. The load balancer task uses the host’s network to access the LNI.
  • The task definition includes the placement constraint matching the loadbalancer custom attribute value.

Lastly, register the task definition with your cluster and create the loadbalancer service using the following AWS Command Line Interface (AWS CLI) command:

aws ecs register-task-definition --cli-input-json file://task-definition-loadbalancer.json

aws ecs create-service--cluster <CLUSTER_NAME> --service-name loadbalancer --task-definition loadbalancer:1 --desired-count 1

Replace the string <CLUSTER_NAME> with the target Amazon ECS cluster name.

The load balancer is now running.

Connecting to the Amazon EC2 instance with the attibute loadbalancer using Session Manager, you can get the following LNI IP address:

Figure 5: Getting the LNI IP

Figure 5: Getting the LNI IP

You can access the web user interface by browsing to the URL from your local network:

http://<HOST_IP>:8080/dashboard/

Replace the string <HOST_IP> with the Amazon EC2 instance host LNI IP address, or DNS hostname.

Step 3: Deploying sample web application in awsvpc mode

First, make sure that the AWSVPC Trunking is turned on, as shown in the following figure:

Figure 6: Enabling AWSVPC Trunking

Figure 6: Enabling AWSVPC Trunking

Create an Amazon ECS task definition for our application named “task-definition-webapp.json”, which describes the container configuration to implement the example web application as followed:

Replace the <PLACEHOLDER> values for your application.

{
    "containerDefinitions": [
        {
            "name": "whoami",
            "image": "<CONTAINER-IMAGE>:latest",
            "cpu": 0,
            "portMappings": [
                {
                    "name": "<WEBAPP>",
                    "containerPort": 80,
                    "hostPort": 80,
                    "protocol": "tcp"
                }
            ],
            "essential": true,
            "environment": [],
            "mountPoints": [],
            "volumesFrom": [],
            "dockerLabels": {
"traefik.http.routers.<WEBAPP>-host.rule":     "Host(`<WEBAPP>.domain.com`)",
               "traefik.http.routers.<WEBAPP>-path.rule": "Path(`/<WEBAPP>`)",
               "traefik.http.services.<WEBAPP>.loadbalancer.server.port": "80"
            },
            "systemControls": []
        }
    ],
    "family": "<WEBAPP>",
    "networkMode": "awsvpc",
    "volumes": [],
    "placementConstraints": [
        {
            "type": "memberOf",
            "expression": "attribute:role == webserver"
        }
    ],
    "requiresCompatibilities": [
        "EC2"
    ],
    "cpu": "256",
    "memory": "128",
    "tags": []
}

In the task-definition-webapp.json, consider the following:

  • The task definition includes the placement constraint matching the webserver custom attribute value.
  • Docker label traefik.http.routers is used to configure host and path based routing rules.
  • As the example web application container exposes the single TCP port 80, Docker label traefik.http.services.<WEBAPP> is used to configure this port for private communication with the Traefik load balancer.

Register the task definition with your cluster and create the loadbalancer service using the following AWS CLI command:

aws ecs register-task-definition --cli-input-json file://task-definition-webapp.json

aws ecs create-service--cluster <CLUSTER_NAME> --service-name <WEBAPP> --task-definition <WEBAPP>:1 --desired-count 1

Replace the string <CLUSTER_NAME> with the target Amazon ECS cluster name and the string <WEBAPP> with your application.

You can access the whoami application by browsing to the URL from your local network:

http://<HOST_IP>/<WEBAPP>

Step 4: Provision DB instance and attach an external storage

The web application has been successfully deployed, so we will move on to the deployment and configuration of the database server next. First, deploy an Amazon EC2 instance to host a MySQL database. As shown in the following screenshot, use the AWS Console to choose an instance type (this is dependent on your Outposts server instance capacity configuration) and configure its network settings to target the correct VPC and the subnet deployed to the Outposts server.

Figure 7: Provisioning a database instance

Figure 7: Provisioning a database instance

When the instance is available, deploy MySQL following a standard documented approach to install on a Linux host from the vendor. After successfully installing MySQL, configure users and tables necessary for the application. The sample application configuration file can now be updated to allow the PHP web server container to connect to the MySQL database, as well as create a user and list the users, as shown in the following figures.

Figure 8: Updating the application config file to use the database instance

Figure 8: Updating the application config file to use the database instance

Figure 9: Sample application connected to database

Figure 9: Sample application connected to database

For the database instance, make sure that the data associated with the application is stored on an existing storage array in the user data center. To do this, you must complete the following:

(a) Enable connectivity to the user network through the LNI.

(b) Mount the iSCSI volume in the EC2 instance.

(c) Configure MySQL to use this iSCSI volume.

To enable connectivity, follow the same process described in step 2 of this post to add an Elastic Network Interface (ENI) with the correct device index to present the LNI to the instance. The following screenshots show a second ENI configured on the instance and associated with the LNI along with the interface and address configuration of the instance that shows two addresses (VPC and user network addresses).

Figure 10: Network interface configuration

Figure 10: Network interface configuration

Now that connectivity has been established to the user network, you can configure the storage array to present an ISCSI volume to the database instance and mount that volume. The following screenshot shows the /mnt mountpoint being used with iSCSI multi-path across four volumes.

Figure 11: iSCSI volume mount

Figure 11: iSCSI volume mount

Finally, configure MySQL to use the iSCSI volume to store data by stopping the MySQL service, updating the default configuration file /etc/my.cnf, and restarting MySQL, as shown in the following figure.

Figure 12: MySQL configuration

Figure 12: MySQL configuration

Clean up:

Please follow the below instructions to clean up after testing:

  • Delete the <WEBAPP> service
  • Delete the loadbalancer service
  • Delete your Amazon ECS cluster
  • Delete the MySQL Database EC2 instance
  • Delete all VPCs

Conclusion

This post has demonstrated how to deploy a sample container-based web application while connecting to the user network, allowing access to the application and connecting to existing storage appliances.

AWS Outposts server allows users to run containers at the edge, addressing challenges related to low latency, local data processing, and data residency. Amazon ECS allows you to deploy consistently, whether in-Region or at the edge, allowing users to develop once and deploy many times.

Get started with Outposts servers by visiting the Outposts servers webpage and learn more about Amazon ECS to begin deploying your containarized workloads at the edge!