Tag Archives: launch

AWS Application Migration Service Major Updates – New Migration Servers Grouping, Updated Launch, and Post-Launch Template

2022-11-28 Channy Yun

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/aws-application-migration-service-major-updates-new-migration-servers-grouping-updated-launch-and-post-launch-template/

Last year, we introduced the general availability of AWS Application Migration Service that simplifies and expedites your migration to AWS by automatically converting your source servers from physical, virtual, or cloud infrastructure to run natively on AWS. Since the GA launch, we have made improvements, adding features such as agentless replication, MAP 2.0 auto-tagging and support for optional post-launch modernization actions.

Today we announce three major updates of Application Migration Service to support your migration projects of any size:

New Migration Servers Grouping – You can group migration servers into “applications,” a group of servers that function together as a single application, and manage the migration stage in “waves,” a plan of migrations including grouping servers and applications.
Updated Launch Template – You can modify the general settings and default launch template, and this template is then used to generate the Amazon Elastic Compute Cloud (Amazon EC2) instance launch template of subsequently installed source servers.
Updated Post-Launch Template – You can configure custom modernization actions for the post-launch template. You can associate any AWS Systems Manager documents and their parameters with a post-launch custom action.

Let’s dive deep into each launch!

New Migration Servers Grouping – Applications and Waves
Customers have clusters of servers that comprise an application, with dependencies between them. The servers within an application share the same configurations, such as network, security policies, etc. Customers want to migrate complete applications and services, as well as set up and configure the application environment.

We introduce the new concept of “application,” representing a group of servers, and you can manage the migration of an application.

The new application feature groups source servers together with the same application for integrated migration jobs. It includes configuring the environment before migrating the application’s servers, creating the appropriate security groups, and performing bulk actions on all of the applications servers.

You can track and monitor the status of application migration and data replication within the migration lifecycle from source servers.

Also, customers with large migrations plan their migration, grouping servers and applications in waves. These are logical groups that describe the migration plan over time. Waves may include multiple servers and applications that do not necessarily have dependencies between them.

We introduce the new concept of “wave,” assisting customers in building their migration plan, as well as executing and monitoring it.

Application Migration Service supports actions on waves, such as launching all servers in a testing environment or performing cutover of a wave. Application Migration Service also provides reporting and monitoring information at the wave level so that customers will be able to manage their migration projects.

Updated Launch Template – Launch Settings and Default EC2 Launch Template
The launch template allows you to control the way Application Migration Service launches instances in the AWS Cloud. You can change the settings for existing and newly added servers individually. Previously, we only supported the AWS Migration Acceleration Program (MAP) option to add tags to launched migration instances.

We added two new options to modify the global launch template, and this template is then used to generate the EC2 launch templates of subsequently installed source servers. Customers start with a global Application Migration Service launch template, which can be used for predefined launch templates. They would then potentially only have to perform modifications to a smaller subset of source servers, as opposed to all of them.

Here are default settings that will be used when launching target servers:

Activate instance type right-sizing – The service will determine the best match instance type. The default instance type defined in the EC2 template will be ignored.
Start instance upon launch – The service will launch instances automatically. If this option is not selected, the launched instance will need to be manually started after launch.
Copy private IP – This enables you to copy the private IP of your source server to the target.
Transfer server tags – Transfer the tags from the source server to the launched instances.
Operating system licensing – Specify whether to continue to use the Bring Your Own License model (BYOL) of the source server or use an AWS provided license.

Also, you can configure the default settings that will be applied to the EC2 launch template of every target server, such as default target subnet, additional security groups, default instance type, Amazon Elastic Block Store (Amazon EBS) volume type, IOPS, and throughput to associate with all instances launched by this service.

Updated Post-Launch Template – Custom Actions
Post-launch settings allow you to control and automate actions performed after the server has been launched in AWS. It includes four built-in actions: installing the AWS Systems Manager agent, installing the AWS Elastic Disaster Recover agent and configuring replication, CentOS conversion, and SUSE subscription conversion.

We added a new option to configure custom actions in the post-launch template. You can associate any AWS Systems Manager and its action parameters. It also includes the order in which the actions will be executed and the source server’s operating systems for which the custom action can be configured.

Choose Add custom action to make a new post-launch custom action. For example, the AWS-CopySnapshot, one of Systems Manager Automation’s runbooks, copies a point-in-time snapshot of an EBS volume. You can copy the snapshot within the same AWS Region or from one Region to another.

In the Action parameters, you can assign SnapshotId and SourceRegion to run the AWS Systems Manager CopySnapshot runbook.

You can create your own Systems Manager document to define the actions that Systems Manager performs on your managed instances. Systems Manager offers more than 100 preconfigured documents that you can use by specifying parameters as the post-launch actions. To learn more, see AWS Systems Manager Automation runbook reference in the AWS documentation.

Now Available
The new migration servers grouping, updates on the launch, and post-launch template are available now, and you can start using them today in all Regions where AWS Application Migration Service is supported.

To learn more, see the Application Migration Service User Guide, give it a try, and please send feedback to AWS re:Post for Application Migration Service or through your usual AWS support contacts.

– Channy

New – Announcing Amazon EFS Elastic Throughput

2022-11-28 Veliswa Boya

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/new-announcing-amazon-efs-elastic-throughput/

Today, we are announcing the availability of Amazon EFS Elastic Throughput, a new throughput mode for Amazon EFS that is designed to provide your applications with as much throughput as they need with pay-as-you-use pricing. This new throughput mode enables you to further simplify running workloads and applications on AWS by providing shared file storage that doesn’t need provisioning or capacity management.

Elastic Throughput is ideal for spiky and unpredictable workloads with performance requirements that are difficult to forecast. When you enable Elastic Throughput on an Amazon EFS file system, you no longer need to think about actively managing your file system performance or over-paying for idle resources in order to ensure performance for your applications. When you enable Elastic Throughput, you don’t specify or provision throughput capacity, Amazon EFS automatically delivers the throughput performance your application needs while you the builder pays only for the amount of data read or written.

Amazon EFS is built to provide serverless, fully elastic file storage that lets you share file data for your cloud-based applications without having to think about provisioning or managing storage capacity and performance. With Elastic Throughput, Amazon EFS now extends its simplicity and elasticity to performance, enabling you to run an even broader range of file workloads on Amazon EFS. Amazon EFS is well suited to support a broad spectrum of use cases that include analytics and data science, machine learning, CI/CD tools, content management and web serving, and SaaS applications.

A Quick Review
As you may already know, Amazon EFS already has the Bursting Throughput mode, which is available as a default and supports bursting to higher levels for up to 12 hours a day. If your application is throughput constrained on Bursting mode (for example, utilizes more than 80 percent of permitted throughput or exhausts burst credits), then you should consider using Provisioned (which we announced in 2018), or the new Elastic Throughput modes.

With this announcement of Elastic Throughput mode, and in addition to the already existing Provisioned Throughput mode, Amazon EFS now offers two options for workloads that require higher levels of throughput performance. You should use Provisioned Throughput if you know your workload’s performance requirements and you expect your workload to consume a higher share (more than 5 percent on average) of your application’s peak throughput capacity. You should use Elastic Throughput if you don’t know your application’s throughput or your application is very spiky.

To access Elastic Throughput mode (or any of the Throughput modes), select Customize (selecting Create instead will create your file system with the default Bursting mode).

Create File system

New – Elastic Throughput

You can also enable Elastic Throughput for new and existing General Purpose file systems using the Amazon EFS console or programmatically using the Amazon EFS CLI, Amazon EFS API, or AWS CloudFormation.

Elastic Throughput in Action
Once you have enabled Elastic Throughput mode, you will be able to monitor your cost and throughput usage using Amazon CloudWatch and set alerts on unplanned throughput charges using AWS Budgets.

I have a test file system elasticblog that I created previously using the Amazon EFS console, and now I cannot wait to see Elastic Throughput in action.

File system (elasticblog)

I have provisioned an Amazon Elastic Compute Cloud (Amazon C2) instance which I mounted to my file system. This EC2 instance has data that I will add to the file system.

I have also created CloudWatch Alarms, which will monitor throughput usage and set alarm thresholds (ReadIOBytes, WriteIOBytes, TotalIOBytes, and MetadataIOBytes).

CloudWatch for Throughput Usage

The CloudWatch dashboard for my test file system elasticblog looks like this.

CloudWatch Dashboard – TotalIOBytes for File System

Elastic Throughput allows you to drive throughput up to a limit of 3 GiB/s for read operations and 1 GiB/s for write operations per file system in all Regions.

Available Now
Amazon EFS Elastic Throughput is available in all Regions supporting EFS except for the AWS China Regions.

To learn more, see the Amazon EFS User Guide. Please send feedback to AWS re:Post for Amazon Elastic File System or through your usual AWS support contacts.

– Veliswa x

New – Fully Managed Blue/Green Deployments in Amazon Aurora and Amazon RDS

2022-11-28 Channy Yun

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/new-fully-managed-blue-green-deployments-in-amazon-aurora-and-amazon-rds/

When updating databases, using a blue/green deployment technique is an appealing option for users to minimize risk and downtime. This method of making database updates requires two database environments—your current production environment, or blue environment, and a staging environment, or green environment. You must then keep these two environments in sync with each other so you may safely test and upgrade your changes to production.

Amazon Aurora and Amazon Relational Database Service (Amazon RDS) customers can use database cloning and promotable read replicas to help self-manage a blue/green deployment. However, self-managing a blue/green deployment can be costly and complex to build and manage. As a result, customers sometimes delay implementing database updates, choosing availability over the benefits that they would gain from updating their databases.

Today, we are announcing the general availability of Amazon RDS Blue/Green Deployments, a new feature for Amazon Aurora with MySQL compatibility, Amazon RDS for MySQL, and Amazon RDS for MariaDB that enables you to make database updates safer, simpler, and faster.

With just a few steps, you can use Blue/Green Deployments to create a separate, synchronized, fully managed staging environment that mirrors the production environment. The staging environment clones your production environment’s primary database and in-Region read replicas. Blue/Green Deployments keep these two environments in sync using logical replication.

In as fast as a minute, you can promote the staging environment to be the new production environment with no data loss. During switchover, Blue/Green Deployments blocks writes on blue and green environments so that the green catches up with the blue, ensuring no data loss. Then, Blue/Green Deployments redirects production traffic to the newly promoted staging environment, all without any code changes to your application.

With Blue/Green Deployments, you can make changes, such as major and minor version upgrades, schema modifications, and operating system or maintenance updates, to the staging environment without impacting the production workload.

Getting Started with Blue/Green Deployments for MySQL Clusters
You can start updating your databases with just a few clicks in the AWS Management Console. To get started, simply select the database that needs to be updated in the console and click Create Blue/Green Deployment under the Actions dropdown menu.

You can set a Blue/Green Deployment identifier and the attributes of your database to be modified, such as the engine version, DB cluster parameter group, and DB parameter group for green databases. To use a Blue/Green Deployment in your Aurora MySQL DB cluster, you should turn on binary logging, changing the value for the binlog_format parameter from OFF to MIXED in the DB cluster parameter group.

When you choose Create Blue/Green Deployment, it creates a new staging environment and runs automated tasks to prepare the database for production. Note, you will be charged the cost of the green database, including read replicas and DB instances in Multi-AZ deployments, and any other features such as Amazon RDS Performance Insights that you may have enabled on green.

You can also do the same job in the AWS Command Line Interface (AWS CLI). To perform an engine version upgrade, simply add a targetEngineVersion parameter and specify the engine version you’d like to upgrade to. This parameter works with both minor and major version upgrades, and it accepts short versions like 5.7 for Amazon Aurora MySQL-Compatible.

$ aws rds create-blue-green-deployment \
--blue-green-deployment-name my-bg-deployment \
--source arn:aws:rds:us-west-2:1234567890:db:my-aurora-mysql \
--target-engine-version 5.7 \
--region us-west-2 \

After creation is complete, you now have a staging environment that is ready for test and validation before promoting it to be the new production environment.

When testing and qualification of changes are complete, you can choose Switch over in the Actions dropdown menu to promote the staging environment marked as Green to be the new production system.

Now you are nearly ready to switch over your green databases to production. Check the settings of your green databases to verify that they are ready for the switchover. You may also set a timeout setting to determine the maximum time limit for your switchover. If Blue/Green Deployments’ switchover guardrails detect that it would take longer than the specified duration, then the switchover is canceled, and no changes are made to the environments. We recommend that you identify times of low or moderate production traffic to initiate a switchover.

After switchover, Blue/Green Deployments does not delete your old production environment. You may access it for additional validations and performance/regression testing, if needed. Please note that it is your responsibility to delete the old production environment when you no longer need it. Standard billing charges apply on old production instances until you delete them.

Now Available
Amazon RDS Blue/Green Deployments is available today on Amazon Aurora with MySQL Compatibility 5.6 or higher, Amazon RDS for MySQL major version 5.6 or higher, and Amazon RDS for MariaDB 10.2 and higher in all AWS commercial Regions, excluding China, and AWS GovCloud Regions.

To learn more, read the Amazon Aurora MySQL Developer Guide or the Amazon RDS for MySQL User Guide. Give it a try, and please send feedback to AWS re:Post for Amazon RDS or through your usual AWS support contacts.

– Channy

New for AWS Backup – Protect and Restore Your CloudFormation Stacks

2022-11-28 Danilo Poccia

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-aws-backup-protect-and-restore-your-cloudformation-stacks/

To define the data protection policy of an application, you have to look at its components and find which ones store data that needs to be protected. Those are the stateful components of your application, such as databases and file systems. Other components don’t store data but need to be restored as well in case of issues. These are stateless components, such as containers and their network configurations.

When you manage your application using infrastructure as code (IaC), you have a single repository where all these components are described. Can we use this information to help protect your applications? Yes! AWS Backup now supports attaching an AWS CloudFormation stack to your data protection policies.

When you use CloudFormation as a resource, all stateful components supported by AWS Backup are backed up around the same time. The backup also includes the stateless resources in the stack, such as AWS Identity and Access Management (IAM) roles and Amazon Virtual Private Cloud (Amazon VPC) security groups. This gives you a single recovery point that you can use to recover the application stack or the individual resources you need. In case of recovery, you don’t need to mix automated tools with custom scripts and manual activities to recover and put the whole application stack back together. As you modernize and update an application managed with CloudFormation, AWS Backup automatically keeps track of changes and updates the data protection policies for you.

CloudFormation support for AWS Backup also helps you prove compliance of your data protection policies. You can monitor your application resources in AWS Backup Audit Manager, a feature of AWS Backup that enables you to audit and report on the compliance of data protection policies. You can also use AWS Backup Vault Lock to manage the immutability of your backups as required by your compliance obligations.

Let’s see how this works in practice.

Using AWS Backup Support for CloudFormation Stacks
First, I need to turn on the CloudFormation resource type for AWS Backup. In the AWS Backup console, I choose Settings in the navigation pane and then, in the Service opt-in section, Configure resources. There, I toggle the CloudFormation resource type on and choose Confirm.

Now that CloudFormation support is enabled, I choose Dashboard in the navigation pane and then Create backup plan. I select the Start with a template option and then the Daily-35day-Retention template. As the name suggests, this template creates daily backups that are kept for 35 days before being automatically deleted. I enter a name for the backup plan and choose Create plan.

Now I can assign resources to my backup plan. I enter a resource assignment name and use the default IAM role that is automatically created with the correct permissions.

In the Resource selection, I can select Include all resource types to automatically protect all resource types that are enabled in my account. Because I’d like to show how CloudFormation support works, I select Include specific resource types and then CloudFormation in the Select resource types dropdown menu. In the Choose resources menu, I can use the All supported CloudFormation stacks option to have all my stacks protected. For simplicity, I choose to protect only one stack, the my-app stack.

I leave the other options at their default values and choose Assign resources. That’s all! Now the CloudFormation stack that I selected will be backed up daily with 35 days of retention. What does that mean? Let’s have a look at what happens when I create an on-demand backup of a CloudFormation stack.

Creating On-Demand Backups for CloudFormation Stacks
I choose Protected resources in the navigation pane and then Create on-demand backup. The next steps are similar to what I did before when assigning resources to a backup plan. I select the CloudFormation resource type and the my-app stack. I use the Create backup now option to start the backup within one hour. I choose 7 days of retention and the Default backup vault. Backup vaults are logical containers that store and organize your backups. I select the default IAM role and choose Create on-demand backup.

Within a few minutes, the backup job is running. I expand the Backup job ID in the Backup jobs list to see the resources being backed up. The stateful resources (such as Amazon DynamoDB tables and Amazon Relational Database Service (RDS) databases) are listed with the current state of the backup job. The stateless resources in my stack (such as IAM roles, AWS Lambda functions, and VPC configurations) are backed up by the job with the CloudFormation resource type.

When the backup job has completed, I go back to the Protected resources page to see the list of resources that I can now restore. In the list, I see the IDs of the stateful resources (in this case, two DynamoDB tables and an Aurora database) and of the CloudFormation stack. If I choose each of the stateful resources, I see the available recovery points corresponding to the different points in time when that resource has been backed up.

If I choose the CloudFormation stack, I get a list of composite recovery points. Each composite recovery point includes all stateless and stateful resources in the stack. More specifically, the stateless resources are included in the CloudFormation template recovery point (the last one in the following screenshot).

Restoring a CloudFormation Backup
Inside the composite recovery point, I select the recovery point of the CloudFormation stack and choose Restore. Restoring a CloudFormation stack backup creates a new stack with a change set that represents the backup. I enter the new stack and change set names and choose Restore backup. After a few minutes, the restore job is completed.

In the CloudFormation console, the new stack is under review. I need to apply the change set.

I choose the new stack and select the change set created by the restore job to apply the change set.

After some time, the resources in my original stack have been recreated in the new stack. The stateful resources have been recreated empty. To recover the stateful resources, I can go back to the list of recovery points, select the recovery point I need, and initiate a restore.

Availability and Pricing
AWS Backup support for CloudFormation stacks is available today using the console, AWS Command Line Interface (CLI), and AWS SDKs in all AWS Regions where AWS Backup is offered. There is no additional cost for the stateless resources backed up and restored by AWS Backup. You only pay for the stateful resources such as databases, storage volumes, or file systems. For more information, see AWS Backup pricing.

You now have an automated solution to create and restore your applications with a simplified experience, eliminating the need to manage custom scripts.

— Danilo

Amazon CloudWatch Internet Monitor Preview – End-to-End Visibility into Internet Performance for your Applications

2022-11-28 Sébastien Stormacq

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/cloudwatch-internet-monitor-end-to-end-visibility-into-internet-performance-for-your-applications/

How many times have you had monitoring dashboards show you a normal situation, and at the same time, you have received customer tickets reporting your app is “slow” or unavailable to them? How much time did it take to diagnose these customer reports?

You told us one of your challenges when monitoring internet-facing applications is to gather data outside of AWS to build a realistic picture of how your application behaves for your customers connected to multiple and geographically distant internet providers. Capturing and monitoring data about internet traffic before it reaches your infrastructure is either difficult or very expensive.

I am happy to announce the public preview of Amazon CloudWatch Internet Monitor, a new capability of CloudWatch that gives visibility into how an internet issue might impact the performance and availability of your applications. It allows you to reduce the time it takes to diagnose internet issues from days to minutes.

Internet Monitor uses the connectivity data that we capture from our global networking footprint to calculate a baseline of performance and availability for internet traffic. This is the same data that we use at AWS to monitor our own internet uptime and availability. With Internet Monitor, you can gain awareness of problems that arise on the internet experienced by your end users in different geographic locations and networks.

There is no need to instrument your application code. You can enable the service in the CloudWatch section of the AWS Management Console and start to use it immediately.

Let’s See It in Action
Getting started with Internet Monitor is easy. Let’s imagine I want to monitor the network paths between my customers and my AWS resources. I open the AWS Management Console and navigate to CloudWatch. I select Internet Monitor on the left-side navigation menu. Then, I select Create monitor.

On the Create monitor page, I enter a Monitor name, and I select Add resources to choose the resources to monitor. For this demo, I select the VPC and the CloudFront distribution hosting my customer-facing application.

I have the opportunity to review my choices. Then, I select Create monitor.

From that moment on, Internet Monitor starts to collect data based on my application’s resource logs behind the scene. There is no need for you to activate (or pay for) VPC Flow Logs, CloudFront logs, or other log types.

After a while, I receive customer complaints about our application being slow. I open Internet Monitor again, I select the monitor I created earlier (Monitor_example), and I immediately see that the application suffers from internet performance issues.

The Health scores graph shows you performance and availability information for your global traffic. AWS has substantial historical data about internet performance and availability for network traffic between geographic locations for different network providers and services. By applying statistical analysis to the data, we can detect when the performance and availability towards your application have dropped, compared to an estimated baseline that we’ve calculated. To make it easier to see those drops, we report that information to you in the form of an estimated performance score and an availability score.

I scroll a bit down the page. The Internet traffic overview map shows the overall event status across all monitored locations. I look at the details in the Health events table. It also highlights other events that are happening globally, sorted by total traffic impact. I notice that a performance issue in Las Vegas, Nevada, US, is affecting my application traffic the most.

Now that I have identified the issue, I am curious about the historical data. Has it happened before?

I select the Historical Explorer tab to understand trends and see earlier data related to this location and network provider. I can view aggregated metrics such as performance score, availability score, bytes transferred, and round-trip time at p50, p90, and p95 percentiles, for a customized timeframe, up to 18 months in the past.

I can see today’s incident is not the first one. This specific client location and network provider has had multiple issues in the past few months.

Now that I understand the context, I wonder what action I can take to mitigate the issue.

I switch to the Traffic insights tab. I see overall traffic data and top client locations that are being monitored based on total traffic (bytes). Apparently, Las Vegas, Nevada, US, is one of the top client locations.

I select the graph to see traffic details for Las Vegas, Nevada, US. In the Lowest Time To First Byte (TTFB) column, I see AWS service and AWS Region setup recommendations for all of the top client location and network combinations. The Predicted Time To First Byte in the table shows the potential impact if I make the suggested architectural change.

In this example, Internet Monitor suggests having CloudFront distribute the traffic currently distributed by EC2 and to allow for some additional traffic to be served by EC2 instances in us-east-1 in addition to us-east-2.

Available Today
Internet Monitor is available in public preview today in 20 AWS Regions:

In the Americas: US East (Ohio), US East (N. Virginia), US West (N. California), US West (Oregon), Canada (Central), South America (São Paulo).
In Asia and Pacific: Asia Pacific (Hong Kong), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo).
In Europe, Middle East, and Africa: Africa (Cape Town), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Milan), Europe (Paris), Europe (Stockholm), Middle East (Bahrain)

Note that AWS CloudFormation support is missing at the moment; it will be added soon.

There is no costs associated with the service during the preview period. Just keep in mind that Internet Monitor vends metrics and logs to CloudWatch; you will be charged for these additional CloudWatch logs and CloudWatch metrics.

Whether you work for a startup or a large enterprise, CloudWatch Internet Monitor helps you be proactive about your application performance and availability. Give it a try today!

— seb

New for Amazon Transcribe – Real-Time Analytics During Live Calls

2022-11-28 Danilo Poccia

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-amazon-transcribe-real-time-analytics-during-live-calls/

The experience customers have when interacting with a contact center can have a profound impact on them. For this reason, we launched Amazon Transcribe Call Analytics last year to help you analyze customer call recordings and get insights into issues and trends related to customer satisfaction and agent performance.

To assist agents in resolving live calls faster, we are introducing today real-time call analytics in Amazon Transcribe Call Analytics. Real-time call analytics provides APIs for developers to accurately transcribe live calls and at the same time identify customer experience issues and sentiment in real time. Transcribe Call Analytics uses state-of-the-art machine learning capabilities to automatically assess thousands of in-progress calls and detect customer experience issues, such as repeated requests to speak to a manager or cancel a subscription.

With a few clicks, supervisors and analysts can create categories in the AWS console to identify customer experience issues using criteria such as specific terms such as “not happy,” “poor quality,” and “cancel my subscription.” Transcribe Call Analytics analyzes in-progress calls in real time to detect when a category is met. Developers can use those signals, along with sentiment trends from the API, to build a proactive system that alerts supervisors about emerging issues or assists agents with relevant information to solve customer issues.

Transcribe Call Analytics also provides a real-time transcript of the live conversation that supervisors can use to quickly get up to speed on the customer interaction and assess the appropriate action. The in-call transcript also eliminates the need for customers to repeat themselves if the call is transferred to another agent. Agents can focus all their attention on the customer during the call instead of taking notes for entry in a CRM system because Transcribe Call Analytics includes an automated call summarization capability, which identifies the issue, outcome, and action item associated with a call.

Transcribe Call Analytics is a foundational API for AWS Contact Center Intelligence solutions such as post-call analytics and the updated real-time call analytics with agent assist solution using the new real-time capabilities.

Let’s see how this works in practice.

Exploring Real-Time Call Analytics in the Console
To see how this works visually, I use the Amazon Transcribe console. First, I create a category to be notified if some terms are used in the call that would require an escalation. I choose Category Management from the navigation pane and then Create category.

I enter Escalation as the name for the category. I select REAL_TIME in the Category type dropdown. Then, I choose Create from scratch.

I only need one rule for this category. In the Rule type dropdown, I select Transcript content match. In the next three options, I choose to trigger the rule when any of the words are mentioned during the entire call, and the speaker is either the customer or the agent. Now, I can enter the words or phrases to look for in the transcript. In this case, I enter cancel, canceled, cancelled, manager, and supervisor. In your case, you might be more specific depending on your business. For example, if subscriptions are your business, you can look for the phrase cancel my subscription.

Now that the category has been created, I use one of the sample calls in the console to test it. I choose Real-Time Analytics in the navigation pane. By choosing Configure advanced settings, I can configure the personally identifiable information (PII) identification and redaction settings. For example, I can choose to identify personal data such as email addresses or redact financial data like bank account numbers.

With no additional charge, I can enable Post-call Analytics so that, at the end of the call, I receive the output of the transcription job in an Amazon Simple Storage Service (Amazon S3) bucket. This output is in a similar format to what I’d receive if I were analyzing a call recording with Transcribe Call Analytics. In this way, I can use the post-call analytics output derived from the audio stream in any process I already have in place for output of analytics generated from call recordings, for example, to update dashboards or generate periodic reports.

With Insurance complaints in Step 1: Specify input audio selected, I choose Start streaming. In the Transcription output section of the console, I receive in real-time the transcription of the call. The words of the customer and agent appear as they are pronounced. Each sentence is flagged with its recognized sentiment (positive, neutral, or negative). The Escalation category that I just configured is found in two sentences, first, when the customer mentions that their insurance has been canceled, and then when the agent mentions their manager. Also, part of a sentence is underlined because an issue has been detected.

Using the Download dropdown, I download the full JSON transcript. If I am only interested in the transcription, I can download the text transcript. The JSON transcript contains an array where each item is similar to what I’d get in real time when using the real-time call analytics API.

Using the Live Call Analytics With Agent Assist (LCA) Solution
You can use the open-source real-time call analytics with agent assist solution for your contact center or as an inspiration of what Amazon Transcribe enables for developers. Let’s look at a couple of screenshots to understand how it works.

Here there is a list of on-going calls with the overall sentiment, the sentiment trend (is it improving or not?), and the categories found in real-time during the call that can be used for specific activities.

When selecting a call from the list, you have access to more in-depth information, including the call transcript and the issues found during the on-going call. This allows to take action quickly to help resolve the call.

Availability and Pricing
Amazon Transcribe Call Analytics with real-time capabilities is available today in US (N. Virginia, Oregon), Canada (Central), Europe (Frankfurt, London), and Asia Pacific (Seoul, Sydney, Tokyo) and supports US English, British English, Australian English, US Spanish, Canadian French, French, German, Italian, and Brazilian Portuguese.

With Amazon Transcribe Call Analytics, you pay as you go and are billed monthly based on tiered pricing. For more information, see Amazon Transcribe pricing.

As part of the AWS Free Tier, you can get started with Amazon Transcribe Call Analytics for free, including the new real-time call analytics API. You can analyze up to 60 minutes of call audio monthly for free for the first 12 months. For more information, see the AWS Free Tier page.

If you’re at re:Invent, you can learn more about this new capability in session AIM307 – JPMorganChase real-time agent assist for contact center productivity. I will update this post when the recording of the session is publicly available.

Start analyzing contact center conversations in real-time to improve your customers’ experience.

— Danilo

Automated in-AWS Failback for AWS Elastic Disaster Recovery

2022-11-28 Steve Roberts

Post Syndicated from Steve Roberts original https://aws.amazon.com/blogs/aws/automated-in-aws-failback-for-aws-elastic-disaster-recovery/

I first covered AWS Elastic Disaster Recovery (DRS) in a 2021 blog post. In that post, I described how DRS “enables customers to use AWS as an elastic recovery site for their on-premises applications without needing to invest in on-premises DR infrastructure that lies idle until needed. Once enabled, DRS maintains a constant replication posture for your operating systems, applications, and databases.” I’m happy to announce that, today, DRS now also supports in-AWS failback, adding to the existing support for non-disruptive recovery drills and on-premises failback included in the original release.

I also wrote in my earlier post that drills are an important part of disaster recovery since, if you don’t test, you simply won’t know for sure that your disaster recovery solution will work properly when you need it to. However, customers rarely like to test because it’s a time-consuming activity and also disruptive. Automation and simplification encourage frequent drills, even at scale, enabling you to be better prepared for disaster, and now you can use them irrespective of whether your applications are on-premises or in AWS. Non-disruptive recovery drills provide confidence that you will meet your recovery time objectives (RTOs) and recovery point objectives (RPOs) should you ever need to initiate a recovery or failback. More information on RTOs and RPOs, and why they’re important to define, can be found in the recovery objectives documentation.

The new automated support provides a simplified and expedited experience to fail back Amazon Elastic Compute Cloud (Amazon EC2) instances to the original Region, and both failover and failback processes (for on-premises or in-AWS recovery) can be conveniently started from the AWS Management Console. Also, for customers that want to customize the granular steps that make up a recovery workflow, DRS provides three new APIs, linked at the bottom of this post.

Failover vs. Failback
Failover is switching the running application to another Availability Zone, or even a different Region, should outages or issues occur that threaten the availability of the application. Failback is the process of returning the application to the original on-premises location or Region. For failovers to another Availability Zone, customers who are agnostic to the zone may continue running the application in its new zone indefinitely if so required. In this case, they will reverse the recovery replication, so the recovered instance is protected for future recovery. However, if the failover was to a different Region, its likely customers will want to eventually fail back and return to the original Region when the issues that caused failover have been resolved.

The below images illustrate architectures for in-AWS applications protected by DRS. The architecture in the image below is for cross-Availability Zone scenarios.

The architecture diagram below is for cross-Region scenarios.

Let’s assume an incident occurs with an in-AWS application, so we initiate a failover to another AWS Region. When the issue has been resolved, we want to fail back to the original Region. The following animation illustrates the failover and failback processes.

Learn more about in-AWS failback with Elastic Disaster Recovery
As I mentioned earlier, three new APIs are also available for customers who want to customize the granular steps involved. The documentation for these can be found using the links below.

The new in-AWS failback support is available in all Regions where AWS Elastic Disaster Recovery is available. Learn more about AWS Elastic Disaster Recovery in the User Guide. For specific information on the new failback support I recommend consulting this topic in the service User Guide.

— Steve

New – Amazon ECS Service Connect Enabling Easy Communication Between Microservices

2022-11-28 Channy Yun

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/new-amazon-ecs-service-connect-enabling-easy-communication-between-microservices/

Microservices architectures are a well-known software development approach to make applications composed of small independent services that communicate over well-defined application programming interfaces (APIs). Customers faced challenges when they started breaking down their monolith applications into microservices, as it required specialized networking knowledge to communicate internally with other microservices.

Amazon Elastic Container Services (Amazon ECS) customers have several solutions for service-to-service, but each one comes with some challenges and complications: 1) Elastic Load Balancing (ELB) needs to carefully plan for configuring infrastructure for high availability and incur additional infrastructure cost. 2) Using Amazon ECS Service Discovery often requires developers to write custom application code for collecting traffic metrics and for making network calls resilient. 3) Service mesh solutions such as AWS App Mesh run outside of Amazon ECS despite having advanced traffic monitoring and routing features between services.

Today, we are announcing the general availability of Amazon ECS Service Connect, a new capability that simplifies building and operating resilient distributed applications. ECS Service Connect provides an easy network setup and seamless service communication deployed across multiple ECS clusters and virtual private clouds (VPCs). You can add a layer of resilience to your ECS service communication and get traffic insights with no changes to your application code.

With ECS Service Connect, you can refer and connect to your services by logical names using a namespace provided by AWS Cloud Map and automatically distribute traffic between ECS tasks without deploying and configuring load balancers. You can set some safe defaults for traffic resilience, such as health checking, automatic retries for 503 errors, and connection draining, for each of your ECS services. Additionally, the Amazon ECS console provides easy-to-use dashboards with real-time network traffic metrics for operational convenience and simplified debugging.

Getting Started with Amazon ECS Service Connect
To get started with the ECS Service Connect, you can specify a namespace as part of creating an ECS cluster or create one in the Cloud Map. A namespace represents a way to structure your services and can span across multiple ECS clusters residing in different VPCs. All ECS services that belong to a specific namespace can communicate with existing services in the namespaces, provided existing network-level connectivity.

You can also see a list of Cloud Map namespaces in Namespaces in the left navigation pane of the Amazon ECS console. When you select a namespace, it shows a list of services with the same namespace from two different ECS clusters with database services (db-mysql, db-redis) and backend services (webui, appserver).

When you create an ECS cluster, you can select one of the namespaces in the Default namespaces of the Networking setting. ECS Service Connect is enabled for all new ECS services running in both AWS Fargate and Amazon EC2 instances. To enable all existing services, you would need to redeploy with either a new version of ECS-optimized Amazon Machine Image (AMI), or with a new Fargate Agent that supports ECS Service Connect.

Or, you can simply create a cluster via AWS Command Line Interface (AWS CLI) with serviceConnect parameter and a default Cloud Map namespace name for service discovery purposes.

$ aws ecs create-cluster
     --cluster "svc-cluster-2"
     --serviceConnect {
       "defaultNamespace": "svc-namespace"
}

This command will create an ECS cluster with the namespace on AWS’s behalf. If you would like to use an already existing Cloud Map namespace, you can simply pass the name of the existing namespace here.

Next, let’s create a service with a task definition and expose your web user-interface server using ECS Service Connect.

$ aws ecs create-service
--cluster "svc-cluster-2"
--service-name "webui"
--task-definition "webui-svc-cluster"
--serviceConnect {
  "enabled": true,
  "namespace": "svc-namespace",
  "services":
   [
      {
         "portName": "webui-port",
         "discoveryName": "webui-svc",
         "clientAliases": [
           {
              "port": 80, // *Required *//
              "dnsName": "webui-svc-domain" // * Optional *//
            }
        }
     ]
   ]
}

In this command, portName represents a reference to the container port, and clientAliases assigns the port number and DNS name, overriding the discovery name that is used in the endpoint. Each service has an endpoint URL that contains the protocol, a DNS name, and the port. You can select the protocol and port name in the task definition or the ECS service configuration. For example, an endpoint could be http://webui:80, grpc://appserver:8080, or http://db-redis:8888.

In the ECS console, you can see this configuration of ECS Service Connect for the webui service in the svc-cluster-2 cluster.

As you can see, you can run the same workloads across different clusters with the same clientAlias and namespace name for high availability. ECS Service Connect will intelligently load balance the traffic to the ECS tasks. To connect to services running in different ECS clusters, you need to specify the same namespace name for all your ECS services that need to talk to each other. ECS Service Connect will make your services discoverable to all other services in the same namespace.

Improving Service Resilience with Observability Data
You can collect traffic metrics with ECS Service Connect observability capabilities. By default, for each ECS service, you can see the number of healthy and unhealthy endpoints, along with inbound and outbound traffic volume.

ECS Service Connect supports HTTP/1, HTTP/2, gRPC, and TCP protocols. So, you can collect the number of requests, number of HTTP errors, and average call latency. For gRPC and TCP, you can see the total number of active connections. All of these metrics are pushed to Amazon CloudWatch or other AWS analytics services via custom log routing

In the Advanced menu, you can publish ECS Service Connect Agent logs for help in debugging in case of issues.

These metrics are only visible in the original interface of the CloudWatch console. When you use the CloudWatch console, switch to the original interface to see the additional metric dimensions of “discovery name” and “target discovery name” under the ECS grouping.

The default settings provide you with a starting point for building resilient applications, and you can fine-tune parameters to limit the impact of failures, latency spikes, and network fluctuations on your application behavior using AWS Management Console or dedicated ECS APIs.

Now Available
Amazon ECS Service Connect is available in all commercial Regions, except China, where Amazon ECS is available. ECS Service Connect is fully supported in AWS CloudFormation, AWS CDK, AWS Copilot, and AWS Proton for infrastructure provisioning, code deployments, and monitoring of your services. To learn more, see the Amazon ECS Service Connect Developer Guide.

My colleagues, Hemanth AVS, Senior Container Specialist SA, and Satya Vajrapu, Senior DevOps Consultant, prepared a hands-on workshop to demonstrate an example of the ECS Service Connect. Join CON303 Networking, service mesh, and service discovery with Amazon ECS when you attend AWS re:Invent 2022.

Give it a try, and please send feedback to AWS re:Post for Amazon ECS or through your usual AWS support contacts.

– Channy

Now Open the 30th AWS Region – Asia Pacific (Hyderabad) Region in India

2022-11-22 Channy Yun

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/now-open-the-30th-aws-region-asia-pacific-hyderabad-region-in-india/

In November 2020, Jeff announced the upcoming AWS Asia Pacific (Hyderabad) as the second Region in India. Yes! Today we are announcing the general availability of the 30th AWS Region, Asia Pacific (Hyderabad) Region, with three Availability Zones and the ap-south-2 API name.

The Asia Pacific (Hyderabad) Region is located in the state of Telangana. As the capital and the largest city in Telangana, Hyderabad is already an important talent hub for IT professionals and entrepreneurs. For example, AWS Hyderabad User Groups has more than 4,000 community members and holds active meetups, including an upcoming Community Day in December 2022.

The new Hyderabad Region gives customers an additional option for running their applications and serving end users from data centers located in India. Customers with data-residency requirements arising from statutes, regulations, and corporate policy can run workloads and securely store data in India while serving end users with even lower latency.

Here are the latest numbers of latency:

AWS Services in the Asia Pacific (Hyderabad) Region
In the new Hyderabad Region, you can use C5, C5d, C6g, M5, M5d, M6gd, R5, R5d, R6g, I3, I3en, T3, and T4g instances, and use a long list of AWS services including: Amazon API Gateway, AWS AppConfig, AWS Application Auto Scaling, Amazon Aurora, Amazon EC2 Auto Scaling, AWS Config, AWS Certificate Manager, AWS Cloud Control API, AWS CloudFormation, AWS CloudTrail, Amazon CloudWatch, Amazon CloudWatch Events, Amazon CloudWatch Logs, AWS CodeDeploy, AWS Database Migration Service, AWS Direct Connect, Amazon DynamoDB, Amazon Elastic Block Store (Amazon EBS), Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service (Amazon ECS), Amazon ElastiCache, Amazon EMR, Elastic Load Balancing, Elastic Load Balancing – Network (NLB), Amazon EventBridge, AWS Fargate, AWS Health Dashboard, AWS Identity and Access Management (IAM), Amazon Kinesis Data Streams, AWS Key Management Service (AWS KMS), AWS Lambda, AWS Marketplace, Amazon OpenSearch Service, Amazon Relational Database Service (Amazon RDS), Amazon Redshift, Amazon Route 53, AWS Secrets Manager, Amazon Simple Storage Service (Amazon S3), Amazon S3 Glacier, Amazon Simple Notification Service (Amazon SNS), Amazon Simple Queue Service (Amazon SQS), AWS Step Functions, AWS Support API, Amazon Simple Workflow Service (Amazon SWF), AWS Systems Manager, AWS Trusted Advisor, VM Import/Export, Amazon Virtual Private Cloud (Amazon VPC), AWS VPN, and AWS X-Ray.

AWS in India
AWS has a long-standing history of helping drive digital transformation in India. AWS first established a presence in the country in 2011, with the opening of an office in Delhi. In 2016, AWS launched the Asia Pacific (Mumbai) Region giving enterprises, public sector organizations, startups, and SMBs access to state-of-the-art public cloud infrastructure. In May 2019, AWS expanded the Region to include a third Availability Zone to support rapid customer growth and provide more choice, flexibility, the ability to replicate workloads across more Availability Zones, and even higher availability.

There are currently 33 Amazon CloudFront edge locations: Mumbai, India (10), New Delhi (7), Chennai (7), Bangalore (4), Hyderabad (3), and Kolkata (2) in India. The edge locations work in concert with a CloudFront Regional edge cache in Mumbai to speed delivery of content. There are six AWS Direct Connect locations, all of which connect to the Asia Pacific (Mumbai) Region: two in Mumbai, one in Chennai, one in Hyderabad, one in Delhi, and one in Bangalore. Finally, the first AWS Local Zones launched in Delhi, India for bringing selected AWS services very close to a particular geographic area. We announced plans to launch three more AWS Local Zones in India, in the cities of Chennai, Bengaluru, and Kolkata.

AWS is also investing in the future of the Indian technology community and workforce, training tech professionals to expand their skillset and cloud knowledge. In fact, since 2017, AWS has trained over three million individuals in India on cloud skills. AWS has worked with government officials, educational institutes, and corporate organizations to achieve this milestone, which has included first-time learners and mid-career professionals alike.

AWS continues to invest in upskilling local developers, students, and the next generation of IT leaders in India through programs such as AWS Academy, AWS Educate, AWS re/Start, and other Training and Certification programs.

AWS Customers in India
We have many amazing customers in India that are doing incredible things with AWS, for example:

SonyLIV is the first Over the top (OTT) service in India born on the AWS Cloud. SonyLIV launched Kaun Banega Crorepati (KBC) interactive game show to allow viewers to submit answers to questions on the show in real time via their mobile devices. SonyLIV uses Amazon ElastiCache to support real-time, in-memory caching at scale, Amazon CloudFront as a low-latency content delivery network, and Amazon SQS as a highly available message queuing service.
DocOnline is a digital healthcare platform that provides video or phone doctor consultations to over 3.5 million families in 10 specialties and 14 Indian languages. DocOnline delivers over 100,000 consultations, diagnostic tests, and medicines every year. DocOnline has built its entire business on AWS to power its online consultation services 24-7 and to continuously measure and improve health outcomes. Being in the Healthcare domain, DocOnline needs to comply with regulatory guidelines, including Data Residency, PII security, and Disaster Recovery in seismic zones. With the AWS Asia Pacific (Hyderabad) Region, DocOnline can ensure critical patient data is hosted in India on the most secure, extensive, and reliable cloud platform while serving customers with even faster response times.
ICICI Lombard General Insurance is one of the first among the large insurance companies in India to move over 140+ applications, including its core application, to AWS. The rapid advances in technology and computing power delivered by cloud computing are poised to radically change the way insurance is delivered as well as consumed. ICICI Lombard has launched new generation products like cyber insurance, telehealth, cashless homecare, and IoT-based risk management solutions for marine transit insurance, providing seamless integration with various digital partners for digital distribution of insurance products and virtual motor claims inspection solutions, which have seen adoption increase from 61 percent last year to 80 percent this year. ICICI Lombard was able to process group health endorsements for their corporate customers in less than a day as compared to 10–12 days earlier. ICICI Lombard is looking at the cloud for further transformative possibilities in real-time inspection of risk and personalized underwriting.
Ministry of Health and Family Welfare (MoHFW), Government of India, needed a highly reliable, scalable, and resilient technical infrastructure to power a large-scale COVID-19 vaccination drive for India’s more than 1.3 billion citizens in 2021. To facilitate the required performance and speed, the MoHFW engaged India’s Ministry of Electronics and Information Technology to build and launch the Co-WIN application powered by AWS, which scales in seconds to handle user registrations and consistently supports 10 million vaccinations daily.

You can find more customer stories in India.

Available Now
The new Hyderabad Region is ready to support your business. You can find a detailed list of the services available in this Region on the AWS Regional Services List.

With this launch, AWS now spans 96 Availability Zones within 30 geographic Regions around the world, with three new Regions launched in 2022, including the AWS Middle East (UAE) Region, the AWS Europe (Zurich) Region, and the AWS Europe (Spain) Region. We have also announced plans for 15 more Availability Zones and five more AWS Regions in Australia, Canada, Israel, New Zealand, and Thailand.

To learn more, see the Global Infrastructure page, and please send feedback through your usual AWS support contacts in India.

— Channy

Introducing our final AWS Heroes of the year – November 2022

2022-11-16 Taylor Lacy

Post Syndicated from Taylor Lacy original https://aws.amazon.com/blogs/aws/introducing-our-final-aws-heroes-of-the-year-november-2022/

The AWS Heroes program celebrates and recognizes builders who are making an impact within the global AWS community. As we come to the end of 2022, the program is recognizing seven individuals who are passionate about AWS, and focused on organizing and speaking at community events, mentoring, authoring content, and even preserving wildlife. Please meet the newest AWS Heroes!

Ed Miller – San Jose, USA

Machine Learning Hero Ed Miller is a Senior Principal Engineer at Arm where he leads technical engagements with strategic partners around machine learning and IoT. He also volunteers with the BearID Project, developing open source, machine learning solutions for non-invasive wildlife monitoring. Ed is working on a human-in-the-loop machine learning application for identifying the famous fat bears on Explore.org’s Brooks Falls Brown Bears webcam. The serverless application, Bearcam Companion, is built using AWS Amplify and various AWS AI services. You can read about it and other projects on Ed’s blogs at dev.to, Hashnode, and the BearID Project.

Jones Zachariah Noel N – Karnataka, India

Serverless Hero Jones Zachariah Noel N is a Senior Developer Advocate in the Developer Relations ecospace at Freshworks, and has previously worked as a Cloud Architect – Serverless where he was focused on designing and architecting solutions built with the AWS Serverless tech stack. Jones is a tech enthusiast who loves to interact with the community, which has helped him learn and share his knowledge, as he also co-organizes AWS User Group Bengaluru. He writes regularly about AWS Serverless and talks about new features and different Serverless services, which can help you level up your Serverless applications’ architecture on dev.to. Additionally, Jones co-runs a YouTube podcast called The Zacs’ Show Talking AWS about DevOps and Serverless practices along with another Zack whom he met through the AWS Community Builder program.

Luciano Mammino – Dublin, Ireland

Serverless Hero Luciano Mammino is a full-stack web developer and a senior cloud architect at fourTheorem. He is a co-author of the book Node.js Design Patterns and co-host of the podcast AWS Bites. Luciano is one of the creators of Middy, one of the most adopted middleware-based Node.js frameworks for AWS Lambda. Through fourTheorem, he also contributes to several other open-source projects in the serverless space, such as SLIC Watch for automated observability. Finally, he is also an eager tech speaker who has evangelized the adoption of serverless from the very early days.

Madhu Kumar – Budapest, Hungary

Container Hero Madhu Kumar is a Principal Cloud Architect and Product Owner (Container Services) working for T-Systems International with over 22 years of IT experience working across multiple regions, including Asia, the Middle East, the US, Europe, and the UK. He is an AWS User Group Leader, DevSecCon Chapter Leader for Hungary, DevOps Institute Brand Ambassador and Chapter Leader, HashiCorp User Group Leader for Hungary, and formally an AWS Community Builder. Madhu is passionate about organizing meetups, driving and assisting global and local communities to come together, and sharing knowledge. He is also a regular speaker at container conferences and AWS events.

Paweł Zubkiewicz – Wroclaw, Poland

Serverless Hero Paweł Zubkiewicz works as a Cloud Architect and Consultant who helps companies build products on AWS. In 2018, Paweł started Serverless Polska, an online community for serverless enthusiasts where he shares his technical knowledge and introduces serverless to a broader audience. Shortly after, he began publishing a newsletter about serverless and AWS cloud. He continuously shares his expertise and insights with the Polish-speaking community to this day, both online and as a conference speaker. Before becoming an AWS Hero, he was an AWS Community Builder since 2020, and shares serverless tutorials on dev.to. He lives in Wroclaw, Poland with his wife and his dog named Pixel. He’s an avid mountain biker and a traveler.

Rossana Suarez – Resistencia, Argentina

Container Hero Rossana Suarez is a DevOps consultant and trainer. She started the ‘295devops’ channel to share her expertise about various DevOps topics, and to help enthusiasts get into the field more easily and with more motivation. She consults with teams of developers and DevOps engineers to help them improve their existing processes for automations, CI/CD, containerization, and orchestration. Rossana presents at Women in Technology’s local meetups to encourage more women to pursue careers in DevOps, is a volunteer with AWS Girls Argentina, and is a frequent speaker about container technologies at AWS Community Days, ContainersDays, and more.

TaeSeong Park – Seoul, Korea

Community Hero TaeSeong Park is a front-end engineer and Unity mobile developer working at IDEASAM. He’s spoken at major AWSKRUG community events, and has led hands-on labs specific to a front-end and mobile app on AWS Amplify. For the past 5 years, TaeSeong has been an organizer of the AWSKRUG Group and was an AWS Community Builder for 2-years. Not only he did he organize the AWSKRUG Gudi meetup, but he’s been a speaker and supporter of other AWSKRUG meetups.

Learn More

If you’d like to learn more about the new Heroes or connect with a Hero near you, please visit the AWS Heroes website or browse the AWS Heroes Content Library.

— Taylor

A New AWS Region Opens in Switzerland

2022-11-09 Sébastien Stormacq

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/a-new-aws-region-opens-in-switzerland/

I am pleased to announce today the opening of our 28th AWS Region: Europe (Zurich), also known by its API name: eu-central-2.

An AWS Region allows you to deploy your most demanding workloads and replicate your applications and data across distinct groups of data centers called Availability Zones. This new Region has three fully redundant Availability Zones located in the vicinity of Zurich. It offers your customers low-latency access to your applications while meeting your data residency requirements.

Regions and Availability Zones
AWS has the concept of a Region. Each Region is fully isolated from all other Regions. Within each Region, we have built Availability Zones. These Availability Zones are fully isolated partitions of our infrastructure that contain a cluster of data centers. Availability Zones are typically separated by multiple kilometers to mitigate the impact of disasters that could affect data centers. The distance between Availability Zones varies between Regions. The distance is large enough to avoid having data centers impacted by the same event at the same time but close enough to allow workloads with synchronous data replication. Availability Zones are linked by redundant, high-bandwidth, and low-latency network connections. Regions are linked by our custom-built, global, low-latency, private network with exabits per second of capacity in Europe.

Unlike other cloud providers, who often define a region as a single data center, the multiple Availability Zone design of every AWS Region offers advantages such as security, availability, performance, and scalability.

Instances and Services
The workloads deployed to this new Europe (Zurich) three-AZ Region can use C5, C5d, I3, I3en, M5, M5d, M6gd, R5, R5d, and T3 instances, and can use a long list of AWS services including Amazon API Gateway, AWS AppConfig, AWS Application Auto Scaling, Amazon Aurora, Amazon EC2 Auto Scaling, AWS Config, AWS Certificate Manager, AWS CloudFormation, AWS CloudTrail, Amazon CloudWatch, Amazon CloudWatch Events, Amazon CloudWatch Logs, AWS CodeDeploy, AWS Database Migration Service (AWS DMS), AWS Direct Connect, Amazon DynamoDB, Amazon Elastic Block Store (Amazon EBS),Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service (Amazon ECS), AWS Fargate, Amazon ElastiCache, Amazon EMR, Amazon OpenSearch Service, Elastic Load Balancing, Elastic Load Balancing – Network (NLB), Amazon EventBridge, Amazon Simple Storage Service Glacier, AWS Identity and Access Management (IAM), Amazon Kinesis Data Streams, AWS Key Management Service (AWS KMS), AWS Lambda, AWS Marketplace, AWS Health Dashboard, Amazon Relational Database Service (Amazon RDS), Aurora PostgreSQL, Amazon Redshift, Amazon Route 53, Amazon Virtual Private Cloud (Amazon VPC), AWS Secrets Manager, Amazon Simple Storage Service (Amazon S3), Amazon Simple Notification Service (Amazon SNS), Amazon Simple Queue Service (Amazon SQS), AWS Step Functions, AWS Support API, Amazon Simple Workflow Service (Amazon SWF), AWS Systems Manager, AWS Trusted Advisor, VM Import/Export, AWS VPN, and AWS X-Ray.

Continuous Investments in Switzerland
AWS has a long history of presence in Switzerland. We have worked with Swiss customers and partners since the launch of AWS 16 years ago. The first Swiss office was opened in Zurich in April 2016 to host the growing local team of technical and business professionals dedicated to supporting Swiss customers. In 2017, the AWS network was expanded into Switzerland with the launch of an Amazon CloudFront edge location and an AWS Direct Connect location. To support this growth, a second AWS office was opened in Geneva.

AWS plans to invest up to 5.9 billion Swiss francs (approximately $5.9 billion) in the Europe (Zurich) Region from 2022–2036 as we build, maintain, operate, and develop data centers to support the projected growth in demand for AWS technologies by our customers.

According to an AWS Economic Impact Study (EIS), this investment will contribute 16.3 billion Swiss francs (approximately $16.3 billion) to the GDP of Switzerland during the same period. This includes the value added by AWS services to the IT sector in Switzerland, as well as the direct, indirect, and induced effects of AWS purchases from the Swiss data center supply chain. The study estimates that this investment will support an average of 2,500 full-time jobs annually at external businesses in the Swiss data supply chain from 2022–2036.

Servicing our Swiss Customers
More than 10,000 Swiss customers use AWS services today. Organizations such as Fisch Asset Management, Helvetia, Eidgenössische Technische Hochschule Zürich (ETH Zürich), Richemont, Swiss Broadcasting Corporation (RSI), Swiss Post, Swisscom, and Swisstopo, just to name a few, use AWS. Private and public sector organizations in Switzerland use AWS to accelerate their time to market, reduce costs associated with IT operations, and scale their businesses globally.

Global luxury group Richemont, owners of prestigious brands like Cartier, Montblanc, IWC Schaffhausen, and Van Cleef & Arpels, moved its entire enterprise IT infrastructure, including 120 SAP instances, to AWS. AWS, with its depth and breadth of services, enables Richemont to provide their customers with new digital experiences faster, including personalized storefronts and styling services, video chat consultations featuring fashion shows customized to the shoppers’ tastes, and tailored offers for early access to new items before they hit stores.

Swisscom, Switzerland’s leading telecoms company and one of its leading IT companies, is using AWS’s proven and broad infrastructure and cloud capabilities to power its 5G network, increase operational efficiency, and fuel innovation. Swisscom is pursuing a cloud-first strategy and will use AWS to increase IT agility, drive operational efficiencies, and accelerate time to market for new information and communications technology (ICT) features and services.

With AWS infrastructure, Swiss startups have been able to quickly scale their businesses and compete globally. Ava, a digital women’s health startup (acquired by FemTec Health) with offices in Zurich, San Francisco, Makati, and Belgrade, is all in on AWS. They created the Ava Fertility Tracker as a daily companion for women, which provides women with real-time, personalized information about fertility, pregnancy, and general health. The Ava bracelet is now sold in 36 countries worldwide and has been running on AWS since the first sales day.

Extending Reach through AWS Partner Network
Switzerland-based AWS Partner Network (APN) Partners also welcomed the news of the launch of the Europe (Zurich) Region.

The APN includes tens of thousands of independent software vendors (ISVs) and systems integrators (SIs) around the world. AWS SIs, consulting partners, and ISVs help enterprise and public sector customers migrate to AWS, deploy mission-critical applications, and provide a full range of services for your cloud environments. We have more than 150 partners ready to help you in Switzerland, one third of them have their headquarters in the country.

Promoting a Diverse Community of Professionals
In December 2020, Amazon announced that it will help 29 million people around the world grow their technological skills with free cloud computing skills training by 2025. Switzerland is part of this global effort. Since 2019, AWS and our AWS training partner Digicomp have delivered training and certification programs to individual learners, customers, and AWS Partners to rapidly build cloud skills and close the skills gap.

Several universities in Switzerland have delivered AWS Academy courses as part of their curriculum, including FHNW (Fachhochschule Nordwestschweiz), Fachhochschule Luzern, and Technische Berufsschule Zürich. To date, 32 Swiss institutions participated in the AWS Academy program, and 16 of them offered classes in 2022.

In March 2022, AWS launched AWS re/Start in Switzerland in collaboration with Powerhouse Lausanne, a training provider that promotes digital equality and diversity in Switzerland. A second cohort of AWS re/Start began in October 2022 in collaboration with the non profit Powercoders, which is focused on teaching IT skills specifically to refugees and helping them transition into the Swiss labor market.

Available Today
With the launch of the Europe (Zurich) Region, AWS is further expanding its infrastructure offering, empowering you with the flexibility to run applications on the most secure and reliable cloud infrastructure while maintaining local data residency and providing the lowest possible latency for Swiss end-users. The new Region is available today on the AWS Management Console and for your API calls.

Go and deploy your workloads on eu-central-2 today!

— seb

AWS Week in Review – November 7, 2022

2022-11-07 Jeff Barr

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-week-in-review-november-7-2022/

With three weeks to go until AWS re:Invent opens in Las Vegas, the AWS News Blog Team is hard at work creating blog posts to share the latest launches and previews with you. As usual, we have a strong mix of new services, new features, and a surprise or two.

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

Amazon SNS Data Protection and Masking – After a quick public preview, this cool feature is now generally available. It uses pattern matching, machine learning models, and content policies to help protect data at scale. You can find many different kinds of personally identifiable information (PII) and protected health information (PHI) in message bodies and either block message delivery or mask (de-identify) the sensitive data, all in real-time and on a per-topic basis. To learn more, read the blog post or the message data protection documentation.

Amazon Textract Updates – This service extracts text, handwriting, and data from any document or image. This past week we updated the AnalyzeID function so that it can now extract the machine readable zone (MRZ) on passports issued by the United States, and we added the entire OCR output to the API response. We also updated the machine learning models that power the AnalyzeDocument function, with a focus on single-character boxed forms commonly found on tax and immigration documents. Finally, we updated the AnalyzeExpense function with support for new fields and higher accuracy for existing fields, bringing the total field count to more than 40.

Another Amazon Braket Processor – Our quantum computing service now supports Aquila, a new 256-qubit quantum computer from QuEra that is based on a programmable array of neutral Rubidium atoms. According to the What’s New, Aquila supports the Analog Hamiltonian Simulation (AHS) paradigm, allowing it to solve for the static and dynamic properties of quantum systems composed of many interacting particles.

Amazon S3 on Outposts – This service now lets you use additional S3 Lifecycle rules to optimize capacity management. You can expire objects as they age or are replaced with newer versions, with control at the bucket level, or for subsets defined by prefixes, object tags, or object sizes. There’s more info in the What’s New and in the S3 documentation.

AWS CloudFormation – There were two big updates last week: support for Amazon RDS Multi-AZ deployments with two readable standbys, and better access to detailed information on failed stack instances for operations on CloudFormation StackSets.

Amazon MemoryDB for Redis – You can now use data tiering as a lower cost way to to scale your clusters up to hundreds of terabytes of capacity. This new option uses a combination of instance memory and SSD storage in each cluster node, with all data stored durably in a multi-AZ transaction log. There’s more information in the What’s New and the blog post.

Amazon EC2 – You can now remove launch permissions for Amazon Machine Images (AMIs) that are directly shared with your AWS account.

X in Y – We launched existing AWS services and instance types in additional Regions:

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Here are some additional news items that you may find interesting:

AWS Open Source News and Updates – My colleague Ricardo Sueiras highlights new open source projects, tools, and demos from the AWS Community. Read Installment 134 to see what’s going on!

New Case Study – A new AWS case study describes how Taggle (a company focused on smart water solutions in Australia) created an IoT platform that runs on AWS and uses Amazon Kinesis Data Streams to store & ingest data in real time. Using AWS allowed them to scale to accommodate 80,000 additional sensors that will roll out in 2022.

Upcoming AWS Events
re:Invent 2022 – AWS re:Invent is just three weeks away! Join us live from November 28th to December 2nd for keynotes, training and certification opportunities, and over 1,500 technical sessions. If you cannot make it to Las Vegas you can also join us online to watch the keynotes and leadership sessions live. Be sure to check out the re:Invent 2022 Attendee Guides, each curated by an AWS Hero, AWS industry team, or AWS partner.

PeerTalk – If you will be attending re:Invent in person and are interested in meeting with me or any of our featured experts, be sure to check out PeerTalk, our new onsite networking program.

That’s all for this week!

— Jeff;

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS.

Introducing Amazon Neptune Serverless – A Fully Managed Graph Database that Adjusts Capacity for Your Workloads

2022-10-27 Danilo Poccia

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/introducing-amazon-neptune-serverless-a-fully-managed-graph-database-that-adjusts-capacity-for-your-workloads/

Amazon Neptune is a fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. With Neptune, you can use open and popular graph query languages to execute powerful queries that are easy to write and perform well on connected data. You can use Neptune for graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security.

Neptune has always been fully managed and handles time-consuming tasks such as provisioning, patching, backup, recovery, failure detection and repair. However, managing database capacity for optimal cost and performance requires you to monitor and reconfigure capacity as workload characteristics change. Also, many applications have variable or unpredictable workloads where the volume and complexity of database queries can change significantly. For example, a knowledge graph application for social media may see a sudden spike in queries due to sudden popularity.

Introducing Amazon Neptune Serverless
Today, we’re making that easier with the launch of Amazon Neptune Serverless. Neptune Serverless scales automatically as your queries and your workloads change, adjusting capacity in fine-grained increments to provide just the right amount of database resources that your application needs. In this way, you pay only for the capacity you use. You can use Neptune Serverless for development, test, and production workloads and optimize your database costs compared to provisioning for peak capacity.

With Neptune Serverless you can quickly and cost-effectively deploy graphs for your modern applications. You can start with a small graph, and as your workload grows, Neptune Serverless will automatically and seamlessly scale your graph databases to provide the performance you need. You no longer need to manage database capacity and you can now run graph applications without the risk of higher costs from over-provisioning or insufficient capacity from under-provisioning.

With Neptune Serverless, you can continue to use the same query languages (Apache TinkerPop Gremlin, openCypher, and RDF/SPARQL) and features (such as snapshots, streams, high availability, and database cloning) already available in Neptune.

Let’s see how this works in practice.

Creating an Amazon Neptune Serverless Database
In the Neptune console, I choose Databases in the navigation pane and then Create database. For Engine type, I select Serverless and enter my-database as the DB cluster identifier.

I can now configure the range of capacity, expressed in Neptune capacity units (NCUs), that Neptune Serverless can use based on my workload. I can now choose a template that will configure some of the next options for me. I choose the Production template that by default creates a read replica in a different Availability Zone. The Development and Testing template would optimize my costs by not having a read replica and giving access to DB instances that provide burstable capacity.

For Connectivity, I use my default VPC and its default security group.

Finally, I choose Create database. After a few minutes, the database is ready to use. In the list of databases, I choose the DB identifier to get the Writer and Reader endpoints that I am going to use later to access the database.

Using Amazon Neptune Serverless
There is no difference in the way you use Neptune Serverless compared to a provisioned Neptune database. I can use any of the query languages supported by Neptune. For this walkthrough, I choose to use openCypher, a declarative query language for property graphs originally developed by Neo4j that was open-sourced in 2015 and contributed to the openCypher project.

To connect to the database, I start an Amazon Linux Amazon Elastic Compute Cloud (Amazon EC2) instance in the same AWS Region and associate the default security group and a second security group that gives me SSH access.

With a property graph I can represent connected data. In this case, I want to create a simple graph that shows how some AWS services are part of a service category and implement common enterprise integration patterns.

I use curl to access the Writer openCypher HTTPS endpoint and create a few nodes that represent patterns, services, and service categories. The following commands are split into multiple lines in order to improve readability.

curl https://<my-writer-endpoint>:8182/openCypher \
-d "query=CREATE (mq:Pattern {name: 'Message Queue'}),
(pubSub:Pattern {name: 'Pub/Sub'}),
(eventBus:Pattern {name: 'Event Bus'}),
(workflow:Pattern {name: 'WorkFlow'}),
(applicationIntegration:ServiceCategory {name: 'Application Integration'}),
(sqs:Service {name: 'Amazon SQS'}), (sns:Service {name: 'Amazon SNS'}),
(eventBridge:Service {name: 'Amazon EventBridge'}), (stepFunctions:Service {name: 'AWS StepFunctions'}),
(sqs)-[:IMPLEMENT]->(mq), (sns)-[:IMPLEMENT]->(pubSub),
(eventBridge)-[:IMPLEMENT]->(eventBus),
(stepFunctions)-[:IMPLEMENT]->(workflow),
(applicationIntegration)-[:CONTAIN]->(sqs),
(applicationIntegration)-[:CONTAIN]->(sns),
(applicationIntegration)-[:CONTAIN]->(eventBridge),
(applicationIntegration)-[:CONTAIN]->(stepFunctions);"

This is a visual representation of the nodes and their relationships for the graph created by the previous command. The type (such as Service or Pattern) and properties (such as name) are shown inside each node. The arrows represent the relationships (such as CONTAIN or IMPLEMENT) between the nodes.

Now, I query the database to get some insights. To query the database, I can use either a Writer or a Reader endpoint. First, I want to know the name of the service implementing the “Message Queue” pattern. Note how the syntax of openCypher resembles that of SQL with MATCH instead of SELECT.

curl https://<my-endpoint>:8182/openCypher \
-d "query=MATCH (s:Service)-[:IMPLEMENT]->(p:Pattern {name: 'Message Queue'}) RETURN s.name;"

{
  "results" : [ {
    "s.name" : "Amazon SQS"
  } ]
}

I use the following query to see how many services are in the “Application Integration” category. This time, I use the WHERE clause to filter results.

curl https://<my-endpoint>:8182/openCypher \
-d "query=MATCH (c:ServiceCategory)-[:CONTAIN]->(s:Service) WHERE c.name='Application Integration' RETURN count(s);"

{
  "results" : [ {
    "count(s)" : 4
  } ]
}

There are many options now that I have this graph database up and running. I can add more data (services, categories, patterns) and more relationships between the nodes. I can focus on my application and let Neptune Serverless manage capacity and infrastructure for me.

Availability and Pricing
Amazon Neptune Serverless is available today in the following AWS Regions: US East (Ohio, N. Virginia), US West (N. California, Oregon), Asia Pacific (Tokyo), and Europe (Ireland, London).

With Neptune Serverless, you only pay for what you use. The database capacity is adjusted to provide the right amount of resources you need in terms of Neptune capacity units (NCUs). Each NCU is a combination of approximately 2 gibibytes (GiB) of memory with corresponding CPU and networking. The use of NCUs is billed per second. For more information, see the Neptune pricing page.

Having a serverless graph database opens many new possibilities. To learn more, see the Neptune Serverless documentation. Let us know what you build with this new capability!

Simplify the way you work with highly connected data using Neptune Serverless.

— Danilo

AWS Batch for Amazon Elastic Kubernetes Service

2022-10-25 Steve Roberts

Post Syndicated from Steve Roberts original https://aws.amazon.com/blogs/aws/aws-batch-for-amazon-elastic-kubernetes-service/

Today I’m pleased to announce AWS Batch for Amazon Elastic Kubernetes Service (Amazon EKS). AWS Batch for Amazon EKS is ideal for customers who no longer want to shoulder the burden of configuring, fine-tuning, and managing Kubernetes clusters and pods to use with their batch processing workflows. Furthermore, there is no charge for this service. You only pay for the resources that your batch jobs launch.

When I’ve previously considered Kubernetes, it appeared to be focused on the management and hosting of microservice workloads. I was therefore surprised to discover that Kubernetes is also used by some customers to run large-scale, compute-intensive batch workloads. The differences between batch and microservice workloads mean that using Kubernetes for batch processing can be difficult and requires you to invest significant time in custom configuration and management to fine-tune a suitable solution.

Microservice and batch workloads on Kubernetes
Before we look further at AWS Batch for Amazon EKS, let’s consider some of the important differences between batch and microservice workloads to help set some context on why running batch workloads on Kubernetes can be difficult:

Microservice workloads are assumed to start and not stop—we expect them to be continuously available. In contrast, batch workloads run to completion and then exit—regardless of success or failure.
The results from a batch workload might not be available for several minutes—and sometimes hours or even days. Microservice workloads are expected to respond to requests within milliseconds.
We usually deploy microservice workloads across several Availability Zones to ensure high availability. This isn’t a requirement for batch workloads. Although we might distribute a batch job to allow it to process different input data in a distributed analysis, we more typically want to prioritize fast and optimal access to resources the job needs within the Availability Zone in which it is running.
Microservice and batch workloads scale differently. For microservices, scaling is generally predictable and usually linear as load increases (or decreases). With batch workloads, you might first perform an initial, or infrequently repeated, proof-of-concept run to analyze performance and discover the correct tuning needed for a full production run. The difference in size between the two can be exponential. Furthermore, with batch workloads, we might scale to an extreme level for a run, then scale back to zero instances for long periods of time, sometimes months.

Although third-party frameworks can help with running batch workloads on Kubernetes, you can also roll your own. Whichever approach you take, significant gaps and challenges can remain in handling the undifferentiated heavy lifting of building, configuring, and maintaining custom batch solutions. Then you also need to consider the scheduling, placing, and scaling of batch workloads on Kubernetes in a cost-effective manner. So how does AWS Batch on Amazon EKS help?

AWS Batch for Amazon EKS
AWS Batch for Amazon EKS offers a fully managed service to run batch workloads using clusters hosted on Amazon Elastic Compute Cloud (Amazon EC2) with no need to install and manage complex, custom batch solutions to address the differences highlighted earlier. AWS Batch provides a scheduler that controls and runs high-volume batch jobs, together with an orchestration component that evaluates when, where, and how to place jobs submitted to a queue. There’s no need for you, as the user, to coordinate any of this work—you just submit a job request into the queue.

Job queueing, dependency tracking, retries, prioritization, compute resource provisioning for Amazon Elastic Compute Cloud (EC2) and Amazon Elastic Compute Cloud (EC2) Spot, and pod submission are all handled using a serverless queue. As a managed service, AWS Batch for Amazon EKS enables you to reduce your operational and management overhead and focus instead on your business requirements. It provides integration with other services such as AWS Identity and Access Management (IAM), Amazon EventBridge, and AWS Step Functions and allows you to take advantage of other partners and tools in the Kubernetes ecosystem.

When running batch jobs on Amazon EKS clusters, AWS Batch is the main entry point to submit workload requests. Based on the queued jobs, AWS Batch then launches worker nodes in your cluster to process the jobs. These nodes are kept separate in a distinct namespace from your other node groups in Amazon EKS. Similarly, nodes in other pods are isolated from those used with AWS Batch.

How it works
AWS Batch uses managed Amazon EKS clusters, which need to be registered with AWS Batch, and permissions set so that AWS Batch can launch and manage compute environments in those clusters to process jobs submitted to the queue. You can find instructions on how to launch a managed cluster that AWS Batch can use in this topic in the Amazon EKS User Guide. Instructions for configuring permissions can be found in the AWS Batch User Guide.

Once one or more clusters have been registered, and permissions set, users can submit jobs to the queue. When a job is submitted, the following actions take place to process the request:

On receiving a job request, the queue dispatches a request to the configured compute environment for resources. If an AWS Batch managed scaling group does not yet exist, one is created, and AWS Batch then starts launching Amazon Elastic Compute Cloud (EC2) instances in the group. These new instances are added to the AWS Batch Kubernetes namespace of the cluster.
The Kubernetes scheduler places any configured DaemonSet on the node.
Once the node is ready, AWS Batch starts sending pod placement requests to your cluster, using labels and taints to make the placement choices for the pods, bypassing much of the logic of the k8s scheduler.
This process is repeated, scaling as needed across more EC2 instances in the scaling group until the maximum configured capacity is reached.
If the job queue has another compute environment defined, such as one configured to use Spot instances, it will launch additional nodes in that compute environment.
Once all work is complete, AWS Batch removes the nodes from the cluster, and terminates the instances.

These steps are illustrated in the animation below.

Start using your clusters with AWS Batch today
AWS Batch for Amazon Elastic Kubernetes Service (Amazon EKS) is available today. As I noted earlier, there is no charge for this service, and you pay only for the resources your jobs consume. To learn more, visit the Getting Started with Amazon EKS topic in the AWS Batch User Guide. There is also a self-guided workshop to help introduce you to AWS Batch on Amazon EKS.

— Steve

Amazon EC2 Trn1 Instances for High-Performance Model Training are Now Available

2022-10-11 Antje Barth

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/amazon-ec2-trn1-instances-for-high-performance-model-training-are-now-available/

Deep learning (DL) models have been increasing in size and complexity over the last few years, pushing the time to train from days to weeks. Training large language models the size of GPT-3 can take months, leading to an exponential growth in training cost. To reduce model training times and enable machine learning (ML) practitioners to iterate fast, AWS has been innovating across chips, servers, and data center connectivity.

At AWS re:Invent 2021, we announced the preview of Amazon EC2 Trn1 instances powered by AWS Trainium chips. AWS Trainium is optimized for high-performance deep learning training and is the second-generation ML chip built by AWS, following AWS Inferentia.

Today, I’m excited to announce that Amazon EC2 Trn1 instances are now generally available! These instances are well-suited for large-scale distributed training of complex DL models across a broad set of applications, such as natural language processing, image recognition, and more.

Compared to Amazon EC2 P4d instances, Trn1 instances deliver 1.4x the teraFLOPS for BF16 data types, 2.5x more teraFLOPS for TF32 data types, 5x the teraFLOPS for FP32 data types, 4x inter-node network bandwidth, and up to 50 percent cost-to-train savings. Trn1 instances can be deployed in EC2 UltraClusters that serve as powerful supercomputers to rapidly train complex deep learning models. I’ll share more details on EC2 UltraClusters later in this blog post.

New Trn1 Instance Highlights
Trn1 instances are available today in two sizes and are powered by up to 16 AWS Trainium chips with 128 vCPUs. They provide high-performance networking and storage to support efficient data and model parallelism, popular strategies for distributed training.

Trn1 instances offer up to 512 GB of high-bandwidth memory, deliver up to 3.4 petaFLOPS of TF32/FP16/BF16 compute power, and feature an ultra-high-speed NeuronLink interconnect between chips. NeuronLink helps avoid communication bottlenecks when scaling workloads across multiple Trainium chips.

Trn1 instances are also the first EC2 instances to enable up to 800 Gbps of Elastic Fabric Adapter (EFA) network bandwidth for high-throughput network communication. This second generation EFA delivers lower latency and up to 2x more network bandwidth compared to the previous generation. Trn1 instances also come with up to 8 TB of local NVMe SSD storage for ultra-fast access to large datasets.

The following table lists the sizes and specs of Trn1 instances in detail.

Instance Name	vCPUs	AWS Trainium Chips	Accelerator Memory	NeuronLink	Instance Memory	Instance Networking	Local Instance Storage
trn1.2xlarge	8	1	32 GB	N/A	32 GB	Up to 12.5 Gbps	1x 500 GB NVMe
trn1.32xlarge	128	16	512 GB	Supported	512 GB	800 Gbps	4x 2 TB NVMe

Trn1 EC2 UltraClusters
For large-scale model training, Trn1 instances integrate with Amazon FSx for Lustre high-performance storage and are deployed in EC2 UltraClusters. EC2 UltraClusters are hyperscale clusters interconnected with a non-blocking petabit-scale network. This gives you on-demand access to a supercomputer to cut model training time for large and complex models from months to weeks or even days.

AWS Trainium Innovation
AWS Trainium chips include speciﬁc scalar, vector, and tensor engines that are purpose-built for deep learning algorithms. This ensures higher chip utilization as compared to other architectures, resulting in higher performance.

Here is a short summary of additional hardware innovations:

Data Types: AWS Trainium supports a wide range of data types, including FP32, TF32, BF16, FP16, and UINT8, so you can choose the most suitable data type for your workloads. It also supports a new, configurable FP8 (cFP8) data type, which is especially relevant for large models because it reduces the memory footprint and I/O requirements of the model.
Hardware-Optimized Stochastic Rounding: Stochastic rounding achieves close to FP32-level accuracy with faster BF16-level performance when you enable auto-casting from FP32 to BF16 data types. Stochastic rounding is a different way of rounding floating-point numbers, which is more suitable for machine learning workloads versus the commonly used Round Nearest Even rounding. By setting the environment variable NEURON_RT_STOCHASTIC_ROUNDING_EN=1 to use stochastic rounding, you can train a model up to 30 percent faster.
Custom Operators, Dynamic Tensor Shapes: AWS Trainium also supports custom operators written in C++ and dynamic tensor shapes. Dynamic tensor shapes are key for models with unknown input tensor sizes, such as models processing text.

AWS Trainium shares the same AWS Neuron SDK as AWS Inferentia, making it easy for everyone who is already using AWS Inferentia to get started with AWS Trainium.

For model training, the Neuron SDK consists of a compiler, framework extensions, a runtime library, and developer tools. The Neuron plugin natively integrates with popular ML frameworks, such as PyTorch and TensorFlow.

The AWS Neuron SDK supports just-in-time (JIT) compilation, in addition to ahead-of-time (AOT) compilation, to speed up model compilation, and Eager Debug Mode, for a step-by-step execution.

To compile and run your model on AWS Trainium, you need to change only a few lines of code in your training script. You don’t need to tweak your model or think about data type conversion.

Get Started with Trn1 Instances
In this example, I train a PyTorch model on an EC2 Trn1 instance using the available PyTorch Neuron packages. PyTorch Neuron is based on the PyTorch XLA software package and enables conversion of PyTorch operations to AWS Trainium instructions.

Each AWS Trainium chip includes two NeuronCore accelerators, which are the main neural network compute units. With only a few changes to your training code, you can train your PyTorch model on AWS Trainium NeuronCores.

SSH into the Trn1 instance and activate a Python virtual environment that includes the PyTorch Neuron packages. If you’re using a Neuron-provided AMI, you can activate the preinstalled environment by running the following command:

source aws_neuron_venv_pytorch_p36/bin/activate

Before you can run your training script, you need to make a few modifications. On Trn1 instances, the default XLA device should be mapped to a NeuronCore.

Let’s start by adding the PyTorch XLA imports to your training script:

import torch, torch_xla
import torch_xla.core.xla_model as xm

Then, place your model and tensors onto an XLA device:

model.to(xm.xla_device())
tensor.to(xm.xla_device())

When the model is moved to the XLA device (NeuronCore), subsequent operations on the model are recorded for later execution. This is XLA’s lazy execution which is different from PyTorch’s eager execution. Within the training loop, you have to mark the graph to be optimized and run on the XLA device using xm.mark_step(). Without this mark, XLA cannot determine where the graph ends.

...
for data, target in train_loader:
	output = model(data)
	loss = loss_fn(output, target)
	loss.backward()
	optimizer.step()
	xm.mark_step()
...

You can now run your training script using torchrun <my_training_script>.py.

When running the training script, you can configure the number of NeuronCores to use for training by using torchrun –nproc_per_node.

For example, to run a multi-worker data parallel model training on all 32 NeuronCores in one trn1.32xlarge instance, run torchrun --nproc_per_node=32 <my_training_script>.py.

Data parallel is a strategy for distributed training that allows you to replicate your script across multiple workers, with each worker processing a portion of the training dataset. The workers then share their result with each other.

For more details on supported ML frameworks, model types, and how to prepare your model training script for large-scale distributed training across trn1.32xlarge instances, have a look at the AWS Neuron SDK documentation.

Profiling Tools
Let’s have a quick look at useful tools to keep track of your ML experiments and profile Trn1 instance resource consumption. Neuron integrates with TensorBoard to track and visualize your model training metrics.

On the Trn1 instance, you can use the neuron-ls command to describe the number of Neuron devices present in the system, along with the associated NeuronCore count, memory, connectivity/topology, PCI device information, and the Python process that currently has ownership of the NeuronCores:

Similarly, you can use the neuron-top command to see a high-level view of the Neuron environment. This shows the utilization of each of the NeuronCores, any models that are currently loaded onto one or more NeuronCores, process IDs for any processes that are using the Neuron runtime, and basic system statistics relating to vCPU and memory usage.

Available Now
You can launch Trn1 instances today in the AWS US East (N. Virginia) and US West (Oregon) Regions as On-Demand, Reserved, and Spot Instances or as part of a Savings Plan. As usual with Amazon EC2, you pay only for what you use. For more information, see Amazon EC2 pricing.

Trn1 instances can be deployed using AWS Deep Learning AMIs, and container images are available via managed services such as Amazon SageMaker, Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Container Service (Amazon ECS), and AWS ParallelCluster.

To learn more, visit our Amazon EC2 Trn1 instances page, and please send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Antje

AWS Local Zones Expansion: Taipei and Delhi

2022-10-06 Jeff Barr

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-local-zones-expansion-taipei-and-delhi/

In late 2019 I told you about the AWS Local Zone in Los Angeles, California. In that post I described Local Zones as a new type of AWS infrastructure deployment that brings select AWS services very close to a particular geographic area. A year after that launch, I announced our plans to add 3 more Local Zones in 2020, and 12 more in 2021. Right now, we are working to bring Local Zones to 33 cities in 27 countries including 6 in Latin America.

Applications hosted in a Local Zone benefit from very low (single-digit millisecond) latency access to EC2 instances and other AWS services. Local Zones also give AWS customers additional choices regarding data residency, giving them the ability to store and process sensitive data (often financial or personal in nature) in-country.

Going Global
Today I am happy to announce the launch of Local Zones in Taipei (Taiwan) and Delhi (India). Like the existing Local Zones in the US, you start by enabling them in the AWS Management Console:

After you do this, you can launch Amazon Elastic Compute Cloud (Amazon EC2) instances, create Amazon Elastic Block Store (Amazon EBS) volumes,and make use of other services including Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), and Amazon Virtual Private Cloud (Amazon VPC). The new Local Zones include T3, C5, M5, R5, and G4dn instances in select sizes, along with General Purpose SSD (gp2) EBS volumes.

Local Zones in Action
AWS customers are working to put Local Zones to use. Here are a few use cases:

AdvaHealth Solutions – This digital health care & life sciences company supports radiology, oncology, ophthalmology and other medical imaging applications with AdvaPACS, a cloud-native image archive. The new Local Zones will allow them to deliver diagnostic image data with low latency in order to improve the point-of-care experience for patients and health care providers across Asia, and will also support expansion into new markets.

M2P Fintech specializes in building financial infrastructure and is an API infrastructure platform for Banking, Lending and Payments products. More than 600 Fintechs, 100 Banks & 100 Financial Institutions across MENA and APAC regions rely on M2P’s platform to power their own branded products including category leaders across ride-hailing, food delivery, and credit cards. M2P uses Local Zones instead of bearing the burden of setting up their own data centers and to meet local requirements for data processing and storage.

NaranjaX – This financial services company is the primary credit card issuer in Argentina. They are engaged in a digital transformation with the target of delivering an improved financial solution to their commercial customers, and believe that using Local Zones will give these customers a strategic advantage.

Pluto XR is the developer of the PlutoSphere OS that enables gamers, developers and operators to live stream XR applications to any XR device. In order to deliver a high quality streaming experience, they run their application as close to their end users as possible. The new Local Zones will allow them to serve millions of users in new metro areas

Riot Games is an American video game developer, publisher and entertainment company based in Los Angeles, California. Their games deliver an optimal player experience through ultra low latency for their MOBA (Multiplayer Online Battle Arena) League of Legends and their first-person tactical shooter VALORANT. By deploying their games into Local Zones, Riot is able to serve players at low latency without the need for operating on-premises compute.

Zenga Media is one of the largest media-tech companies in India. They provide live streaming and over-the-top distribution of entertainment content to millions of users globally, while using cloud-based video editing and sharing to process content destined for TV shows, sports broadcasts, news, and movies. They will use Local Zones to provide local connectivity to their editors and customers, thereby speeding processing and delivering a superior video streaming experience to customers.

Local Zones Resources
Here are a few resources to help you learn more about designing, building, and using Local Zones:

Local Zones – Start here!
Local Zones FAQ – The answers to all of your questions.
Local Zones Locations – See the full set of available and announced Local Zones.
Local Zones Features – See the services, instance types, and volume types that are available in each location.
Local Zone Location Request – Use this page to express interest in a new location.
Local Zone is Now Available in Los Angeles – This blog post steps through the entire process of getting started with a Local Zone.
Local Zones Resources – Even more links.

I am always interested in hearing about how our customers are making use of Local Zones. Leave me a comment or track me down online and let me know what you are working on!

— Jeff;

Amazon File Cache – A High Performance Cache On AWS For Your On-Premises File Systems

2022-09-30 Sébastien Stormacq

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/amazon-file-cache-a-high-performance-cache-on-aws-for-your-on-premises-file-systems/

I am pleased to announce today the availability of Amazon File Cache, a new high-speed cache service on AWS designed for processing file data stored in disparate locations—including on premises. File Cache accelerates and simplifies your most demanding cloud bursting and hybrid workflows by giving your applications access to files using a fast and familiar POSIX interface, no matter if the original files live on premises on any file system that can be accessed through NFS v3 or on Amazon Simple Storage Service (Amazon S3).

Imagine you have a large data set on on-premises storage infrastructure, and your end-of-month reporting typically takes two to three days to run. You want to move that occasional workload to the cloud to run it on larger machines with more CPU and memory to reduce the processing time. But you’re not ready to move the data set to the cloud yet.

Imagine another scenario where you have access to a large data set on Amazon Simple Storage Service (Amazon S3), spread across multiple Regions. Your application that wants to exploit this data set is coded for traditional (POSIX) file system access and uses command line tools like awk, sed, pipes, and so on. Your application requires file access with sub-millisecond latencies. You cannot update the source code to use the S3 API.

File Cache helps to address these use cases and many others, think about management and transformation of video files, AI/ML data sets, and so on. File Cache creates a file system–based cache in front of either NFS v3 file systems or S3 buckets in one or more Regions. It transparently loads file content and metadata (such as the file name, size, and permissions) from the origin and presents it to your applications as a traditional file system. File Cache automatically releases the less recently used cached files to ensure the most active files are available in the cache for your applications.

You can link up to eight NFS file systems or eight S3 buckets to a cache, and they will be exposed as a unified set of files and directories. You can access the cache from a variety of AWS compute services, such as virtual machines or containers. The connection between File Cache and your on-premises infrastructure uses your existing network connection, based on AWS Direct Connect and/or Site-to-Site VPN.

When using File Cache, your applications benefit from consistent, sub-millisecond latencies, up to hundreds of GB/s of throughput, and up to millions of operations per second. Just like with other storage services, such as Amazon Elastic Block Store (Amazon EBS), the performance depends on the size of the cache. The cache size can be expanded to petabyte scale, with a minimum size of 1.2 TiB.

Let’s See How It Works
To show you how it works, I create a file cache on top of two existing Amazon FSx for OpenZFS file systems. In a real-world scenario, it is likely you will create caches on top of on-premises file systems. I choose FSx for OpenZFS for the demo because I don’t have an on-premises data center at hand (I should maybe invest in seb-west-1). Both demo OpenZFS file systems are accessible from a private subnet in my AWS account. Finally, I access the cache from an EC2 Linux instance.

I open my browser and navigate to the AWS Management Console. I search for “Amazon FSx” in the console search bar and click on Caches in the left navigation menu. Alternatively, I go directly to the File Cache section of the console. To get started, I select Create cache.

I enter a Cache name for my cache (AWSNewsBlog for this demo) and a Cache storage capacity. The storage capacity is expressed in tebibytes. The minimum value is 1.2 TiB or increments of 2.4 TiB. Notice that the Throughput capacity increases as you choose large cache sizes.

I check and accept the default values provided for Networking and Encryption. For networking, I might select a VPC, subnet, and security group to associate with my cache network interface. It is recommended to deploy the cache in the same subnet as your compute service to minimize the latency when accessing files. For encryption, I might use an AWS KMS-managed key (the default) or select my own.

Then, I create Data Repository Association. This is the link between the cache and a data source. A data source might be an NFS file system or an S3 bucket or prefix. I might create up to eight data repository associations for one cache. All Data Repository Associations for a cache have the same type: they are all NFS v3 or all S3. If you need both, you can create two caches.

In this demo, I choose to link two OpenZFS file systems on my AWS account. You can link to any NFS v3 servers, including the ones you already have on premises. Cache path allows you to choose where the source file system will be mounted in the cache. The Data repository path is the URL to your NFS v3 or S3 data repository. The format is nfs://hostname/path or s3://bucketname/path.

The DNS server IP addresses allows File Cache to resolve the DNS name of your NFS server. This is useful when DNS resolution is private, like in my example. When you are associating NFS v3 servers deployed in a VPC, and when using the AWS-provided DNS server, the DNS server IP address of your VPC is the VPC Range + two. In my example, my VPC CIDR range is 172.31.0.0, hence the DNS server IP address is 172.31.0.2.

Do not forget to click on the Add button! Otherwise, your input is ignored. You can repeat the operation to add more data repositories.

File Cache - Create new Data Repository Association- dataset one

File Cache - Create new Data Repository Association- dataset two

Once I have entered my two data repositories, I select Next, and I review my choices. When I am ready, I select Create cache.

After a few minutes, the cache status becomes Available.

The last part is to mount the cache on the machine where my workload is deployed. File Cache uses Lustre behind the scene. I have to install the Lustre client for Linux first, as explained in our documentation. Once done, I select the Attach button on the console to receive the instructions to download and install the Lustre client and to mount the cache file system. To do so, I connect to an EC2 instance running in the same VPC. Then I type:

sudo mount -t lustre -o relatime,flock file_cache_dns_name@tcp:/mountname /mnt

This command mounts my cache with two options:

relatime – Maintains atime (inode access times) data, but not for each time that a file is accessed. With this option enabled, atime data is written to disk only if the file has been modified since the atime data was last updated (mtime) or if the file was last accessed more than a certain amount of time ago (one day by default). relatime is required for automatic cache eviction to work properly.
flock – Enables file locking for your cache. If you don’t want file locking enabled, use the mount command without flock.

Once mounted, processes running on my EC2 instance can access files in the cache as usual. As I defined at cache creation time, the first ZFS file system is available inside the cache at /dataset1, and the second ZFS file system is available as /dataset2.

$ echo "Hello File Cache World" > /mnt/zsf1/greetings

$ sudo mount -t lustre -o relatime,flock fc-0280000000001.fsx.us-east-2.aws.internal@tcp:/r3xxxxxx /mnt/cache

$ ls -al /mnt/cache
total 98
drwxr-xr-x 5 root root 33280 Sep 21 14:37 .
drwxr-xr-x 2 root root 33280 Sep 21 14:33 dataset1
drwxr-xr-x 2 root root 33280 Sep 21 14:37 dataset2

$ cat /mnt/cache/dataset1/greetings
Hello File Cache World

I can observe and measure the activity and the health of my caches using Amazon CloudWatch metrics and AWS CloudTrail log monitoring.

CloudWatch metrics for a File Cache resource are organized into three categories:

Front-end I/O metrics
Backend I/O metrics
Cache front-end utilization metrics

As usual, I can create dashboards or define alarms to be informed when metrics reach thresholds that I defined.

Things To Keep In Mind
There are a couple of key points to keep in mind when using or planning to use File Cache.

First, File Cache encrypts data at rest and supports encryption of data in transit. Your data is always encrypted at rest using keys managed in AWS Key Management Service (AWS KMS). You can use either service-owned keys or your own keys (customer-managed CMKs).

Second, File Cache provides two options for importing data from your data repositories to the cache: lazy load and preload. Lazy load imports data on demand if it’s not already cached, and preload imports data at user request before you start your workload. Lazy loading is the default. It makes sense for most workloads since it allows your workload to start without waiting for metadata and data to be imported to the cache. Pre loading is helpful when your access pattern is sensitive to first-byte latencies.

Pricing and Availability
There are no upfront or fixed-price costs when using File Cache. You are billed for the provisioned cache storage capacity and metadata storage capacity. The pricing page has the details. In addition to File Cache itself, you pay for S3 request costs, AWS Direct Connect charges, and the usual data transfer charges for inter-AZ, inter-Region, and internet egress traffic between File Cache and the data sources.

File Cache is available in US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), and Europe (London).

Now go build and create your first file cache today!

— seb

Amazon WorkSpaces Introduces Ubuntu Desktops

2022-09-29 Sébastien Stormacq

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/amazon-workspaces-introduces-ubuntu-desktops/

At AWS, we love to give our customers choices: the choice of infrastructure to deploy your workloads, to store your most important data, or the operating systems for your virtual desktops.

Many of you choose Amazon Workspaces to provision and distribute virtual desktops securely and at scale to your workforce. Our customers choose Workspaces when choosing a solution to enable secure remote workers or when they want to quickly provision desktop for contractors, just to name two use cases. When using Workspaces, you’ve had the choice between two operating systems: Microsoft Windows or Amazon Linux.

Starting today, you can also provision Ubuntu desktops for your developers, engineers, or data scientists. This allows virtual desktop users to have more choices and to embrace new categories of workloads. It also gives virtual desktop administrators a consistent experience whether you are managing Windows or Linux-based desktops for your workforce. You can now have one process and set of tools to manage the majority of your desktop operating systems.

According to the HackerEarth 2020 Developer Survey, Ubuntu is the most widely used operating system among professional developers (66 percent Ubuntu, 61 percent Windows, and 57 percent macOS). Many developers or DevOps engineers have a battery of scripts, tools, or libraries well tested on their Ubuntu desktops or laptops. Providing Ubuntu desktop on Workspaces gives developers and engineers a familiar and compatible environment allowing them to work from anywhere, with access to a wealth of open-source tools and libraries in cutting-edge fields like data science, AI/ML, cloud, and IoT. All Ubuntu WorkSpaces benefit from Ubuntu Pro, which includes support for expanded security patching for 10 years.

Having Ubuntu on Amazon Workspaces is important for you, but also for us, AWS, and for Canonical, the publisher of Ubuntu.

“
We’ve brought Ubuntu Desktop to WorkSpaces so developers can streamline the design, coding, pipelines, and deployment of Ubuntu-based workloads, whether instances or containers, all within the AWS environment,” said
Alex Gallagher, VP Cloud for
Canonical. “
Also, Ubuntu virtual desktops on WorkSpaces enable IT organizations to quickly and easily provision high-performance Ubuntu Desktop instances, delivered as a fully managed AWS service. In the face of constant and increasing pressure to support the security and productivity needs of hybrid workers, that’s a win for IT organizations and their end users.”

Why a Virtual Desktop for Developers And Engineers?
There are several benefits to providing virtual cloud desktops to your technical workforce.

First, the security: all your files are securely stored on cloud-based volumes within the security perimeter of your AWS account. Application assets such as source code or design documents are not locally stored on end-user machines, and no data is at risk in case of a laptop being lost or stolen.

Second, the ease of provisioning at scale. Providing new desktops, including with the latest generation of hardware, is a matter of minutes. Onboarding new team members or contractors is accelerated, and can even be automated.

Third, the reduction in costs by paying on demand, either monthly or hourly, just for the time the resources are used.

Fourth, end-users may access their desktop from anywhere. They can work with the same desktop from on premises, from home, or when traveling.

And fifth, virtual desktop administrators now have a common administration experience across Windows and two distributions of Linux (Ubuntu and Amazon Linux).

How to Get Started
For IT administrators, provisioning Ubuntu desktops is no different than provisioning Windows or Amazon Linux desktops. You may choose the hardware bundle, with various sizes of CPUs and memory, depending on the end-user needs. Workspaces are bound to an Active Directory. You may use your on-premises AD or a fully managed directory service in the cloud.

For this blog post, I choose to show you the end-user experience instead. As a developer or engineer, once my desktop is provisioned, I receive an email like this one:

I select the link and enter my password. Then, I download and install a client from https://clients.amazonworkspaces.com/.

For this demo, I am using the Windows Workspaces client. After downloading and installing it, I enter the registration code I received by email.

A few seconds later, I have access to my Ubuntu desktop.

Imagine I later travel to a family member’s place, and I have to access my work desktop. I can borrow a web browser and reconnect from the web client. Not surprisingly, I find my desktop in the exact state I left it—even the mouse pointer is in the same place. When closing a session, I am confident that I did not leave any confidential files behind me. All my files, messages, and applications are securely stored within my company AWS account.

Things You Need To Know
The service team is actively listening to your feedback and building new capabilities into Workspaces.

For this launch, we chose to use version 22.04 LTS (Jammy Jellyfish). Canonical has optimized the base image for cloud development use cases. We have preinstalled AWS CLI and SDK.

You may select from a selection of Value, Standard, Performance, Power, or Power Pro bundles. These bundles provide you with computer sizes starting at 1 vCPU and 2 GB RAM, up to 8 vCPU and 32 GB RAM.

There are two features of Workspaces that are not present today for the launch of Ubuntu Workspaces. They will be added in the very near future. First, you may connect today using the Windows or web-based Workspaces clients. Linux and macOS clients will be supported as a quick follow-up to this launch. Second, the GPU-based Graphics.g4dn, and GraphicsPro.g4dn bundles will be available in the coming weeks.

Availability and Pricing
Ubuntu Workspaces are available in all AWS Regions where Workspaces is available, except a Region in China: China (Ningxia). Again, we’re working hard to add support for China (Ningxia) as a quick follow-up after launch.

Workspace pricing is either per month, for users that primarily use Workspaces as their main desktop, or per hour for those in your workforce making occasional use of the workspace. Ubuntu Workspace prices start at $23 per month and per desktop or $0.19 per hour. Our pricing page has the details.

Now go build and start your first Ubuntu Workspaces today.

— seb

AWS IoT FleetWise Now Generally Available – Easily Collect Vehicle Data and Send to the Cloud

2022-09-27 Channy Yun

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/aws-iot-fleetwise-now-generally-available-easily-collect-vehicle-data-and-send-to-the-cloud/

Today we announce the general availability of AWS IoT FleetWise, a fully managed AWS service that makes it easier to collect, transform, and transfer vehicle data to the cloud. Last AWS re:Invent 2021, we previewed AWS IoT FleetWise, heard customer feedback, and improved features for various use cases of near-real-time vehicle data processing.

With AWS IoT FleetWise, automakers, fleet operators, and automotive suppliers can take the complex variability out of collecting data from vehicle fleets at scale. You can access standardized fleet-wide vehicle data and avoid developing custom data collection systems, or you can integrate AWS IoT FleetWise to enhance your existing systems. AWS IoT FleetWise enables intelligent data collection that sends the exact data you need from the vehicle to the cloud. You can use the data to analyze vehicle fleet health to more quickly identify potential maintenance issues or make in-vehicle infotainment systems smarter. Furthermore, you can use it to train machine learning (ML) models that improve autonomous driving and advanced driver assistance systems (ADAS).

For example, electric vehicle (EV) battery temperature is a critical metric that should be continuously analyzed for the entire vehicle fleet. In order to avoid costly continuous data ingestion, you may want to optimize the data collection by setting a threshold on EV battery temperature. The results of this analysis would be provided to the automaker’s quality engineering department, enabling fast assessment of the criticality and possible root causes of any issues identified at certain temperatures. Based on the root cause analysis, the automaker can then take short-term actions to support the driver affected by the issue, as well as midterm actions to improve vehicle quality.

How AWS IoT FleetWise Works
AWS IoT FleetWise provides a vehicle modeling framework that you can use to model your vehicle and its sensors and actuators in the cloud. To enable secure communication between your vehicle and the cloud, AWS IoT FleetWise also provides the AWS IoT FleetWise Edge Agent application that you can use to download and install in-vehicle electronic control units (ECUs) such as the gateway, in-vehicle infotainment controller, etc. You define data collection schemes in the cloud and deploy them to your vehicle.

The AWS IoT FleetWise Edge Agent running in your vehicle uses data collection schemes to control what data to collect and when to transfer it to the cloud. Data collected and ingested through AWS IoT FleetWise Edge Agent software goes directly into your Amazon Timestream table or Amazon Simple Storage Service (Amazon S3) repositories via AWS IoT Core.

AWS IoT FleetWise Features
To get started with AWS IoT FleetWise, you can register your account and configure the settings via the AWS console. AWS IoT FleetWise automatically registers your AWS account, IAM role, and Amazon Timestream resources.

The Edge Agent software is a C++ application distributed as source code and is available on GitHub to collect, decode, normalize, cache, and ingest vehicle data to AWS. It supports multiple deployment options, such as vehicle gateways, infotainment systems, telematics control units (TCUs), or aftermarket devices. When vehicles are connected to the cloud, the Edge Agent continually receives data collection schemes and collects, decodes, normalizes and ingests the transformed vehicle data to AWS.

Let’s see the benefits and features of AWS IoT FleetWise:

Signal catalog
A signal catalog contains a collection of vehicle signals. Signals are fundamental structures that you define to contain vehicle data and its metadata. A signal can be a sensor and its status, an attribute as static information of the manufacturer, a branch to represent a nested structure such as Vehicle.Powertrain.combustionEngine expression, or an actuator such as the state of a vehicle device. For example, you can create a sensor to receive in-vehicle temperature values and store its metadata, including a sensor name, a data type, and a unit.

Signals in a signal catalog can be used to model vehicles that use different protocols and data formats. For example, there are two cars made by different automakers: one uses the Controller Area Network (CAN) to transmit the in-vehicle temperature data and the other uses On-board Diagnostic (OBD) protocol.

You can define a sensor in the signal catalog to receive in-vehicle temperature values. This sensor can be used to represent the thermocouples in both cars, irrespective of how this temperature data is available within the vehicle networks. For more information, see Create and manage signal catalogs in the AWS documentation.

Vehicle models
Vehicle models are virtual declarative representations that standardize the format of your vehicles and define relationships between signals in the vehicles. Vehicle models enforce consistent information across multiple vehicles of the same type so that you can quickly configure and create a vehicle fleet. In each vehicle model, you can add signals, including attributes, branches (signal hierarchies), sensors, and actuators.

You can define condition-based schemes to control what data to collect, such as data in-vehicle temperature values that are greater than 40 degrees. You can also define time-based schemes to control how often to collect data. For more information, see Create and manage vehicle models in the AWS documentation.

When a decoder manifest is associated with a vehicle model, you can create a vehicle. Each vehicle corresponds to an AWS IoT thing. You can use an existing AWS IoT thing to create a vehicle or set AWS IoT FleetWise to automatically create an AWS IoT thing for your vehicle. For more information, see Provision vehicles in the AWS documentation. After you create vehicles, you can create campaigns for them.

Campaigns
A campaign gives the AWS IoT FleetWise Edge Agent instructions on how to select, collect, and transfer data to the cloud. You can make a campaign with vehicle attributes that you added when creating vehicles, and a data collection scheme. You can manually define the data collection scheme either condition-based logical expressions such as $variable.myVehicle.InVehicleTemperature > 40.0, or time-based data collection in milliseconds such as from 10000 – 60000 milliseconds. To learn more, see Create a campaign in the AWS documentation.

After you create and approve the campaign, AWS IoT FleetWise automatically deploys the campaign to the listed vehicles. The AWS IoT FleetWise Edge Agent software doesn’t start collecting data until a running campaign is deployed to the vehicle. If you want to pause collecting data from vehicles connected to the campaign, on the Campaign summary page, choose Suspend. To resume collecting data from vehicles connected to the campaign, choose Resume.

Demo – Visualizing Vehicle Data
Here is a demo that aims to show how AWS IoT FleetWise can make it easy to collect vehicle data and use it to build visualizing applications. In this demo, you can simulate two kinds of vehicles, an NXP GoldBox powered by an Automotive Grade Linux distribution that runs the AWS IoT FleetWise agent as an AWS IoT Greengrass component or a completely virtual vehicle implemented as an AWS Graviton ARM-based Amazon EC2 instance. To learn more, see the getting started guide and source code in the GitHub repository.

The vehicle in CARLA Simulator can self-drive or be driven with a game steering wheel connected to your desktop. You can watch a live demo video.

Data is collected by AWS IoT FleetWise and stored in the Amazon Timestream table, and visualized on a Grafana Dashboard.

Customer and Partner Voices
During the preview period, we heard lots of feedback from our customers and partners in automotive industry such as automakers, fleet operators, and automotive suppliers.

For example, Hyundai Motor Group (HMG) is a global vehicle manufacturer that offers consumers a technology-rich lineup of cars, sport utility vehicles, and electrified vehicles. HMG has used AWS services, such as using Amazon SageMaker, to reduce its ML model training time for autonomous driving models.

Hae Young Kwon, vice president and head of the infotainment development group at HMG, said:

“As a leading global vehicle manufacturer, we have come to appreciate the breadth and depth of AWS services to help create new connected vehicle capabilities. With more data available from our expanding global fleet of connected cars, we look forward to leveraging AWS IoT FleetWise to discover how we can build more personalized ownership experiences for our customers.”

LG CNS is a global IT service provider and AWS Premier Consulting Partner that is transforming smart transportation services by building an advanced transportation system that is convenient and safe by maximizing the operational efficiency of multiple modes of transport, including buses, subways, taxis, railways, and airplanes.

Jae Seung Lee, vice president at LG CNS, said:

“At LG CNS, we are committed to advancing the technology that is powering the future of transportation. By using AWS IoT FleetWise, we are creating a new data platform that allows us to ingest, analyze, and simulate vehicle conditions in real-time. With these advanced insights, our customers can gain a better understanding of their vehicles and, as a result, improve decision-making about their fleets.”

Bridgestone is a global leader in tires and rubber building on its expertise to provide solutions for safe and sustainable mobility. Bridgestone has worked with AWS for several years to develop a system that delivers insights derived from the interaction between a tire and a vehicle using advanced machine learning capabilities on Amazon SageMaker.

Brian Goldstine, president of mobility solutions and fleet management at Bridgestone Americas Inc. said:

“Bridgestone has been working with AWS to transform the digital services we provide to our automotive manufacturer, fleet, and retail customers. We look forward to exploring how AWS IoT FleetWise will make it easier for our customers to collect detailed tire data, which can provide new insights for their products and applications.”

Renesas Electronics Corporation is a global leader in microcontrollers, analog, power, and system on chips (SoC) products. Renesas launched cellular-to-cloud IoT development platforms and its cloud development kits to run on AWS IoT Core and FreeRTOS.

Yusuke Kawasaki, director at Renesas Electronics Corporation, said:

“The volume of connected vehicle data is forecast to increase dramatically over the next few years, driven by new and evolving customer expectations. As a result, Renesas is focused on addressing the needs of automotive engineers facing increasing system complexity. Incorporating AWS IoT FleetWise into our vehicle gateway solution will enable our customers to enjoy our market-ready approach for large-scale data collection and accelerate their cloud development strategy. We look forward to further collaborating with AWS to provide a better and simpler development environment for our customers.”

By working with AWS IoT FleetWise Partners, you can take advantage of solutions to streamline your IoT projects, reduce the risk of your efforts, and accelerate time to value. To learn more how AWS accelerates the automotive industry’s digital transformation, see AWS for Automotive.

Now Available
AWS IoT FleetWise is now generally available in the US East (N. Virginia) and Europe (Frankfurt) Regions. You pay for the vehicles you have created and messages per vehicle per month. Additional services used alongside AWS IoT FleetWise, such as AWS IoT Core and Amazon Timestream, are billed separately. For more detail, see the AWS IoT FleetWise pricing page.

To learn more, see the AWS IoT FleetWise resources page including documentations, videos, and blog posts. Please send feedback to AWS re:Post for AWS IoT FleetWise or through your usual AWS support contacts.

– Channy

AWS Week In Review — September 26, 2022

2022-09-27 Sébastien Stormacq

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/aws-week-in-review-september-26-2022/

It looks like my travel schedule is coupled with this Week In Review series of blog posts. This week, I am traveling to Fort-de-France in the French Caribbean islands to meet our customers and partners. I enjoy the travel time when I am offline. It gives me the opportunity to reflect on the past or plan for the future.

Last Week’s Launches
Here are some of the launches that caught my eye last week:

Amazon SageMaker Autopilot – has added a new Ensemble training mode powered by AutoGluon that is 8X faster than the current Hyper parameter Optimization Mode and supports a wide range of algorithms, including LightGBM, CatBoost, XGBoost, Random Forest, Extra Trees, linear models, and neural networks based on PyTorch and FastAI.

AWS Outposts and Amazon EKS – You can now deploy both the worker nodes and the Kubernetes control plane on an Outposts rack. This allows you to maximize your application availability in case of temporary network disconnection on premises. The Kubernetes control plane continues to manage the worker nodes, and no pod eviction happens when on-premises network connectivity is reestablished.

Amazon Corretto 19 – Corretto is a no-cost, multiplatform, production-ready distribution of OpenJDK. Corretto is distributed by Amazon under an open source license. This version supports the latest OpenJDK feature release and is available on Linux, Windows, and macOS. You can download Corretto 19 from our downloads page.

Amazon CloudWatch Evidently – Evidently is a fully-managed service that makes it easier to introduce experiments and launches in your application code. Evidently adds support for Client Side Evaluations (CSE) for AWS Lambda, powered by AWS AppConfig. Evidently CSE allows application developers to generate feature evaluations in single-digit milliseconds from within their own Lambda functions. Check the client-side evaluation documentation to learn more.

Amazon S3 on AWS Outposts – S3 on Outposts now supports object versioning. Versioning helps you to locally preserve, retrieve, and restore each version of every object stored in your buckets. Versioning objects makes it easier to recover from both unintended user actions and application failures.

Amazon Polly – Amazon Polly is a service that turns text into lifelike speech. This week, we announced the general availability of Hiujin, Amazon Polly’s first Cantonese-speaking neural text-to-speech (NTTS) voice. With this launch, the Amazon Polly portfolio now includes 96 voices across 34 languages and language variants.

X in Y – We launched existing AWS services in additional Regions:

Other AWS News
Introducing the Smart City Competency program – The AWS Smart City Competency provides best-in-class partner recommendations to our customers and the broader market. With the AWS Smart City Competency, you can quickly and confidently identify AWS Partners to help you address Smart City focused challenges.

An update to IAM role trust policy behavior – This is potentially a breaking change. AWS Identity and Access Management (IAM) is changing an aspect of how role trust policy evaluation behaves when a role assumes itself. Previously, roles implicitly trusted themselves. AWS is changing role assumption behavior to always require self-referential role trust policy grants. This change improves consistency and visibility with regard to role behavior and privileges. This blog post shares the details and explains how to evaluate if your roles are impacted by this change and what to modify. According to our data, only 0.0001 percent of roles are impacted. We notified by email the account owners.

Amazon Music Unifies Music Queuing – The Amazon Music team published a blog post to explain how they created a unified music queue across devices. They used AWS AppSync and AWS Amplify to build a robust solution that scales to millions of music lovers.

Upcoming AWS Events
Check your calendar and sign up for an AWS event in your Region and language:

AWS re:Invent – Learn the latest from AWS and get energized by the community present in Las Vegas, Nevada. Registrations are open for re:Invent 2022 which will be held from Monday, November 28 to Friday, December 2.

AWS Summits – Come together to connect, collaborate, and learn about AWS. Registration is open for the following in-person AWS Summits: Bogotá (October 4), and Singapore (October 6).

Natural Language Processing (NLP) Summit – The AWS NLP Summit 2022 will host over 25 sessions focusing on the latest trends, hottest research, and innovative applications leveraging NLP capabilities on AWS. It is happening at our UK headquarters in London, October 5–6, and you can register now.

AWS Innovate for every app – This regional online conference is designed to inspire and educate executives and IT professionals about AWS. It offers dozens of technical sessions in eight languages (English, Spanish, French, German, Italian, Japanese, Korean, and Indonesian). Register today: Americas, September 28; Europe, Middle-East, and Africa, October 6; Asia Pacific & Japan, October 20.

AWS Community Days – AWS Community Day events are community-led conferences to share and learn with one another. In September, the AWS community in the US will run events in Arlington, Virginia (September 30). In Europe, Community Day events will be held in October. Join us in Amersfoort, Netherlands (October 3), Warsaw, Poland (October 14), and Dresden, Germany (October 19).

AWS Tour du Cloud – The AWS Team in France has prepared a roadshow to meet customers and partners with a one-day free conference in seven cities across the country (Aix en Provence, Lille, Toulouse, Bordeaux, Strasbourg, Nantes, and Lyon), and in Fort-de-France, Martinique.

AWS Fest – This third-party event will feature AWS influencers, community heroes, industry leaders, and AWS customers, all sharing AWS optimization secrets (this week on Wednesday, September). You can register for AWS Fest here.

Stay Informed
That is my selection for this week! To better keep up with all of this news, please check out the following resources:

What’s New with AWS – All AWS announcements. Add the RSS feed to your news reader.
AWS on Air – Join us every Friday on Twitch, Twitter, and LinkedIn.
The Official AWS Podcast – Listen each week for updates on the latest AWS news and deep dives into exciting use cases. There are also official AWS podcasts in your local languages. Check the ones in Spanish, French, Italian, and German.
AWS News Blog – This blog.

— seb
This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Noise

Tag Archives: launch

AWS Application Migration Service Major Updates – New Migration Servers Grouping, Updated Launch, and Post-Launch Template

New – Announcing Amazon EFS Elastic Throughput

New – Fully Managed Blue/Green Deployments in Amazon Aurora and Amazon RDS

New for AWS Backup – Protect and Restore Your CloudFormation Stacks

Amazon CloudWatch Internet Monitor Preview – End-to-End Visibility into Internet Performance for your Applications

New for Amazon Transcribe – Real-Time Analytics During Live Calls

Automated in-AWS Failback for AWS Elastic Disaster Recovery

New – Amazon ECS Service Connect Enabling Easy Communication Between Microservices

Now Open the 30th AWS Region – Asia Pacific (Hyderabad) Region in India

Introducing our final AWS Heroes of the year – November 2022

Ed Miller – San Jose, USA

Jones Zachariah Noel N – Karnataka, India

Luciano Mammino – Dublin, Ireland

Madhu Kumar – Budapest, Hungary

Paweł Zubkiewicz – Wroclaw, Poland

Rossana Suarez – Resistencia, Argentina

TaeSeong Park – Seoul, Korea

Learn More

A New AWS Region Opens in Switzerland

AWS Week in Review – November 7, 2022

Introducing Amazon Neptune Serverless – A Fully Managed Graph Database that Adjusts Capacity for Your Workloads

AWS Batch for Amazon Elastic Kubernetes Service

Amazon EC2 Trn1 Instances for High-Performance Model Training are Now Available

AWS Local Zones Expansion: Taipei and Delhi

Amazon File Cache – A High Performance Cache On AWS For Your On-Premises File Systems

Amazon WorkSpaces Introduces Ubuntu Desktops

AWS IoT FleetWise Now Generally Available – Easily Collect Vehicle Data and Send to the Cloud

AWS Week In Review — September 26, 2022

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