Tag Archives: AWS Compute

Architecting for data residency with AWS Outposts rack and landing zone guardrails

Post Syndicated from Sheila Busser original https://aws.amazon.com/blogs/compute/architecting-for-data-residency-with-aws-outposts-rack-and-landing-zone-guardrails/

This blog post was written by Abeer Naffa’, Sr. Solutions Architect, Solutions Builder AWS, David Filiatrault, Principal Security Consultant, AWS and Jared Thompson, Hybrid Edge SA Specialist, AWS.

In this post, we will explore how organizations can use AWS Control Tower landing zone and AWS Organizations custom guardrails to enable compliance with data residency requirements on AWS Outposts rack. We will discuss how custom guardrails can be leveraged to limit the ability to store, process, and access data and remain isolated in specific geographic locations, how they can be used to enforce security and compliance controls, as well as, which prerequisites organizations should consider before implementing these guardrails.

Data residency is a critical consideration for organizations that collect and store sensitive information, such as Personal Identifiable Information (PII), financial, and healthcare data. With the rise of cloud computing and the global nature of the internet, it can be challenging for organizations to make sure that their data is being stored and processed in compliance with local laws and regulations.

One potential solution for addressing data residency challenges with AWS is to use Outposts rack, which allows organizations to run AWS infrastructure on premises and in their own data centers. This lets organizations store and process data in a location of their choosing. An Outpost is seamlessly connected to an AWS Region where it has access to the full suite of AWS services managed from a single plane of glass, the AWS Management Console or the AWS Command Line Interface (AWS CLI).  Outposts rack can be configured to utilize landing zone to further adhere to data residency requirements.

The landing zones are a set of tools and best practices that help organizations establish a secure and compliant multi-account structure within a cloud provider. A landing zone can also include Organizations to set policies – guardrails – at the root level, known as Service Control Policies (SCPs) across all member accounts. This can be configured to enforce certain data residency requirements.

When leveraging Outposts rack to meet data residency requirements, it is crucial to have control over the in-scope data movement from the Outposts. This can be accomplished by implementing landing zone best practices and the suggested guardrails. The main focus of this blog post is on the custom policies that restrict data snapshots, prohibit data creation within the Region, and limit data transfer to the Region.

Prerequisites

Landing zone best practices and custom guardrails can help when data needs to remain in a specific locality where the Outposts rack is also located.  This can be completed by defining and enforcing policies for data storage and usage within the landing zone organization that you set up. The following prerequisites should be considered before implementing the suggested guardrails:

1. AWS Outposts rack

AWS has installed your Outpost and handed off to you. An Outpost may comprise of one or more racks connected together at the site. This means that you can start using AWS services on the Outpost, and you can manage the Outposts rack using the same tools and interfaces that you use in AWS Regions.

2. Landing Zone Accelerator on AWS

We recommend using Landing Zone Accelerator on AWS (LZA) to deploy a landing zone for your organization. Make sure that the accelerator is configured for the appropriate Region and industry. To do this, you must meet the following prerequisites:

    • A clear understanding of your organization’s compliance requirements, including the specific Region and industry rules in which you operate.
    • Knowledge of the different LZAs available and their capabilities, such as the compliance frameworks with which you align.
    • Have the necessary permissions to deploy the LZAs and configure it for your organization’s specific requirements.

Note that LZAs are designed to help organizations quickly set up a secure, compliant multi-account environment. However, it’s not a one-size-fits-all solution, and you must align it with your organization’s specific requirements.

3. Set up the data residency guardrails

Using Organizations, you must make sure that the Outpost is ordered within a workload account in the landing zone.

Figure 1 Landing Zone Accelerator Outposts workload on AWS high level Architecture

Figure 1: Landing Zone Accelerator – Outposts workload on AWS high level Architecture

Utilizing Outposts rack for regulated components

When local regulations require regulated workloads to stay within a specific boundary, or when an AWS Region or AWS Local Zone isn’t available in your jurisdiction, you can still choose to host your regulated workloads on Outposts rack for a consistent cloud experience. When opting for Outposts rack, note that, as part of the shared responsibility model, customers are responsible for attesting to physical security, access controls, and compliance validation regarding the Outposts, as well as, environmental requirements for the facility, networking, and power. Utilizing Outposts rack requires that you procure and manage the data center within the city, state, province, or country boundary for your applications’ regulated components, as required by local regulations.

Procuring two or more racks in the diverse data centers can help with the high availability for your workloads. This is because it provides redundancy in case of a single rack or server failure. Additionally, having redundant network paths between Outposts rack and the parent Region can help make sure that your application remains connected and continue to operate even if one network path fails.

However, for regulated workloads with strict service level agreements (SLA), you may choose to spread Outposts racks across two or more isolated data centers within regulated boundaries. This helps make sure that your data remains within the designated geographical location and meets local data residency requirements.

In this post, we consider a scenario with one data center, but consider the specific requirements of your workloads and the regulations that apply to determine the most appropriate high availability configurations for your case.

Outposts rack workload data residency guardrails

Organizations provide central governance and management for multiple accounts. Central security administrators use SCPs with Organizations to establish controls to which all AWS Identity and Access Management (IAM) principals (users and roles) adhere.

Now, you can use SCPs to set permission guardrails.  A suggested preventative controls for data residency on Outposts rack that leverage the implementation of SCPs are shown as follows. SCPs enable you to set permission guardrails by defining the maximum available permissions for IAM entities in an account. If an SCP denies an action for an account, then none of the entities in the account can take that action, even if their IAM permissions let them. The guardrails set in SCPs apply to all IAM entities in the account, which include all users, roles, and the account root user.

Upon finalizing these prerequisites, you can create the guardrails for the Outposts Organization Unit (OU).

Note that while the following guidelines serve as helpful guardrails – SCPs – for data residency, you should consult internally with legal and security teams for specific organizational requirements.

 To exercise better control over workloads in the Outposts rack and prevent data transfer from Outposts to the Region or data storage outside the Outposts, consider implementing the following guardrails. Additionally, local regulations may dictate that you set up these additional guardrails.

  1. When your data residency requirements require restricting data transfer/saving to the Region, consider the following guardrails:

a. Deny copying data from Outposts to the Region for Amazon Elastic Compute Cloud (Amazon EC2), Amazon Relational Database Service (Amazon RDS), Amazon ElastiCache and data sync “DenyCopyToRegion”.

b. Deny Amazon Simple Storage Service (Amazon S3) put action to the Region “DenyPutObjectToRegionalBuckets”.

If your data residency requirements mandate restrictions on data storage in the Region,  consider implementing this guardrail to prevent  the use of S3 in the Region.

Note: You can use Amazon S3 for Outposts.

c. If your data residency requirements mandate restrictions on data storage in the Region, consider implementing “DenyDirectTransferToRegion” guardrail.

Out of Scope is metadata such as tags, or operational data such as KMS keys.

 
{
  "Version": "2012-10-17",
  "Statement": [
      {
      "Sid": "DenyCopyToRegion",
      "Action": [
        "ec2:ModifyImageAttribute",
        "ec2:CopyImage",  
        "ec2:CreateImage",
        "ec2:CreateInstanceExportTask",
        "ec2:ExportImage",
        "ec2:ImportImage",
        "ec2:ImportInstance",
        "ec2:ImportSnapshot",
        "ec2:ImportVolume",
        "rds:CreateDBSnapshot",
        "rds:CreateDBClusterSnapshot",
        "rds:ModifyDBSnapshotAttribute",
        "elasticache:CreateSnapshot",
        "elasticache:CopySnapshot",
        "datasync:Create*",
        "datasync:Update*"
      ],
      "Resource": "*",
      "Effect": "Deny"
    },
    {
      "Sid": "DenyDirectTransferToRegion",
      "Action": [
        "dynamodb:PutItem",
        "dynamodb:CreateTable",
        "ec2:CreateTrafficMirrorTarget",
        "ec2:CreateTrafficMirrorSession",
        "rds:CreateGlobalCluster",
        "es:Create*",
        "elasticfilesystem:C*",
        "elasticfilesystem:Put*",
        "storagegateway:Create*",
        "neptune-db:connect",
        "glue:CreateDevEndpoint",
        "glue:UpdateDevEndpoint",
        "datapipeline:CreatePipeline",
        "datapipeline:PutPipelineDefinition",
        "sagemaker:CreateAutoMLJob",
        "sagemaker:CreateData*",
        "sagemaker:CreateCode*",
        "sagemaker:CreateEndpoint",
        "sagemaker:CreateDomain",
        "sagemaker:CreateEdgePackagingJob",
        "sagemaker:CreateNotebookInstance",
        "sagemaker:CreateProcessingJob",
        "sagemaker:CreateModel*",
        "sagemaker:CreateTra*",
        "sagemaker:Update*",
        "redshift:CreateCluster*",
        "ses:Send*",
        "ses:Create*",
        "sqs:Create*",
        "sqs:Send*",
        "mq:Create*",
        "cloudfront:Create*",
        "cloudfront:Update*",
        "ecr:Put*",
        "ecr:Create*",
        "ecr:Upload*",
        "ram:AcceptResourceShareInvitation"
      ],
      "Resource": "*",
      "Effect": "Deny"
    },
    {
      "Sid": "DenyPutObjectToRegionalBuckets",
      "Action": [
        "s3:PutObject"
      ],
      "Resource": ["arn:aws:s3:::*"],
      "Effect": "Deny"
    }
  ]
}
  1. If your data residency requirements require limitations on data storage in the Region, consider implementing this guardrail “DenySnapshotsToRegion” and “DenySnapshotsNotOutposts” to restrict the use of snapshots in the Region.

a. Deny creating snapshots of your Outpost data in the Region “DenySnapshotsToRegion”

 Make sure to update the Outposts “<outpost_arn_pattern>”.

b. Deny copying or modifying Outposts Snapshots “DenySnapshotsNotOutposts”

Make sure to update the Outposts “<outpost_arn_pattern>”.

Note: “<outpost_arn_pattern>” default is arn:aws:outposts:*:*:outpost/*

{
  "Version": "2012-10-17",
  "Statement": [

    {
      "Sid": "DenySnapshotsToRegion",
      "Effect":"Deny",
      "Action":[
        "ec2:CreateSnapshot",
        "ec2:CreateSnapshots"
      ],
      "Resource":"arn:aws:ec2:*::snapshot/*",
      "Condition":{
         "ArnLike":{
            "ec2:SourceOutpostArn":"<outpost_arn_pattern>"
         },
         "Null":{
            "ec2:OutpostArn":"true"
         }
      }
    },
    {

      "Sid": "DenySnapshotsNotOutposts",          
      "Effect":"Deny",
      "Action":[
        "ec2:CopySnapshot",
        "ec2:ModifySnapshotAttribute"
      ],
      "Resource":"arn:aws:ec2:*::snapshot/*",
      "Condition":{
         "ArnLike":{
            "ec2:OutpostArn":"<outpost_arn_pattern>"
         }
      }
    }

  ]
}
  1. This guardrail helps to prevent the launch of Amazon EC2 instances or creation of network interfaces in non-Outposts subnets. It is advisable to keep data residency workloads within the Outposts rather than the Region to ensure better control over regulated workloads. This approach can help your organization achieve better control over data residency workloads and improve governance over your AWS Organization.

Make sure to update the Outposts subnets “<outpost_subnet_arns>”.

{
"Version": "2012-10-17",
  "Statement":[{
    "Sid": "DenyNotOutpostSubnet",
    "Effect":"Deny",
    "Action": [
      "ec2:RunInstances",
      "ec2:CreateNetworkInterface"
    ],
    "Resource": [
      "arn:aws:ec2:*:*:network-interface/*"
    ],
    "Condition": {
      "ForAllValues:ArnNotEquals": {
        "ec2:Subnet": ["<outpost_subnet_arns>"]
      }
    }
  }]
}

Additional considerations

When implementing data residency guardrails on Outposts rack, consider backup and disaster recovery strategies to make sure that your data is protected in the event of an outage or other unexpected events. This may include creating regular backups of your data, implementing disaster recovery plans and procedures, and using redundancy and failover systems to minimize the impact of any potential disruptions. Additionally, you should make sure that your backup and disaster recovery systems are compliant with any relevant data residency regulations and requirements. You should also test your backup and disaster recovery systems regularly to make sure that they are functioning as intended.

Additionally, the provided SCPs for Outposts rack in the above example do not block the “logs:PutLogEvents”. Therefore, even if you implemented data residency guardrails on Outpost, the application may log data to CloudWatch logs in the Region.

Highlights

By default, application-level logs on Outposts rack are not automatically sent to Amazon CloudWatch Logs in the Region. You can configure CloudWatch logs agent on Outposts rack to collect and send your application-level logs to CloudWatch logs.

logs: PutLogEvents does transmit data to the Region, but it is not blocked by the provided SCPs, as it’s expected that most use cases will still want to be able to use this logging API. However, if blocking is desired, then add the action to the first recommended guardrail. If you want specific roles to be allowed, then combine with the ArnNotLike condition example referenced in the previous highlight.

Conclusion

The combined use of Outposts rack and the suggested guardrails via AWS Organizations policies enables you to exercise better control over the movement of the data. By creating a landing zone for your organization, you can apply SCPs to your Outposts racks that will help make sure that your data remains within a specific geographic location, as required by the data residency regulations.

Note that, while custom guardrails can help you manage data residency on Outposts rack, it’s critical to thoroughly review your policies, procedures, and configurations to make sure that they are compliant with all relevant data residency regulations and requirements. Regularly testing and monitoring your systems can help make sure that your data is protected and your organization stays compliant.

References

Building Sustainable, Efficient, and Cost-Optimized Applications on AWS

Post Syndicated from Sheila Busser original https://aws.amazon.com/blogs/compute/building-sustainable-efficient-and-cost-optimized-applications-on-aws/

This blog post is written by Isha Dua Sr. Solutions Architect AWS, Ananth Kommuri Solutions Architect AWS, and Dr. Sam Mokhtari Sr. Sustainability Lead SA WA for AWS.

Today, more than ever, sustainability and cost-savings are top of mind for nearly every organization. Research has shown that AWS’ infrastructure is 3.6 times more energy efficient than the median of U.S. enterprise data centers and up to five times more energy efficient than the average in Europe. That said, simply migrating to AWS isn’t enough to meet the Environmental, Social, Governance (ESG) and Cloud Financial Management (CFM) goals that today’s customers are setting. In order to make conscious use of our planet’s resources, applications running on the cloud must be built with efficiency in mind.

That’s because cloud sustainability is a shared responsibility. At AWS, we’re responsible for optimizing the sustainability of the cloud – building efficient infrastructure, enough options to meet every customer’s needs, and the tools to manage it all effectively. As an AWS customer, you’re responsible for sustainability in the cloud – building workloads in a way that minimizes the total number of resource requirements and makes the most of what must be consumed.

Most AWS service charges are correlated with hardware usage, so reducing resource consumption also has the added benefit of reducing costs. In this blog post, we’ll highlight best practices for running efficient compute environments on AWS that maximize utilization and decrease waste, with both sustainability and cost-savings in mind.

First: Measure What Matters

Application optimization is a continuous process, but it has to start somewhere. The AWS Well Architected Framework Sustainability pillar includes an improvement process that helps customers map their journey and understand the impact of possible changes. There is a saying “you can’t improve what you don’t measure.”, which is why it’s important to define and regularly track metrics which are important to your business. Scope 2 Carbon emissions, such as those provided by the AWS Customer Carbon Footprint Tool, are one metric that many organizations use to benchmark their sustainability initiatives, but they shouldn’t be the only one.

Even after AWS meets our 2025 goal of powering our operations with 100% renewable energy, it’s still be important to maximize the utilization and minimize the consumption of the resources that you use. Just like installing solar panels on your house, it’s important to limit your total consumption to ensure you can be powered by that energy. That’s why many organizations use proxy metrics such as vCPU Hours, storage usage, and data transfer to evaluate their hardware consumption and measure improvements made to infrastructure over time.

In addition to these metrics, it’s helpful to baseline utilization against the value delivered to your end-users and customers. Tracking utilization alongside business metrics (orders shipped, page views, total API calls, etc) allows you to normalize resource consumption with the value delivered to your organization. It also provides a simple way to track progress towards your goals over time. For example, if the number of orders on your ecommerce site remained constant over the last month, but your AWS infrastructure usage decreased by 20%, you can attribute the efficiency gains to your optimization efforts, not changes in your customer behavior.

Utilize all of the available pricing models

Compute tasks are the foundation of many customers’ workloads, so it typically sees biggest benefit by optimization. Amazon EC2 provides resizable compute across a wide variety of compute instances, is well-suited to virtually every use case, is available via a number of highly flexible pricing options. One of the simplest changes you can make to decrease your costs on AWS is to review the purchase options for the compute and storage resources that you already use.

Amazon EC2 provides multiple purchasing options to enable you to optimize your costs based on your needs. Because every workload has different requirements, we recommend a combination of purchase options tailored for your specific workload needs. For steady-state workloads that can have a 1-3 year commitment, using Compute Savings Plans helps you save costs, move from one instance type to a newer, more energy-efficient alternative, or even between compute solutions (e.g., from EC2 instances to AWS Lambda functions, or AWS Fargate).

EC2 Spot instances are another great way to decrease cost and increase efficiency on AWS. Spot Instances make unused Amazon EC2 capacity available for customers at discounted prices. At AWS, one of our goals it to maximize utilization of our physical resources. By choosing EC2 Spot instances, you’re running on hardware that would otherwise be sitting idle in our datacenters. This increases the overall efficiency of the cloud, because more of our physical infrastructure is being used for meaningful work. Spot instances use market-based pricing that changes automatically based on supply and demand. This means that the hardware with the most spare capacity sees the highest discounts, sometimes up to XX% off on-demand prices, to encourage our customers to choose that configuration.

Savings Plans are ideal for predicable, steady-state work. On-demand is best suited for new, stateful, and spiky workloads which can’t be instance, location, or time flexible. Finally, Spot instances are a great way to supplement the other options for applications that are fault tolerant and flexible. AWS recommends using a mix of pricing models based on your workload needs and ability to be flexible.

By using these pricing models, you’re creating signals for your future compute needs, which helps AWS better forecast resource demands, manage capacity, and run our infrastructure in a more sustainable way.

Choose efficient, purpose-built processors whenever possible

Choosing the right processor for your application is as equally important consideration because under certain use cases a more powerful processor can allow for the same level of compute power with a smaller carbon footprint. AWS has the broadest choice of processors, such as Intel – Xeon scalable processors, AMD – AMD EPYC processors, GPU’s FPGAs, and Custom ASICs for Accelerated Computing.

AWS Graviton3, AWS’s latest and most power-efficient processor, delivers 3X better CPU performance per-watt than any other processor in AWS, provides up to 40% better price performance over comparable current generation x86-based instances for various workloads, and helps customers reduce their carbon footprint. Consider transitioning your workload to Graviton-based instances to improve the performance efficiency of your workload (see AWS Graviton Fast Start and AWS Graviton2 for ISVs). Note the considerations when transitioning workloads to AWS Graviton-based Amazon EC2 instances.

For machine learning (ML) workloads, use Amazon EC2 instances based on purpose-built Amazon Machine Learning (Amazon ML) chips, such as AWS Trainium, AWS Inferentia, and Amazon EC2 DL1.

Optimize for hardware utilization

The goal of efficient environments is to use only as many resources as required in order to meet your needs. Thankfully, this is made easier on the cloud because of the variety of instance choices, the ability to scale dynamically, and the wide array of tools to help track and optimize your cloud usage. At AWS, we offer a number of tools and services that can help you to optimize both the size of individual resources, as well as scale the total number of resources based on traffic and load.

Two of the most important tools to measure and track utilization are Amazon CloudWatch and the AWS Cost & Usage Report (CUR). With CloudWatch, you can get a unified view of your resource metrics and usage, then analyze the impact of user load on capacity utilization over time. The Cost & Usage Report (CUR) can help you understand which resources are contributing the most to your AWS usage, allowing you to fine-tune your efficiency and save on costs. CUR data is stored in S3, which allows you to query it with tools like Amazon Athena or generate custom reports in Amazon QuickSight or integrate with AWS Partner tools for better visibility and insights.

An example of a tool powered by CUR data is the AWS Cost Intelligence Dashboard. The Cost Intelligence Dashboard provides a detailed, granular, and recommendation-driven view of your AWS usage. With its prebuilt visualizations, it can help you identify which service and underlying resources are contributing the most towards your AWS usage, and see the potential savings you can realize by optimizing. It even provides right sizing recommendations and the appropriate EC2 instance family to help you optimize your resources.

Cost Intelligence Dashboard is also integrated with AWS Compute Optimizer, which makes instance type and size recommendations based on workload characteristics. For example, it can identify if the workload is CPU-intensive, if it exhibits a daily pattern, or if local storage is accessed frequently. Compute Optimizer then infers how the workload would have performed on various hardware platforms (for example, Amazon EC2 instance types) or using different configurations (for example, Amazon EBS volume IOPS settings, and AWS Lambda function memory sizes) to offer recommendations. For stable workloads, check AWS Compute Optimizer at regular intervals to identify right-sizing opportunities for instances. By right sizing with Compute Optimizer, you can increase resource utilization and reduce costs by up to 25%. Similarly, Lambda Power Tuning can help choose the memory allocated to Lambda functions is an optimization process that balances speed (duration) and cost while lowering your carbon emission in the process.

CloudWatch metrics are used to power EC2 Autoscaling, which can automatically choose the right instance to fit your needs with attribute-based instance selection and scale your entire instance fleet up and down based on demand in order to maintain high utilization. AWS Auto Scaling makes scaling simple with recommendations that let you optimize performance, costs, or balance between them. Configuring and testing workload elasticity will help save money, maintain performance benchmarks, and reduce the environmental impact of workloads. You can utilize the elasticity of the cloud to automatically increase the capacity during user load spikes, and then scale down when the load decreases. Amazon EC2 Auto Scaling allows your workload to automatically scale up and down based on demand. You can set up scheduled or dynamic scaling policies based on metrics such as average CPU utilization or average network in or out. Then, you can integrate AWS Instance Scheduler and Scheduled scaling for Amazon EC2 Auto Scaling to schedule shut downs and terminate resources that run only during business hours or on weekdays to further reduce your carbon footprint.

Design applications to minimize overhead and use fewer resources

Using the latest Amazon Machine Image (AMI) gives you updated operating systems, packages, libraries, and applications, which enable easier adoption as more efficient technologies become available. Up-to-date software includes features to measure the impact of your workload more accurately, as vendors deliver features to meet their own sustainability goals.

By reducing the amount of equipment that your company has on-premises and using managed services, you can help facilitate the move to a smaller, greener footprint. Instead of buying, storing, maintaining, disposing of, and replacing expensive equipment, businesses can purchase services as they need that are already optimized with a greener footprint. Managed services also shift responsibility for maintaining high average utilization and sustainability optimization of the deployed hardware to AWS. Using managed services will help distribute the sustainability impact of the service across all of the service tenants, thereby reducing your individual contribution. The following services help reduce your environmental impact because capacity management is automatically optimized.

 AWS  Managed Service   Recommendation for sustainability improvement

Amazon Aurora

You can use Amazon  Aurora Serverless to automatically start up, shut down, and scale capacity up or down based on your application’s needs.

Amazon Redshift

You can use Amazon Redshift Serverless to run and scale data warehouse capacity.

AWS Lambda

 You can Migrate AWS Lambda functions to Arm-based AWS Graviton2 processors.

Amazon ECS

 You can run Amazon ECS on AWS Fargate to avoid the undifferentiated heavy lifting by leveraging sustainability best practices AWS put in place for management of the control plane.

Amazon EMR

 You can use EMR Serverless to avoid over- or under-provisioning resources for your data processing jobs.

AWS Glue

 You can use Auto-scaling for AWS Glue to enable on-demand scaling up and scaling down of the computing resources.

 Centralized data centers consume a lot of energy, produce a lot of carbon emissions and cause significant electronic waste. While more data centers are moving towards green energy, an even more sustainable approach (alongside these so-called “green data centers”) is to actually cut unnecessary cloud traffic, central computation and storage as much as possible by shifting computation to the edge. Edge Computing stores and uses data locally, on or near the device it was created on. This reduces the amount of traffic sent to the cloud and, at scale, can limit the overall energy used and carbon emissions.

Use storage that best supports how your data is accessed and stored to minimize the resources provisioned while supporting your workload. Solid state devices (SSDs) are more energy intensive than magnetic drives and should be used only for active data use cases. You should look into using ephemeral storage whenever possible and categorize, centralize, deduplicate, and compress persistent storage.

AWS Outposts, AWS Local Zones and AWS Wavelength services deliver data processing, analysis, and storage close to your endpoints, allowing you to deploy APIs and tools to locations outside AWS data centers. Build high-performance applications that can process and store data close to where it’s generated, enabling ultra-low latency, intelligent, and real-time responsiveness. By processing data closer to the source, edge computing can reduce latency, which means that less energy is required to keep devices and applications running smoothly. Edge computing can help to reduce the carbon footprint of data centers by using renewable energy sources such as solar and wind power.

Conclusion

In this blog post, we discussed key methods and recommended actions you can take to optimize your AWS compute infrastructure for resource efficiency. Using the appropriate EC2 instance types with the right size, processor, instance storage and pricing model can enhance the sustainability of your applications. Use of AWS managed services, options for edge computing and continuously optimizing your resource usage can further improve the energy efficiency of your workloads. You can also analyze the changes in your emissions over time as you migrate workloads to AWS, re-architect applications, or deprecate unused resources using the Customer Carbon Footprint Tool.

Ready to get started? Check out the AWS Sustainability page to find out more about our commitment to sustainability and learn more about renewable energy usage, case studies on sustainability through the cloud, and more.