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In the ever-evolving realm of cloud computing, organizations of all sizes prioritize cost optimization and resource efficiency. As enterprises scale their cloud infrastructures, managing a multitude of Amazon Elastic Compute Cloud (Amazon EC2) instances becomes increasingly complex. While these instances provide exceptional flexibility and scalability, ensuring efficient and cost-effective resource utilization can be challenging.

This is where AWS Compute Optimizer comes into play. AWS Compute Optimizer is a robust service that delivers tailored recommendations to optimize your Amazon EC2 instances. It helps identify appropriate instance types, minimize underutilized resources, and enhance overall performance. In this article, we will delve into AWS Compute Optimizer and demonstrate how to automate the application of its recommendations, leading to substantial cost savings and improved resource efficiency.

What Is AWS Compute Optimizer?

AWS Compute Optimizer, a service from Amazon Web Services (AWS), assesses the configuration and utilization metrics of your AWS resources to provide insightful recommendations. This service offers a suite of APIs and an intuitive console interface to help customers reduce costs and elevate workload performance by suggesting the most suitable AWS resources for their needs.

It analyzes historical utilization metrics to pinpoint optimal configurations across various AWS services, including Amazon EC2 instance types, Amazon Elastic Block Store (EBS) volume setups, task sizes for Amazon Elastic Container Service (ECS) on AWS Fargate, AWS Lambda function memory sizes, and licensing recommendations for commercial software running on Amazon EC2.

How to Automate the Implementation of AWS Compute Optimizer Recommendations?

While AWS Compute Optimizer provides rightsizing recommendations for various AWS services, this discussion will center on Amazon EC2 instances.

To facilitate the implementation of AWS Compute Optimizer recommendations, we can leverage a streamlined solution (see the architecture below). This solution is centered around an AWS Step Function, which efficiently orchestrates the optimization process. It also utilizes Amazon EventBridge, AWS Lambda, Amazon Simple Notification Service (SNS), and Amazon API Gateway to implement the recommendations seamlessly.

Figure 1. The diagram illustrates how to automate the implementation of AWS Compute Optimizer Recommendations.

Here’s how it operates:

• Customizable Schedule: Amazon EventBridge Scheduler initiates the execution of an AWS Step Function on a bi-weekly basis (default schedule).
• Data Collection: The AWS Step Function calls the AWS Compute Optimizer API to fetch the latest recommendations for Amazon EC2 instances. A template for AWS resource-specific actions to process is generated.
• Parallel Processing: Each resource recommendation is processed concurrently by the solution to ascertain if it pertains to an overprovisioned resource and if it complies with the customer’s defined risk profile.
• Optional User Approval: If enabled, the solution sends a request for approval via SNS and API Gateway prior to making any alterations. This ensures that customers maintain final control over resource modifications.
• Maintenance Window: The solution waits until the next maintenance window to implement any changes, allowing for controlled and scheduled updates that minimize disruption.
• Rollback: If any errors occur during the resource update process, the solution automatically reverts to the original configuration and notifies the customer about the issue.

Benefits

• Continuous Cost Optimization: AWS Compute Optimizer consistently offers recommendations to ensure your cloud resources are appropriately sized. Automating the implementation of these recommendations allows customers to achieve ongoing cost optimization, reducing unnecessary expenditures as workloads and resources evolve.
• Resource Efficiency: Guarantees that your resources are optimized for your workloads, leading to enhanced performance and efficiency.
• Reduced Manual Work: Manual resizing of instances is labor-intensive and prone to errors. This automated solution reduces the need for manual intervention and mitigates the risk of human errors.
• Risk Mitigation: Risk profiles enable customers to establish thresholds based on their risk tolerance, ensuring that only business-approved recommendations are executed.
• Scheduled Changes: Defined maintenance windows specify when changes can be made, minimizing disruptions to customer operations.
• Rollback Mechanism: Should any issues arise during the resource update process, the solution includes a rollback mechanism to revert resources to their original configuration.
• Email Approvals: Customers can opt to enforce email approval before executing changes, adding an extra layer of control.

With AWS Compute Optimizer and this automation solution, you can easily optimize your Amazon EC2 instances, cut costs, and enhance resource efficiency, ultimately maximizing the value of your cloud resources. Automating AWS Compute Optimizer recommendations simplifies the process and ensures that your infrastructure remains cost-effective and optimized. If you’re curious about the digital nomad lifestyle, check out this insightful article on what it means to be a digital nomad.

Getting Started with the Solution Deployment

Now that we have a clear understanding of AWS Compute Optimizer and how to automate the process, let’s put everything into action. To implement this solution, follow these steps:

1. Enable AWS Compute Optimizer:
   If you haven’t done so already, activate AWS Compute Optimizer on the AWS account where you wish to automate recommendations:
   1. Navigate to AWS Compute Optimizer in the AWS Management Console and click on “Get started.”

   Figure 2. The image demonstrates how to activate AWS Compute Optimizer.

   2. Validate and update the settings, then click on “Opt-in.”

   Figure 3. The image shows the steps for opting in on AWS Compute Optimizer.

2. Deploy the Solution:
   After enabling AWS Compute Optimizer, deploy the automation solution:
   1. Go to AWS CloudFormation in the AWS Management Console.
   2. Select “Create stack.”

   Figure 4. The image illustrates how to create a new AWS CloudFormation Stack.

   3. Choose “Upload a template file” and upload the template found in the GitHub repository.
   4. Define a name for the stack, which will prefix all created resources.
   5. Update the parameters as follows:
      • ArchitecturalChange: Specifies if the automation will consider recommendations that involve changes to the type of processor.
      • ConcurrencyLimit: Sets the cap for the number of concurrently executed Lambda functions.
      • Email: Indicates the email for notifications regarding change approvals and rollbacks.
      • EmailApproval: Determines if the automation will send an approval email prior to implementing changes.
      • ExcludeTag: Identifies the tag used to exempt resources from resizing.
      • MaintenanceWindowDay: Specifies the day when the automation will apply changes.
      • MaintenanceWindowTime: Sets the time for executing changes.
      • RiskProfile: Defines the maximum risk tolerance for recommendations, ensuring that high-risk suggestions are not acted upon.
   6. The solution creates IAM resources, so it is crucial to follow these steps diligently to ensure successful deployment. For more guidance, this video resource on AWS setup is excellent.

Conclusion

By leveraging AWS Compute Optimizer and automating its recommendations, organizations can streamline resource management, enhance performance, and achieve substantial cost savings. As cybersecurity continues to be a critical area of focus, it’s worth noting insights from this report on data breaches, which emphasizes the human element in security.