Part 7: Advanced Performance Tuning with AWS Compute Optimizer

In the previous parts of this series, we covered how to use AWS Budgets and Alerts to manage and control costs effectively. Now, we will shift our focus back to performance optimization, specifically using AWS Compute Optimizer—a powerful service that helps you make informed decisions about EC2 instance types, AWS Lambda functions, Auto Scaling groups, and EBS volumes by analyzing historical usage patterns.

In this part, we will explore how to use AWS Compute Optimizer to further enhance the performance and cost-efficiency of your infrastructure. By analyzing your resource usage data, Compute Optimizer provides actionable insights and recommendations to right-size your resources, ensuring that you are neither over-provisioning nor under-provisioning your cloud environment.

Introduction to AWS Compute Optimizer

What Is AWS Compute Optimizer?

AWS Compute Optimizer is a service that uses machine learning to analyze your resource usage and recommend the most cost-effective and performance-optimized instance types for your workloads. It supports a range of AWS resources, including:

• EC2 instances
• EBS volumes
• Auto Scaling groups
• AWS Lambda functions

Compute Optimizer’s recommendations are based on historical performance data, such as CPU, memory, disk I/O, and network utilization. It identifies opportunities to downsize or right-size instances, optimize Auto Scaling groups, and improve EBS volume performance, helping you achieve the right balance between cost and performance.

Why Use AWS Compute Optimizer?

1. Right-Sizing Resources: Avoid paying for over-provisioned resources by identifying underutilized instances, volumes, and functions. Compute Optimizer helps ensure that you’re only paying for the resources you need.
2. Performance Optimization: Prevent performance bottlenecks by identifying under-provisioned resources. Compute Optimizer recommends larger instance types or volumes when workloads require more capacity.
3. Actionable Insights: Compute Optimizer provides recommendations that you can implement with a few clicks, making it easy to act on the suggestions.
4. Cost Savings: By downsizing over-provisioned resources, you can significantly reduce AWS costs while maintaining optimal performance.
5. Automated Analysis: Compute Optimizer continuously monitors your workloads, ensuring that you always have up-to-date recommendations as your usage patterns change over time.

How AWS Compute Optimizer Works

AWS Compute Optimizer continuously analyzes your AWS resources using machine learning models trained on AWS’s performance data. It evaluates factors such as CPU utilization, memory consumption, network traffic, and storage I/O to make recommendations for resource optimization.

Supported Resources:

• EC2 Instances: Compute Optimizer analyzes your EC2 instances and recommends the most appropriate instance type based on your usage patterns.
• Auto Scaling Groups: It evaluates Auto Scaling group configurations and recommends whether to change instance types or the group’s desired capacity.
• EBS Volumes: For EBS volumes, Compute Optimizer analyzes IOPS and throughput to suggest resizing or switching volume types.
• AWS Lambda Functions: Compute Optimizer evaluates Lambda functions’ memory usage and duration to recommend optimal memory configurations.

Accessing AWS Compute Optimizer

AWS Compute Optimizer is accessible through the AWS Management Console, the AWS CLI, and the AWS SDK. The service is enabled by default for EC2 instances, Auto Scaling groups, and Lambda functions, but you will need to enable it manually for EBS volumes.

Enabling AWS Compute Optimizer via the AWS Console:

1. Go to the AWS Compute Optimizer dashboard in the AWS Management Console.
2. Click Get Started to enable the service.
3. Once enabled, Compute Optimizer will begin analyzing your resources. It may take a few hours or days for recommendations to become available, depending on your resource usage.

Enabling Compute Optimizer via the AWS CLI:

aws compute-optimizer update-enrollment-status --status Active

Viewing Recommendations via the AWS Console:

1. Go to the Compute Optimizer dashboard.
2. Click on the Recommendations tab to view recommendations for your EC2 instances, Auto Scaling groups, EBS volumes, and Lambda functions.

Understanding Compute Optimizer Recommendations

Compute Optimizer provides three categories of recommendations for each resource type:

1. Under-provisioned: The resource does not have enough capacity to handle the workload, and performance might be degraded. A larger instance type or more IOPS for volumes is recommended.
2. Over-provisioned: The resource has more capacity than needed, leading to unnecessary costs. A smaller instance type or fewer IOPS for volumes is recommended.
3. Optimized: The resource is right-sized and performing efficiently. No action is necessary.

For each recommendation, Compute Optimizer provides detailed insights into why the recommendation was made and the potential impact on performance and cost if the recommendation is implemented.

Example Recommendations:

• EC2 Instances: If an EC2 instance is consistently running at 30% CPU utilization, Compute Optimizer may recommend downsizing from an m5.large to an m5.medium instance.
• EBS Volumes: If an EBS volume’s IOPS usage is consistently low, Compute Optimizer may suggest switching from a Provisioned IOPS SSD (io1) volume to a General Purpose SSD (gp2) volume to save on costs.

Working with EC2 Instance Recommendations

One of the most common use cases for Compute Optimizer is right-sizing EC2 instances. Here’s how you can use Compute Optimizer to analyze your EC2 instance performance and implement recommendations for improving cost-efficiency and performance.

Step 1: Viewing EC2 Recommendations

1. In the AWS Compute Optimizer dashboard, navigate to the EC2 Recommendations tab.
2. You will see a list of EC2 instances along with their current instance type, utilization metrics (CPU, memory, disk, and network), and Compute Optimizer’s recommendation.

Example:
You may see a recommendation like the following:

• Current Instance: m5.large (2 vCPUs, 8 GiB memory)
• Recommended Instance: m5.medium (2 vCPUs, 4 GiB memory)
• Potential Savings: $100 per month

Compute Optimizer will also show the expected performance impact, which is typically classified as None, Low, or High. In this case, if the workload is consistently using less memory, Compute Optimizer will recommend switching to a smaller instance, with no expected performance impact.

Step 2: Implementing EC2 Instance Recommendations

1. Select the EC2 instance you want to modify based on the recommendation.
2. Stop the instance from the EC2 dashboard.
3. Modify the instance type by selecting Actions > Instance settings > Change instance type.
4. Choose the recommended instance type and apply the changes.
5. Start the instance again.

Example Command Using AWS CLI to Modify an EC2 Instance Type:

aws ec2 modify-instance-attribute --instance-id i-1234567890abcdef0 --instance-type "{\"Value\": \"m5.medium\"}"

Step 3: Monitor Performance After Changes

After making the change, monitor the instance’s performance using AWS CloudWatch to ensure that the recommended instance type is performing as expected. You can monitor key metrics such as CPU utilization, memory usage (if the CloudWatch Agent is installed), and network traffic.

Optimizing Auto Scaling Groups with Compute Optimizer

Auto Scaling groups dynamically adjust the number of running instances based on your application’s needs. Compute Optimizer analyzes the performance of the instances within your Auto Scaling group and provides recommendations to optimize both the instance type and the group’s scaling settings.

Step 1: Viewing Auto Scaling Group Recommendations

1. In the Compute Optimizer dashboard, navigate to the Auto Scaling Groups tab.
2. You will see a list of Auto Scaling groups along with recommendations for the group’s instance type and scaling settings.

Example:
Compute Optimizer may recommend switching from m5.large to m5.medium instance types for all instances within the group if the CPU and memory usage are consistently low.

Step 2: Implementing Auto Scaling Recommendations

1. In the EC2 Auto Scaling console, select the Auto Scaling group you want to modify.
2. Update the Launch Configuration or Launch Template to use the recommended instance type.
3. Optionally, adjust the group’s desired capacity or scaling policies based on Compute Optimizer’s recommendation.

Example Command Using AWS CLI to Modify an Auto Scaling Group:

aws autoscaling update-auto-scaling-group --auto-scaling-group-name my-asg --launch-configuration-name my-launch-config --min-size 1 --max-size 5

Step 3: Monitor Performance

As with individual EC2 instances, monitor the performance of the Auto Scaling group after implementing the changes. Use CloudWatch to track metrics such as CPU utilization and instance count to ensure that the group is scaling appropriately based on demand.

Optimizing EBS Volumes with Compute Optimizer

Compute Optimizer also provides recommendations for EBS volumes, helping you optimize storage performance and costs. The service analyzes the IOPS and throughput patterns of your EBS volumes and recommends resizing or switching volume types.

Step 1: Viewing EBS Recommendations

1. In the Compute Optimizer dashboard, navigate to the EBS Volume Recommendations tab.
2. You will see a list of EBS volumes along with their current configuration (e.g., volume type, size, IOPS) and Compute Optimizer’s recommendation.

Example:
Compute Optimizer may recommend switching from a Provisioned IOPS SSD (io1) volume to a General Purpose SSD (gp2) volume if the IOPS usage is consistently low.

Step 2: Implementing EBS Recommendations

1. Stop the instance using the EBS volume (if applicable).
2. Modify the EBS volume type by selecting Actions > Modify Volume in the EBS console.
3. Choose the recommended volume type or size and apply the changes.

Example Command Using AWS CLI to Modify an EBS Volume:

aws ec2 modify-volume --volume-id vol-1234567890abcdef0 --volume-type gp2

Optimizing AWS Lambda Functions with Compute Optimizer

For serverless applications using AWS Lambda, Compute Optimizer can recommend memory configurations based on the function’s execution time and memory usage.

Step 1: Viewing Lambda Recommendations

1. In the Compute Optimizer dashboard, navigate to the Lambda Function Recommendations tab.
2. You will see a list of Lambda functions along with recommendations to adjust the memory configuration.

Example:
Compute Optimizer may recommend reducing the memory allocation for a Lambda function if the current memory usage is far below the allocated limit.

Step 2: Implementing Lambda Recommendations

1. In the AWS Lambda console, select the function you want to modify.
2. Update the memory allocation by selecting Configuration > Memory (MB).
3. Set the memory to the recommended value and save the changes.

Example Command Using AWS CLI to Modify Lambda Memory Allocation:

aws lambda update-function-configuration --function-name my-function --memory-size 512

Best Practices for Using AWS Compute Optimizer

1. Regularly Review Recommendations: AWS Compute Optimizer continuously analyzes your resources, so it’s important to regularly review the recommendations to keep your infrastructure optimized.
2. Implement Recommendations in Test Environments First: Before making changes in production, test the recommendations in a staging or test environment to ensure that they meet your performance requirements.
3. Monitor Performance After Changes: Always monitor performance using CloudWatch after applying Compute Optimizer’s recommendations to ensure that your resources are performing as expected.
4. Combine with AWS Cost Explorer: Use AWS Cost Explorer in conjunction with Compute Optimizer to track the cost savings from implementing optimization recommendations.

Conclusion

In this part of the series, we explored how AWS Compute Optimizer can help you optimize the performance and cost-efficiency of your EC2 instances, Auto Scaling groups, EBS volumes, and Lambda functions. By using machine learning to analyze historical usage patterns, Compute Optimizer provides actionable recommendations for right-sizing your resources, ensuring that you’re not over- or under-provisioning your cloud infrastructure.

In the next part of the series, we will dive into case studies of successful cost and performance optimization on AWS, where we’ll look at real-world examples of organizations that have effectively used the strategies and tools discussed throughout this series. Stay tuned!