Leveraging AWS Compute Optimizer for Continuous Right-Sizing and Cost Reduction

Leveraging AWS Compute Optimizer for Continuous Right-Sizing and Cost Reduction

In the dynamic environment of Amazon Web Services (AWS), ensuring that compute resources are perfectly matched to workload requirements is a constant challenge. Over-provisioning leads to unnecessary cloud spending, while under-provisioning degrades application performance and user experience. The practice of right-sizing is essential for maximizing efficiency and minimizing operational costs.

AWS Compute Optimizer (ACO) is a crucial, machine learning-powered service that addresses this challenge head-on. It analyzes utilization metrics and resource configuration data over time to provide actionable recommendations for ideal resource sizing. This guide explores how to effectively utilize ACO's insights for continuous optimization across Amazon EC2 instances, EBS volumes, and AWS Lambda functions, transforming sporadic reviews into a proactive cost management strategy.

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Understanding AWS Compute Optimizer

AWS Compute Optimizer provides recommendations by analyzing the historical utilization metrics of your resources, typically collected over the last 14 days. It utilizes sophisticated machine learning algorithms trained on AWS usage patterns to identify resources that are either over-provisioned (leading to waste) or under-provisioned (leading to performance bottlenecks).

ACO evaluates several factors, including CPU utilization, memory usage (if the appropriate CloudWatch agent is installed), network throughput, and disk I/O, generating recommendations that prioritize both cost efficiency and performance.

Key Metrics Provided by ACO

1. Optimization Findings: Categorization of the resource (e.g., Over-provisioned, Under-provisioned, Optimized).
2. Estimated Monthly Savings: Projected cost reduction if the recommendation is implemented.
3. Performance Risk: A low, medium, or high assessment indicating the likelihood that implementing the recommendation will negatively impact the workload's performance.
4. Recommended Options: Specific alternative resource configurations (e.g., instance types, memory settings, EBS volume specs).

Note: Compute Optimizer is a cost-free service. It generates value solely by identifying potential savings and performance improvements in other paid services.

Right-Sizing Amazon EC2 Instances

EC2 instances are often the largest single driver of cloud compute costs. ACO provides tailored recommendations for stand-alone instances and instances within Auto Scaling Groups (ASGs).

Identifying Over- and Under-Provisioned Instances

ACO categorizes EC2 instances based on its analysis:

• Over-provisioned: Instances exhibiting consistently low CPU utilization and memory usage. ACO suggests moving to a smaller, less expensive instance type (e.g., switching from m5.large to t3.medium).
• Under-provisioned: Instances showing consistently high utilization, often peaking at 100% CPU. ACO suggests migrating to a larger, more robust instance type to improve application responsiveness (e.g., switching from c5.xlarge to c5.2xlarge).

Implementing EC2 Right-Sizing Recommendations

Implementing a change requires careful planning, especially for production workloads. The process for changing an instance type typically involves stopping, modifying, and restarting the instance.

Example: Modifying an Over-provisioned Instance via CLI

If ACO recommends downsizing an instance from m5.large to t3.large, the steps are:

1. Stop the Instance:
   bash aws ec2 stop-instances --instance-ids i-1234567890abcdef0
2. Modify the Instance Type:
   bash aws ec2 modify-instance-attribute --instance-id i-1234567890abcdef0 --instance-type "{'Value': 't3.large'}"
3. Start the Instance:
   bash aws ec2 start-instances --instance-ids i-1234567890abcdef0

Best Practice: Always perform these changes during low-traffic periods and monitor the instance metrics closely (CPU, latency, application logs) for 24-48 hours after implementation to ensure the new size can handle peak load without performance degradation.

Optimizing Amazon EBS Volumes

Compute Optimizer extends its recommendations to Elastic Block Store (EBS) volumes attached to EC2 instances. Optimization here focuses on maximizing performance per dollar by suggesting modern volume types and adjusting IOPS/throughput settings.

Migration Recommendations

The most common and significant optimization is migrating older volume types, especially gp2, to the newer gp3 volume type.

Volume Type	Advantage
gp2	Performance tied directly to size; often expensive for high IOPS.
gp3	Base performance is decoupled from size; allows tuning IOPS/Throughput independently, often leading to substantial cost reduction.

ACO will recommend specific changes to IOPS and throughput values based on observed usage patterns. For example, if a gp2 volume is costing $10/month and ACO finds that a smaller gp3 volume with custom IOPS can achieve the same performance for $6/month, it will generate that finding.

Actionable Step: Modifying a Volume

EBS volume modifications can usually be performed while the volume is in use (unlike changing an EC2 instance type), though performance impact should be considered.

# Example: Migrating volume to gp3 and setting specific IOPS/throughput
aws ec2 modify-volume \
    --volume-id vol-fedcba9876543210 \
    --volume-type gp3 \
    --iops 3000 \
    --throughput 125

Right-Sizing AWS Lambda Functions

For serverless workloads, Compute Optimizer provides critical insights into AWS Lambda functions. In Lambda, the memory setting dictates the amount of vCPU allocated to the function. Right-sizing Lambda is primarily about finding the lowest memory configuration that still meets performance targets.

The Memory/CPU Tradeoff

ACO analyzes the function's invocation duration across various memory configurations. A function might be allocated 1024MB of memory but only truly require 512MB to complete in the same acceptable timeframe. Reducing the memory reduces the cost per invocation, as billing is calculated based on (Memory Allocated * Duration).

ACO provides recommendations that often involve decreasing the memory setting, leading to cost savings without significant (or any) increase in latency.

Implementing Lambda Function Optimization

Lambda optimization is straightforward, usually requiring a simple update to the function's configuration.

Example: Updating Lambda Memory Configuration

If ACO recommends moving a function from 2048MB to 1024MB:

aws lambda update-function-configuration \
    --function-name MyOptimizedFunction \
    --memory-size 1024

Integrating Continuous Optimization into Your Workflow

Right-sizing should not be a one-time audit but a continuous discipline. Compute Optimizer facilitates this through its API and integration with AWS Organizations.

1. Centralized Management

If using AWS Organizations, designate a delegated administrator account for Compute Optimizer. This allows ACO to provide consolidated recommendations across all accounts, offering a holistic view of potential enterprise-wide savings.

2. Automation and Notification

Use the Compute Optimizer API and integrate it with AWS CloudWatch Events or Lambda to create automated workflows:

• Scheduled Reporting: Set up a daily or weekly trigger that pulls the latest high-priority recommendations (e.g., those with the highest estimated savings).
• Alerting: Trigger alerts via SNS when ACO identifies resources with specific findings (e.g., under-provisioned instances with high performance risk).
• Semi-Automated Implementation: For low-risk, high-savings recommendations (like EBS gp3 migration), use Lambda functions to automatically generate change requests or even apply the change directly after passing a necessary governance threshold.

# Conceptual Python snippet using boto3 to retrieve recommendations
import boto3

aco_client = boto3.client('compute-optimizer')

response = aco_client.get_ec2_instance_recommendations(
    filters=[
        {'name': 'finding', 'values': ['Overprovisioned']}
    ]
)
# Process and act on the recommended options...

Best Practices for Using Compute Optimizer

Area	Best Practice
Monitoring Period	Ensure resources have been running under typical load for at least 14 days before trusting recommendations.
Performance Testing	After implementing a downsizing recommendation, always run load tests to ensure the application maintains required SLOs (Service Level Objectives).
Specialized Workloads	Be cautious with stateful applications, databases, or third-party license servers that might require specific instance types or minimum resources, even if ACO recommends a smaller size.
Memory Metric	For EC2, install the CloudWatch agent to collect detailed memory usage data. Without this, ACO's right-sizing recommendations rely primarily on CPU and network, which may be incomplete.
Continuous Review	Treat the ACO dashboard as a living document. Workloads change constantly, requiring regular reassessment of resource sizing.

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Conclusion

AWS Compute Optimizer transforms the complex task of resource optimization into an actionable, data-driven process. By systematically applying the recommendations for EC2 instances, EBS volumes, and Lambda functions—and integrating the service into a continuous review cycle—organizations can achieve significant and sustainable cost reductions while simultaneously ensuring that their applications maintain optimal performance. Leveraging ACO is a fundamental step toward mastering cloud financial management (FinOps) and operational excellence on AWS.