AWS Compute Optimizer is a service that analyzes your AWS resource configurations and utilization metrics to provide recommendations for optimizing compute resources. It uses machine learning to help you identify optimal AWS resources for your workloads, potentially reducing costs and improving per…AWS Compute Optimizer is a service that analyzes your AWS resource configurations and utilization metrics to provide recommendations for optimizing compute resources. It uses machine learning to help you identify optimal AWS resources for your workloads, potentially reducing costs and improving performance.
Compute Optimizer evaluates several resource types including EC2 instances, EC2 Auto Scaling groups, EBS volumes, and Lambda functions. The service collects and analyzes CloudWatch metrics over a period of time to understand your workload patterns and resource utilization.
For EC2 instances, Compute Optimizer examines CPU utilization, memory utilization (when CloudWatch agent is installed), network throughput, and disk I/O. Based on this analysis, it recommends instance types that better match your workload requirements. Recommendations are classified as under-provisioned, over-provisioned, or optimized.
The service provides three types of recommendations: current generation instance type suggestions, projected utilization metrics after implementing recommendations, and estimated monthly savings or performance improvement opportunities.
To use Compute Optimizer effectively, you should enable it at the organization or account level. The service requires at least 30 consecutive hours of metric data to generate recommendations, though 14 days of data produces more accurate suggestions.
Compute Optimizer integrates with AWS Organizations, allowing centralized management of recommendations across multiple accounts. You can export recommendations to S3 buckets for further analysis or integration with other tools.
For enhanced recommendations, you can enable the paid feature that extends the lookback period up to three months, providing more accurate recommendations for workloads with variable patterns.
Key benefits include cost reduction through right-sizing, improved application performance by identifying under-provisioned resources, and data-driven decision making for capacity planning. SysOps Administrators should regularly review Compute Optimizer findings as part of their cost optimization strategy and use these insights when planning infrastructure changes or responding to performance issues.
AWS Compute Optimizer is a service that analyzes the configuration and utilization metrics of your AWS resources to recommend optimal AWS resources for your workloads. It uses machine learning to analyze historical utilization data and provide recommendations that can help you reduce costs and improve performance.
Why is AWS Compute Optimizer Important?
• Cost Reduction: Identifies over-provisioned resources where you're paying for capacity you don't need • Performance Improvement: Detects under-provisioned resources that may be causing performance bottlenecks • Right-sizing: Provides specific instance type recommendations based on actual usage patterns • Automated Analysis: Eliminates manual effort in analyzing CloudWatch metrics across multiple resources • Data-Driven Decisions: Uses up to 14 days of metrics (or 93 days with enhanced infrastructure metrics) for accurate recommendations
Step 1: Opt-in to the Service You must opt-in to Compute Optimizer at the account level or organization level through AWS Organizations.
Step 2: Data Collection Once enabled, Compute Optimizer begins collecting utilization metrics from Amazon CloudWatch. It requires at least 30 consecutive hours of metrics data to generate recommendations.
Step 3: Analysis The service uses machine learning algorithms to analyze CPU utilization, memory utilization, network throughput, and storage metrics.
Step 4: Recommendations Based on the analysis, Compute Optimizer categorizes resources as: • Under-provisioned: Resource capacity is insufficient for the workload • Over-provisioned: Resource capacity exceeds workload requirements • Optimized: Resource is well-matched to workload requirements • Not optimized: Resource can be optimized
Key Features to Remember
• Enhanced Infrastructure Metrics: Paid feature that extends the look-back period from 14 days to 93 days for more accurate recommendations • Savings Opportunities: Displays estimated monthly savings for each recommendation • Risk Assessment: Shows performance risk ratings (very low, low, medium, high) for each recommendation • Export Recommendations: Can export recommendations to Amazon S3 for further analysis • Cross-account Support: Works with AWS Organizations for organization-wide optimization
Integration with Other AWS Services
• Amazon CloudWatch: Source of utilization metrics • AWS Organizations: For managing recommendations across multiple accounts • Amazon S3: For exporting recommendation reports • AWS Cost Explorer: Complementary service for cost analysis
Exam Tips: Answering Questions on AWS Compute Optimizer
Tip 1: Know the Prerequisites Remember that Compute Optimizer requires at least 30 consecutive hours of CloudWatch metrics before generating recommendations. Questions may test this minimum requirement.
Tip 2: Understand Resource Types Be familiar with which resources are supported. If a question asks about optimizing RDS instances or S3 buckets, Compute Optimizer is NOT the answer.
Tip 3: Differentiate from Similar Services • Compute Optimizer = Right-sizing recommendations using ML • AWS Cost Explorer = Cost analysis and forecasting • Trusted Advisor = Broader best practice recommendations across multiple categories • AWS Budgets = Budget alerts and tracking
Tip 4: Remember the Opt-in Requirement Compute Optimizer is not enabled by default. You must explicitly opt-in at the account or organization level.
Tip 5: Enhanced Infrastructure Metrics When questions mention needing longer historical data analysis (beyond 14 days), Enhanced Infrastructure Metrics is the feature that extends the look-back period to 93 days.
Tip 6: Scenario-Based Questions When you see scenarios involving: • EC2 instances running at low CPU utilization = Compute Optimizer for right-sizing • Lambda functions with over-provisioned memory = Compute Optimizer • EBS volumes with unused provisioned IOPS = Compute Optimizer
Tip 7: Cost and Performance Balance Compute Optimizer recommendations balance both cost AND performance. It won't recommend changes that significantly degrade performance just to save costs.
Tip 8: Memory Metrics for EC2 Remember that memory utilization requires the CloudWatch agent to be installed on EC2 instances. Native CloudWatch metrics don't include memory utilization.
Common Exam Scenarios
• A company wants to reduce EC2 costs by identifying oversized instances = Use AWS Compute Optimizer • Need recommendations for Lambda function memory settings = Use AWS Compute Optimizer • Want to analyze 3 months of historical data for better recommendations = Enable Enhanced Infrastructure Metrics • Need organization-wide compute optimization = Enable Compute Optimizer with AWS Organizations