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 organizations identify the most cost-effective and performance-efficient resource configurations fo…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 organizations identify the most cost-effective and performance-efficient resource configurations for their workloads.
For Solutions Architects dealing with organizational complexity, Compute Optimizer addresses several key challenges:
**Supported Resources:**
- Amazon EC2 instances
- Amazon EBS volumes
- AWS Lambda functions
- Amazon ECS services on Fargate
- Auto Scaling groups
**How It Works:**
Compute Optimizer collects resource utilization data over a period of time (up to 93 days with enhanced infrastructure metrics) and analyzes patterns using machine learning algorithms. It then compares current configurations against optimal settings based on CPU, memory, network, and storage metrics.
**Key Benefits for Complex Organizations:**
1. **Cost Optimization:** Identifies over-provisioned resources where you can downsize to reduce costs while maintaining performance requirements.
2. **Performance Improvement:** Detects under-provisioned resources that may be causing performance bottlenecks and recommends appropriate upgrades.
3. **Cross-Account Visibility:** When integrated with AWS Organizations, it provides recommendations across multiple accounts, essential for enterprise environments with complex organizational structures.
4. **Automated Analysis:** Eliminates manual effort in analyzing workload patterns and determining optimal configurations.
**Integration with AWS Organizations:**
Compute Optimizer can be enabled at the organization level, allowing centralized visibility into optimization opportunities across all member accounts. This is particularly valuable for organizations managing hundreds of accounts with diverse workloads.
**Recommendation Categories:**
- Over-provisioned (potential cost savings)
- Under-provisioned (potential performance gains)
- Optimized (current configuration is appropriate)
The service provides estimated monthly savings and performance risk ratings, enabling architects to make informed decisions when designing and maintaining solutions across complex organizational structures.
AWS Compute Optimizer - Complete Guide for AWS Solutions Architect Professional
What is AWS Compute Optimizer?
AWS Compute Optimizer is a machine learning-powered service that analyzes your AWS resource configurations and utilization metrics to provide recommendations for optimal AWS resource types and sizes. It helps you identify the most cost-effective and performance-efficient compute resources for your workloads.
Why is AWS Compute Optimizer Important?
• Cost Optimization: Identifies over-provisioned resources that are wasting money • Performance Improvement: Detects under-provisioned resources that may cause performance bottlenecks • Right-sizing: Provides specific recommendations for instance types, EBS volumes, and Lambda functions • Data-driven Decisions: Uses up to 14 days of historical CloudWatch metrics for analysis • Multi-account Support: Works across AWS Organizations for enterprise-wide optimization
Supported Resources
• Amazon EC2 instances • Amazon EC2 Auto Scaling groups • Amazon EBS volumes • AWS Lambda functions • Amazon ECS services on AWS Fargate
How AWS Compute Optimizer Works
1. Data Collection: Compute Optimizer collects resource utilization data from Amazon CloudWatch 2. Analysis: Machine learning algorithms analyze CPU, memory, network, and storage metrics 3. Recommendation Generation: The service generates recommendations based on workload patterns 4. Risk Assessment: Each recommendation includes a risk level (Very Low, Low, Medium, High) 5. Projected Metrics: Shows expected CPU and memory utilization after implementing recommendations
Key Features
• Enhanced Infrastructure Metrics: Paid feature that extends the lookback period to 93 days and provides memory utilization metrics (requires CloudWatch agent) • Savings Opportunities: Displays potential monthly savings for each recommendation • Export Recommendations: Export findings to Amazon S3 for further analysis • Integration with AWS Organizations: Delegate administration and view recommendations across all member accounts
Recommendation Types
• Under-provisioned: Resources with insufficient capacity for the workload • Over-provisioned: Resources with excess capacity beyond workload requirements • Optimized: Resources that are already well-matched to their workloads • None: Insufficient data to make a recommendation
Prerequisites and Requirements
• Resources must be running for at least 30 consecutive hours • EC2 instances must have CloudWatch detailed monitoring enabled for accurate recommendations • Memory metrics require CloudWatch agent installation • IAM permissions must allow Compute Optimizer to access CloudWatch metrics
Exam Tips: Answering Questions on AWS Compute Optimizer
1. Cost Optimization Scenarios: When a question asks about identifying underutilized or over-provisioned EC2 instances across an organization, Compute Optimizer is typically the answer.
2. Right-sizing Questions: If the scenario involves determining the correct instance type or size based on actual usage patterns, think Compute Optimizer.
3. Multi-account Management: For questions about optimizing compute resources across multiple AWS accounts in an organization, remember that Compute Optimizer integrates with AWS Organizations.
4. Distinguish from Other Services: • AWS Cost Explorer: Shows cost and usage data, has basic right-sizing recommendations • AWS Trusted Advisor: Provides broader best practice checks across multiple categories • AWS Compute Optimizer: Focuses specifically on compute optimization with ML-powered analysis
5. Memory Utilization: Questions mentioning memory-based recommendations should include CloudWatch agent as a prerequisite since EC2 memory metrics are not available by default.
6. Lambda Optimization: Compute Optimizer can recommend optimal memory configurations for Lambda functions based on invocation patterns.
7. EBS Volume Recommendations: Remember that Compute Optimizer can recommend changes to EBS volume types (gp2 to gp3) and IOPS configurations.
8. Look for Keywords: • "Analyze utilization patterns"• "Right-size instances"• "Optimize compute costs"• "Machine learning-based recommendations"• "Historical metrics analysis" 9. Integration Points: Understand that Compute Optimizer works with CloudWatch for metrics, S3 for exports, and Organizations for multi-account scenarios.
10. Timeframe Considerations: Standard analysis uses 14 days of data; Enhanced Infrastructure Metrics extends this to 93 days for more accurate recommendations for variable workloads.