Compute Power Optimization for AWS Developer Associate
Why Compute Power Optimization is Important
Compute power optimization is a critical skill for AWS developers because it ensures applications run efficiently while minimizing costs. Poorly optimized compute resources lead to wasted spending, degraded performance, and scalability issues. AWS charges based on resource consumption, making optimization essential for cost management and operational excellence.
What is Compute Power Optimization?
Compute power optimization refers to the process of selecting, configuring, and managing AWS compute resources to achieve the best balance between performance, cost, and availability. This includes choosing the right instance types, sizing resources appropriately, leveraging auto-scaling, and utilizing serverless options when suitable.
Key AWS Compute Services to Understand:
Amazon EC2: Virtual servers with various instance families (General Purpose, Compute Optimized, Memory Optimized, Storage Optimized, Accelerated Computing)
AWS Lambda: Serverless compute that scales automatically and charges per invocation and duration
Amazon ECS/EKS: Container orchestration services for running containerized applications
AWS Fargate: Serverless compute engine for containers
How Compute Power Optimization Works
1. Right-Sizing: Analyze CPU, memory, and network utilization to select appropriately sized instances. Use AWS Compute Optimizer and CloudWatch metrics to identify over-provisioned or under-provisioned resources.
2. Instance Type Selection: Match workload requirements to instance families. Use compute-optimized (C-series) for CPU-intensive tasks, memory-optimized (R-series) for in-memory databases, and GPU instances for machine learning workloads.
3. Auto Scaling: Configure Auto Scaling groups to dynamically adjust capacity based on demand. Use target tracking policies, step scaling, or scheduled scaling based on predictable patterns.
4. Spot Instances: Leverage Spot Instances for fault-tolerant, flexible workloads to achieve up to 90% cost savings compared to On-Demand pricing.
5. Lambda Optimization: Configure appropriate memory allocation (which also affects CPU), optimize cold starts by keeping functions warm, and use provisioned concurrency for latency-sensitive applications.
6. Reserved Capacity: Use Reserved Instances or Savings Plans for steady-state workloads to reduce costs by up to 72%.
Exam Tips: Answering Questions on Compute Power Optimization
Tip 1: When a question mentions unpredictable or variable workloads, think Auto Scaling and Lambda as primary solutions.
Tip 2: For cost optimization scenarios with flexible timing, Spot Instances are typically the correct answer. Remember they can be interrupted with a 2-minute warning.
Tip 3: If a question describes consistent, predictable workloads running 24/7, Reserved Instances or Savings Plans provide the best cost optimization.
Tip 4: Lambda questions often focus on memory configuration affecting performance, cold start mitigation, and timeout settings. Remember that increasing memory also increases CPU allocation.
Tip 5: Watch for questions about monitoring and analysis tools. AWS Compute Optimizer and CloudWatch are key services for identifying optimization opportunities.
Tip 6: Container-based questions may ask about Fargate versus EC2 launch types. Fargate eliminates server management overhead but costs more per compute unit.
Tip 7: For latency-sensitive Lambda functions, provisioned concurrency is the solution to eliminate cold starts.
Tip 8: Questions about batch processing or background jobs are often best suited for Spot Instances or Lambda depending on execution duration requirements.
Tip 9: Remember the 15-minute maximum execution time limit for Lambda. Long-running processes require EC2, ECS, or Step Functions for orchestration.
Tip 10: When questions mention burst performance, T-series instances with CPU credits are relevant. Understand baseline performance versus burst capacity.