Configure and right-size compute resources

Configuring and right-sizing compute resources is essential for matching your workload’s performance requirements. This practice helps prevent under-utilization of resources, which can waste costs, as well as over-utilization, which can lead to performance degradation. Proper selection ensures optimal performance efficiency in the AWS cloud environment.

Best Practices

Implement Auto Scaling

• Utilize Auto Scaling to adjust the number of compute instances based on demand, ensuring your resources match workload fluctuations.
• Configure scaling policies that reflect your application’s usage patterns, such as CPU or memory utilization thresholds.
• Continuously monitor performance and refine your scaling policies for optimal efficiency.

Use Compute Resource Monitoring Tools

• Leverage AWS CloudWatch or third-party monitoring tools to track resource utilization and performance metrics.
• Analyze metrics regularly to identify underutilized or overutilized resources and make adjustments.
• Establish alerts for performance anomalies to proactively manage compute resources.

Select Appropriate Instance Types

• Evaluate various instance types (e.g., general-purpose, compute-optimized, memory-optimized) based on your workload’s specific requirements.
• Use AWS Instance Scheduler to regularly review and optimize instance configurations to align with workload needs.
• Test and benchmark different instance types to determine which provides the best performance for your applications.

Utilize Spot Instances for Cost Efficiency

• Consider using Amazon EC2 Spot Instances for workloads that are flexible or can tolerate interruptions, significantly reducing costs.
• Implement fallback strategies to ensure workload continuity if Spot Instances become unavailable.
• Regularly assess the cost savings versus performance impacts when using Spot Instances.

Review and Refine Resource Allocations Periodically

• Conduct regular reviews of your compute resource allocations to ensure they are still aligned with current workload requirements.
• Adjust configurations, sizes, and quantities of resources based on the latest performance metrics and application growth.
• Implement a process for continuous improvement that incorporates feedback and new performance data into resource planning.

Questions to ask your team

• How do you determine the performance requirements of your workload?
• What metrics do you track to assess compute resource utilization?
• Have you established a process for regularly reviewing and adjusting your compute resource configurations?
• How often do you analyze the performance of different compute resource types for your application components?
• Do you utilize automated tools to help with right-sizing your compute resources?
• What methods do you use to test the impact of different configurations on performance?
• Are you taking advantage of features such as Auto Scaling to optimize resource utilization?
• How do you ensure that your compute resources match the traffic patterns and usage of your application?

Who should be doing this?

Cloud Architect

• Assess workload performance requirements and usage patterns.
• Select appropriate compute resources (e.g., EC2, Lambda) based on application needs.
• Design scalable architectures that utilize various compute choices effectively.
• Implement monitoring tools to track performance and resource utilization.
• Make recommendations for resource adjustments based on monitoring data.

DevOps Engineer

• Automate the deployment and configuration of compute resources.
• Implement CI/CD pipelines that adapt to performance requirements.
• Continuously monitor application performance and resource usage.
• Optimize configurations for cost-effectiveness and performance efficiencies.
• Collaborate with the Cloud Architect to align compute resources with workload needs.

Performance Analyst

• Analyze workload performance metrics and usage patterns.
• Provide insights on potential performance bottlenecks related to compute resources.
• Conduct benchmarking tests to evaluate performance of different compute options.
• Recommend strategies for optimizing compute resource usage.
• Report on performance efficiency improvements and resource utilization trends.

What evidence shows this is happening in your organization?

• Compute Resource Right-Sizing Checklist: A detailed checklist that guides teams through the process of evaluating and right-sizing compute resources based on workload performance requirements, ensuring resources are neither under- nor over-utilized.
• Compute Selection Strategy Document: A comprehensive strategy document that outlines best practices for selecting compute resources tailored to specific application designs and usage patterns, with examples relevant to different workload types.
• Performance Efficiency Dashboard: An interactive dashboard that monitors the performance metrics of compute resources in real-time, aiding in the assessment of whether the current resources are appropriately sized and configured.
• Right-Sizing Playbook: A playbook that provides step-by-step guidance on how to analyze workload performance and configure compute resources accordingly, including case studies and success stories from the organization.
• Resource Configuration Policy: A policy statement that establishes guidelines for configuring compute resources, focusing on maintaining optimal performance efficiency and documenting the rationale behind resource decisions.

Cloud Services

AWS

• AWS Compute Optimizer: Analyzes your utilization metrics and provides recommendations for optimal instance types and sizes based on your workload needs.
• Amazon EC2 Auto Scaling: Automatically adjusts the number of EC2 instances in response to changing demand to ensure optimal resource utilization.
• AWS Lambda: Allows you to run code in response to events and automatically manages the compute resources for you, ensuring efficient compute usage.

Azure

• Azure Advisor: Provides personalized best practices and recommendations to optimize your Azure resources for performance and cost.
• Azure Autoscale: Automatically scales your applications to meet demand, maintaining performance and resource efficiency without manual intervention.
• Azure Functions: Enables serverless computing, automatically scaling the resources based on demand and optimizing performance.

Google Cloud Platform

• Google Cloud Recommender: Analyzes resource utilization patterns and provides recommendations for rightsizing compute instances based on performance requirements.
• Google Cloud Autoscaler: Automatically scales instance groups based on load, ensuring that you have the right amount of resources for your workload.
• Cloud Functions: Runs your code in a serverless environment, dynamically allocating resources based on demand and efficiency.

Question: How do you select and use compute resources in your workload?
Pillar: Performance Efficiency (Code: PERF)