Scale your compute resources dynamically

The ability to dynamically scale compute resources is crucial for maintaining performance efficiency. This approach not only saves costs by preventing over-provisioning but also ensures that workloads run optimally under varying demand. Leveraging cloud elasticity allows for real-time adjustments that meet workload requirements without sacrificing performance.

Best Practices

Implement Auto Scaling

• Utilize AWS Auto Scaling to automatically adjust the number of compute resources based on demand. This ensures that you receive the necessary capacity during peak times and save costs during low demand periods.
• Configure scaling policies that react to specific CloudWatch metrics, such as CPU utilization or request count, to trigger scaling actions.
• Test and adjust your scaling policies regularly to optimize for performance efficiency, ensuring that your application can handle fluctuations in load without manual intervention.

Leverage Spot Instances

• Consider using EC2 Spot Instances to take advantage of unused EC2 capacity at reduced costs. This can be particularly useful for batch processing or flexible workloads.
• Implement a strategy to monitor and automatically replace Spot Instances with On-Demand Instances if they become unavailable, maintaining performance continuity.
• Evaluate your workload’s tolerance for interruptions to ensure that Spot Instances are a viable option without impacting your application performance.

Use Managed Services

• Where possible, utilize AWS managed services (e.g., AWS Lambda for serverless functions, Amazon ECS for container orchestration) to automatically handle resource scaling and maintenance.
• Managed services provide built-in elasticity and can automatically scale based on incoming requests or workload size, optimizing performance efficiently without manual management.
• Evaluate the trade-offs between managed services and traditional compute resources to align with your application design and usage patterns.

Monitor Performance Metrics

• Continuously monitor application performance and resource utilization through AWS CloudWatch, setting up alerts for unusual patterns that suggest over- or under-utilization.
• Regularly review CloudWatch Logs and Metrics to identify trends and adjust your compute resources proactively rather than reactively.
• Use AWS Cost Explorer to analyze your spending patterns alongside performance data, allowing for adjustments in resource allocation for better cost efficiency.

Questions to ask your team

• Have you implemented auto-scaling for your compute resources?
• How do you monitor the performance of your workload to determine when to scale?
• What metrics do you analyze to guide your scaling decisions?
• Do you have thresholds set for scaling in and scaling out?
• How quickly can your system respond to changes in demand?
• Have you tested your scaling policies under different load scenarios?

Who should be doing this?

Cloud Architect

• Design and implement scalable cloud architectures that leverage dynamic compute resources.
• Evaluate application design and usage patterns to determine optimal compute configurations.
• Select appropriate AWS compute services (e.g., EC2, Lambda, ECS) based on workload requirements.
• Monitor resource utilization and performance metrics to make informed adjustments.
• Ensure compliance with best practices for performance efficiency as per the AWS Well-Architected Framework.

DevOps Engineer

• Implement CI/CD pipelines that support dynamic scaling of compute resources.
• Develop automation scripts or use orchestration tools to provision and manage compute resources based on demand.
• Monitor system performance and apply scaling policies to adjust compute resources as needed in real-time.
• Collaborate with development teams to ensure applications are optimized for performance and scalability.

Product Owner

• Define workload requirements and success criteria for performance efficiency.
• Prioritize features and enhancements based on performance metrics and resource utilization.
• Coordinate with stakeholders to align expectations on application performance and resource management.
• Review and approve scaling strategies to ensure they meet business needs and enhance user experience.

What evidence shows this is happening in your organization?

• Dynamic Scaling Checklist: A comprehensive checklist to assess the current capability of the organization in dynamically scaling compute resources. Includes criteria for evaluating existing workloads and identifying opportunities for improvement.
• Cloud Compute Resources Scaling Policy: A detailed policy document that outlines the organization’s approach to scaling compute resources. It covers guidelines, roles, and responsibilities for monitoring performance and triggering scaling events.
• Performance Monitoring Dashboard: An interactive dashboard that displays real-time metrics, such as CPU usage, memory consumption, and load patterns, to help teams identify when to scale resources dynamically.
• Scaling Strategy Guide: A strategic guide that provides best practices for selecting compute resources based on workload patterns. It includes detailed analysis methods and scenarios for scaling up or down efficiently.
• Elasticity Implementation Playbook: A playbook that outlines step-by-step actions to implement elasticity in cloud environments. It includes configuration templates, AWS services usage, and examples of workloads benefitting from dynamic scaling.
• Performance Efficiency Model: A model illustrating how different compute choices can affect overall performance efficiency. It provides visual representations of workload scenarios and the potential impacts of scaling decisions.

Cloud Services

AWS

• Amazon EC2 Auto Scaling: Automatically adjusts the number of EC2 instances in response to the changing demand of your applications, helping to ensure optimal performance.
• AWS Lambda: Run your code in response to triggers without provisioning or managing servers, automatically scaling based on the number of events.
• Amazon ECS and EKS: Manage container workloads with auto-scaling features that adjust resources based on demand for your containerized applications.

Azure

• Azure Virtual Machine Scale Sets: Allow you to deploy and manage a set of identical VMs that can automatically scale in and out based on demand.
• Azure Functions: Serverless compute service that allows you to run event-driven applications, automatically scaling to meet demand.
• Azure Kubernetes Service (AKS): Easily manage your Kubernetes environment with built-in scaling features to adjust the number of pods based on load requirements.

Google Cloud Platform

• Google Compute Engine Autoscaler: Automatically adjusts the number of VM instances in response to traffic and load, ensuring optimal performance for workloads.
• Cloud Functions: Allows you to run your code in a serverless environment, automatically scaling based on incoming requests.
• Google Kubernetes Engine (GKE): Manages Kubernetes clusters and includes features for node auto-scaling based on workload requirements.

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