Obtain resources upon detection that more resources are needed for a workload

Designing workloads that can dynamically scale in response to demand fluctuations is essential for maintaining performance and availability. By proactively acquiring resources based on workload demands, organizations can ensure a seamless user experience while optimizing resource utilization and cost.

Best Practices

Implement Automated Scaling Solutions

• Use AWS Auto Scaling to automatically adjust capacity based on demand without manual intervention. Define scaling policies that trigger changes in instance counts based on metrics such as CPU utilization or request counts. This ensures your application remains reliable during peak and low usage times.
• Utilize AWS Lambda to create serverless architectures, allowing your application to automatically scale in response to events without upfront resource provisioning. Lambda functions can automatically start and stop based on demand, efficiently managing resources and costs.
• Leverage Amazon S3’s event notifications and triggers to automate workflows and resource management when specific actions occur in your storage (e.g., object creation or deletion) to enhance reliability and responsiveness.
• Incorporate third-party monitoring and scaling tools alongside AWS services to gain granular control and insights into scaling operations, ensuring that your workload remains resilient to demand fluctuations.

Questions to ask your team

• What automation tools are you currently using for scaling resources in your workload?
• How do you monitor demand to trigger scaling actions?
• Can you provide examples of resources that are automatically scaled in your architecture?
• What strategies do you have in place to handle resource failures without manual intervention?
• Are there any bottlenecks in your current automation processes for scaling or replacing resources?

Who should be doing this?

Cloud Architect

• Design scalable architectures that leverage automation for resource management.
• Implement AWS services such as Amazon S3 and AWS Auto Scaling to enhance reliability.
• Evaluate and select third-party tools and AWS SDKs for automating scaling processes.

DevOps Engineer

• Configure automation scripts and CI/CD pipelines for resource scaling.
• Monitor workload performance and adjust automation settings as needed.
• Ensure integration of automation tools with existing infrastructure.

Systems Administrator

• Maintain and oversee the deployment of managed services used for automation.
• Implement monitoring and alerting solutions for scaling events.
• Troubleshoot and resolve issues related to automated resource scaling.

Project Manager

• Coordinate between teams to ensure alignment on automation strategies.
• Manage timelines and deliverables related to scalable workload initiatives.
• Facilitate training sessions on automation tools and processes.

What evidence shows this is happening in your organization?

• AWS Auto Scaling Playbook: A comprehensive guide for implementing AWS Auto Scaling within your workloads, detailing best practices for configuration, automation, and monitoring of scaling events.
• Scaling Automation Checklist: A checklist to ensure that all necessary steps and configurations are completed to automate scaling effectively, covering services like AWS Lambda, Amazon ECS, and Amazon EC2.
• Incident Response Runbook for Resource Impairment: A runbook that outlines the steps to take when a resource becomes impaired, emphasizing the use of automated recovery processes supported by AWS services.
• Resource Scaling Strategy Document: A formal document that outlines the strategies for scaling infrastructure based on demand, incorporating automation, managed services, and third-party tool integrations.
• Monitoring and Alerts Dashboard: A dashboard template that visualizes workload performance metrics in real time, allowing teams to monitor scaling effectiveness and set alerts for proactive resource management.

Cloud Services

AWS

• AWS Auto Scaling: Automatically adjusts the number of Amazon EC2 instances or other resources based on demand.
• AWS Lambda: Runs code in response to events and automatically manages the compute resources required by that code.
• Amazon S3: Provides scalable storage for static content and can automatically scale based on data request patterns.
• Amazon Elastic Load Balancing (ELB): Automatically distributes incoming application traffic across multiple targets, such as Amazon EC2 instances.

Azure

• Azure Autoscale: Automatically scales your applications based on demand and schedules, ensuring optimal performance.
• Azure Functions: A serverless compute service that automatically scales based on demand, allowing for event-driven execution of code.
• Azure Blob Storage: Offers scalable storage solutions that can automatically manage increasing storage demands.
• Azure Load Balancer: Distributes incoming traffic among healthy instances to ensure application reliability.

Google Cloud Platform

• Google Cloud Autoscaler: Automatically adjusts the number of instances in managed instance groups based on load or scheduling.
• Google Cloud Functions: A serverless execution environment that automatically scales based on the incoming request rate.
• Google Cloud Storage: Provides scalable and secure object storage that automatically adapts to your data storage needs.
• Google Cloud Load Balancing: Distributes incoming application traffic across multiple instances, ensuring reliability and efficiency.

Question: How do you design your workload to adapt to changes in demand?
Pillar: Reliability (Code: REL)