Scale workload infrastructure dynamically

Efficiently aligning cloud resources to demand is crucial for achieving sustainability goals. By optimizing the geographic placement of workloads, organizations can minimize latency, reduce energy consumption, and lower the total network resources required for their operations. This practice not only enhances performance but also contributes to a more sustainable cloud environment.

Best Practices

Implement Auto Scaling and On-Demand Capacity

• Define clear metrics and thresholds (such as CPU usage, memory, or request rates) so that workloads scale up or down automatically in real time to match demand.
• Leverage AWS services supporting auto scaling (e.g., AWS Auto Scaling, Amazon EC2 Auto Scaling, or AWS Fargate) to avoid running unnecessary resources, reducing carbon footprint and operational cost.
• Test and adjust scaling policies regularly to ensure that capacity changes remain aligned with seasonal or fluctuating usage patterns.

Adopt Right-Sizing Strategies

• Use performance data and ongoing monitoring to right-size instances and containers, ensuring instances are neither underutilized nor overprovisioned.
• Experiment with different instance families, sizes, and pricing models (e.g., Spot Instances or Reserved Instances) to find the most efficient deployment option.
• Review resource usage and perform regular capacity assessments to reduce waste proactively.

Optimize Application Architecture for Elasticity

• Design applications with stateless components and decoupled services to make scaling easier and more efficient.
• Use managed services like AWS Lambda, Amazon SQS, and Amazon SNS to offload server management and scale automatically at the function or messaging level.
• Implement caching and content delivery networks (CDNs) such as Amazon CloudFront to reduce the load on core infrastructure, improving efficiency and lowering idle resource consumption.

Leverage Observability and Continuous Improvement

• Monitor resource usage continuously using AWS CloudWatch, AWS X-Ray, or third-party observability tools to pinpoint inefficiencies.
• Regularly run experiments and game days to validate your scaling approach and adjust resource configurations as needed.
• Incorporate feedback loops from this observability data into your development and operational processes to drive ongoing optimizations.

Integrate Sustainability Objectives into Governance

• Set policies and guidelines that mandate regular reviews of capacity utilization and environmental impact for all projects and workloads.
• Incorporate sustainability goals into sprint reviews, design sessions, and application lifecycle processes to ensure alignment across teams.
• Educate teams on the environmental benefits of dynamic scaling, making it part of ongoing cultural and operational practices.

Questions to ask your team

• Are you using auto scaling and elasticity capabilities to match resource supply with varying demand?
• How do you measure resource usage to ensure you are only running the minimum necessary infrastructure?
• Do you have processes to anticipate and respond to spikes in demand to avoid overprovisioning?
• How frequently do you evaluate your workloads for potential right-sizing opportunities?
• Are you using metrics and alerts to trigger scaling events based on actual consumption?
• Do you regularly remove or decommission unused assets to reduce wasted resources?

Who should be doing this?

Cloud Architect

• Design and implement scalable architectures that align with sustainability goals
• Leverage appropriate AWS services to automatically adjust resources based on demand
• Ensure solution designs minimize energy consumption and carbon footprint

DevOps Engineer

• Configure and maintain automation for dynamic scaling policies
• Implement continuous integration and delivery pipelines to rapidly deploy changes
• Monitor system performance and adjust scaling rules to optimize resource usage

Operations Manager

• Oversee resource utilization to maintain sustainable allocation of cloud resources
• Establish operational processes that encourage minimal idle capacity
• Coordinate with cross-functional teams to align infrastructure capacity with demand

Finance Manager

• Analyze costs associated with dynamic scaling and optimize investment
• Collaborate with operations to allocate budgets effectively for sustainability initiatives
• Evaluate cost efficiency of various scaling approaches to ensure financial viability

What evidence shows this is happening in your organization?

• Dynamic Scaling Checklist: A structured checklist to ensure proper and consistent implementation of dynamic scaling. It covers configuring auto-scaling settings, monitoring resource utilization, and regularly reviewing capacity requirements to minimize unnecessary resource use.
• Auto Scaling Policy: A comprehensive policy document outlining the guidelines for using cloud elasticity. It details best practices for right-sizing infrastructures, defining scaling thresholds, and routinely evaluating scaling policies for sustainable resource management.
• Infrastructure Scaling Dashboard: A real-time dashboard providing visibility into current resource usage, capacity, and scaling events. This tool helps teams track consumption patterns, identify over-provisioned resources, and ensure cloud infrastructure aligns with demand to support sustainability goals.

Cloud Services

AWS

• AWS Auto Scaling: Automatically scale your Amazon EC2 capacity to maintain steady and predictable performance at the lowest possible cost.
• Amazon EC2 Auto Scaling: Launch or terminate EC2 instances based on scaling policies, ensuring the number of running instances matches demand.
• AWS Lambda: Run code without provisioning or managing servers. Scales automatically in response to requests.
• Amazon Elastic Kubernetes Service (EKS): Automatically scale containerized workloads using Kubernetes native autoscaling features on Amazon EKS.

Azure

• Azure Virtual Machine Scale Sets: Automatically create and manage a group of load-balanced VMs, scaling easily to meet demand.
• Azure App Service Autoscale: Dynamically scale web apps, mobile, or API apps hosted on Azure to match traffic demands.
• Azure Kubernetes Service (AKS): Leverage Kubernetes autoscaling to dynamically adjust container workloads on Azure.

Google Cloud Platform

• Google Compute Engine Autoscaler: Automatically add or remove VM instances from a managed instance group based on load.
• Google Kubernetes Engine (GKE): Use Kubernetes cluster autoscaling to accommodate dynamic workloads on GKE.
• Cloud Run: Automatically scale stateless containers based on HTTP requests or events in a fully managed serverless platform.