Best practice 11.2 – Plan and provision capacity for predictable workload usage

For well-defined workloads, planning capacity ahead based on average usage pattern helps improve resource utilization and avoid over provisioning. For a spiky workload, set up automatic scaling to meet user and workload demand.

Suggestion 11.2.1 – Choose the right instance type based on workload pattern and growth ratio

Consider resource needs, such as CPU, memory, and networking that meet the performance requirements of your workload. Choose the right instance type and avoid overprovisioning. An optimized EC2 instance runs your workloads with optimal performance and infrastructure cost. For example, choose the smaller instance if your growth ratio is low as this allows more granular incremental change.

Suggestion 11.2.2 – Choose the right sizing based on average or medium workload usage

Right sizing is the process of matching instance types and sizes to your workload performance and capacity requirements at the lowest possible cost. It’s also the process of looking at deployed instances and identifying opportunities to downsize without compromising capacity or other requirements that will result in lower costs.

Suggestion 11.2.3 – Use automatic scaling capability to meet the peak demand instead of over provisioning

Analytics services can scale dynamically to meet demand. Then, after the demand has dropped below a certain threshold, the service will remove the resources that are no longer needed. The automatic scaling of serverless services enables applications to handle sudden traffic spikes without capacity planning, reducing costs and improving availability.

There are a number of services that can automatically scale, and other services that you need to configure the scaling for. For example, AWS services like Amazon EMR, AWS Glue, and Amazon Kinesis can auto-scale seamlessly in response to usage spikes and remove resources without any configuration.