# PERF02-BP06 Re-evaluate compute needs based on metrics
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 Use system-level metrics to identify the behavior and requirements of your workload over time. Evaluate your workload's needs by comparing the available resources with these requirements and make changes to your compute environment to best match your workload's profile. For example, over time a system might be observed to be more memory-intensive than initially thought, so moving to a different instance family or size could improve both performance and efficiency. 

 **Common anti-patterns:** 
+  You only monitor system-level metrics to gain insight into your workload. 
+  You architect your compute needs for peak workload requirements. 
+  You oversize the compute solution to meet scaling or performance requirements when moving to a new compute solution would match your workload characteristics 

 **Benefits of establishing this best practice:** To optimize performance and resource utilization, you need a unified operational view, real-time granular data, and a historical reference. You can create automatic dashboards to visualize this data and perform metric math to derive operational and utilization insights. 

 **Level of risk exposed if this best practice is not established:** Low 

## Implementation guidance
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 Use a data-driven approach to optimize resources: To achieve maximum performance and efficiency, use the data gathered over time from your workload to tune and optimize your resources. Look at the trends in your workload's usage of current resources and determine where you can make changes to better match your workload's needs. When resources are over-committed, system performance degrades, whereas underutilization results in a less efficient use of resources and higher cost. 

## Resources
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 **Related documents:** 
+  [Cloud Compute with AWS ](https://aws.amazon.com/products/compute/?ref=wellarchitected) 
+  [AWS Compute Optimizer](https://aws.amazon.com/compute-optimizer/) 
+  [Cloud Compute with AWS](https://aws.amazon.com/products/compute/) 
+  [EC2 Instance Types](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instance-types.html) 
+  [ECS Containers: Amazon ECS Container Instances](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ECS_instances.html) 
+  [EKS Containers: EKS Worker Nodes](https://docs.aws.amazon.com/eks/latest/userguide/worker.html) 
+  [Functions: Lambda Function Configuration](https://docs.aws.amazon.com/lambda/latest/dg/best-practices.html#function-configuration) 
+  [Processor State Control for Your EC2 Instance](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/processor_state_control.html) 

 **Related videos:** 
+  [Amazon EC2 foundations (CMP211-R2)](https://www.youtube.com/watch?v=kMMybKqC2Y0) 
+  [Better, faster, cheaper compute: Cost-optimizing Amazon EC2 (CMP202-R1)](https://www.youtube.com/watch?v=_dvh4P2FVbw) 
+  [Deliver high performance ML inference with AWS Inferentia (CMP324-R1)](https://www.youtube.com/watch?v=17r1EapAxpk) 
+  [Optimize performance and cost for your AWS compute (CMP323-R1)](https://www.youtube.com/watch?v=zt6jYJLK8sg) 
+  [Powering next-gen Amazon EC2: Deep dive into the Nitro system](https://www.youtube.com/watch?v=rUY-00yFlE4) 

 **Related examples:** 
+  [Rightsizing with Compute Optimizer and Memory utilization enabled](https://www.wellarchitectedlabs.com/cost/200_labs/200_aws_resource_optimization/5_ec2_computer_opt/) 
+  [AWS Compute Optimizer Demo code](https://github.com/awslabs/ec2-spot-labs/tree/master/aws-compute-optimizer)