Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-aws-compute-optimizer-resource-efficiency-metrics-to-estimate-savings-opportunities-and-performance-risks/
By applying the knowledge drawn from Amazon’s experience running diverse workloads in the cloud, AWS Compute Optimizer identifies workload patterns and recommends optimal AWS resources.
Today, I am happy to share that AWS Compute Optimizer now delivers resource efficiency metrics alongside its recommendations to help you assess how efficiently you are using AWS resources:
- A dashboard shows you savings and performance improvement opportunities at the account level. You can dive into resource types and individual resources from the dashboard.
- The Estimated monthly savings (On-Demand) and Savings opportunity (%) columns estimate the possible savings for over-provisioned resources. You can sort your recommendations using these two columns to quickly find the resources on which to focus your optimization efforts.
- The Current performance risk column estimates the bottleneck risk with the current configuration for under-provisioned resources.
These efficiency metrics are available for Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda, and Amazon Elastic Block Store (EBS) at the resource and AWS account levels.
For multi-account environments, Compute Optimizer continuously calculates resource efficiency metrics at individual account level in an AWS organization to help identify teams with low cost-efficiency or possible performance risks. This lets you to create goals and track progress over time. You can quickly understand just how resource-efficient teams and applications are, easily prioritize recommendation evaluation and adoption by engineering team, and establish a mechanism that drives a cost-aware culture and accountability across engineering teams.
Using Resource Efficiency Metrics in AWS Compute Optimizer
You can opt in using the AWS Management Console or the AWS Command Line Interface (CLI) to start using Compute Optimizer. You can enroll the account that you’re currently signed in to or all of the accounts within your organization. Depending on your choice, Compute Optimizer analyzes resources that are in your individual account or for each account in your organization, and then generates optimization recommendations for those resources.
To see your savings opportunity in Compute Optimizer, you should also opt in to AWS Cost Explorer and enable the rightsizing recommendations in the AWS Cost Explorer preferences page. For more details, see Getting started with rightsizing recommendations.
I already enrolled some time ago, and in the Compute Optimizer console I see the overall savings opportunity for my account.
Below that, I have a recap of the performance improvement opportunity. This includes an overview of the under-provisioned resources, as well as the performance risks that they pose by resource type.
Let’s dive into some of those savings. In the EC2 instances section, Compute Optimizer found 37 over-provisioned instances.
I follow the 37 instances link to get recommendations for those resources, and then sort the table by Estimated monthly savings (On-Demand) descending.
On the right, in the same table, I see which is the current instance type, the recommended instance type based on Computer Optimizer estimates, the difference in pricing, and if there are platform differences between the current and recommended instance types.
I can select each instance to further drill down into the metrics collected, as well as the other possible instance types suggested by Computer Optimizer.
Back to the Compute Optimizer Dashboard, in the Lambda functions section, I see that eight functions have under-provisioned memory.
Again, I follow the 8 functions link to get recommendations for those resources, and then sort the table by Current performance risk. In my case, the risk is always low, but different values can help prioritize your activities.
Here, I see the current and recommended configured memory for those Lambda functions. I can select each function to get a view of the metrics collected. Choosing the memory allocated to Lambda functions is an optimization process that balances speed (duration) and cost. See Profiling functions with AWS Lambda Power Tuning in the documentation for more information.
Availability and Pricing
You can use resource efficiency metrics with AWS Compute Optimizer in any AWS Region where it is offered. For more information, see the AWS Regional Services List. There is no additional charge for this new capability. See the AWS Compute Optimizer pricing page for more information.
This new feature lets you implement a periodic workflow to optimize your costs:
- You can start by reviewing savings opportunities for all of your accounts to identify which accounts have the highest savings opportunity.
- Then, you can drill into those accounts with the highest savings opportunity. You can refer to the estimated monthly savings to see which recommendations can drive the largest absolute cost impact.
- Finally, you can communicate optimization opportunities and priority order to the teams using those accounts.
— Danilo