What Is AWS Compute Optimizer? The Complete Guide

To meet your computing needs, Amazon Web Services (AWS) offers a simple way of selecting particular virtual machine configurations. You can also automatically scale your cloud resources to suit changing workload requirements, unlike with traditional data centers.

The nicest feature is that you may adjust your workload requirements to fit into particular instance types (VM types) that AWS offers. This is time-consuming and prone to error when done by hand. AWS Compute Optimizer is useful in this situation.

So how does the AWS Compute Optimizer help with that?

What is AWS Compute Optimizer?

An AWS function called AWS Compute Optimizer helps you in selecting the best Amazon EC2 instances for maximum effectiveness and performance.

Compute Optimizer analyzes your past resource usage using machine learning (ML).

Through data analysis, it makes recommendations on how to optimize computing efficiency without going over budget. It then displays to you if your resources are optimal, under-, or over-provisioned.

Four AWS services are analyzed by AWS Compute Optimizer and given optimization recommendations:

Amazon Elastic Compute Cloud (EC2) instance types
Amazon Elastic Block Store (EBS) volumes
AWS Lambda functions
Auto Scaling Groups (ASC)

How does this work?

AWS Compute Optimizer uses machine learning to analyse historical utilization data from CloudWatch, including input/output (IO), storage, CPU, and network consumption. Compute Organizer generates recommendations based on data analysis to save expenses and maximize workload efficiency and computing power.

Graphs are displayed for the most current metric history data by AWS Compute Optimizer. These graphs also show the expected usage for the optimization suggestions made by the service, allowing a company to weigh alternatives and determine which method of instance provisioning is most affordable.

What metrics does AWS Compute Optimizer use to make recommendations?

AWS Compute Optimizer gathers data on virtual CPU, memory, storage, network in/out, disk read/write, volume read/write, and other metrics for instances that are currently in use from AWS CloudWatch.

Installing the AWS CloudWatch Agent is an additional option to gather more Compute Optimizer metrics, such as operating system memory, at an additional cost.

The service gathers historical metrics from the previous 14 days of usage by default. Additionally, they have it programmed to gather data every five minutes. You can reduce that to once every minute if you'd like.

In addition, you can use Enhanced infrastructure metrics to increase the analysis duration from 14 days to three months. These addons are all charged separately.

Getting Started

With an AWS account, using AWS Compute Optimizer is simple to get started. The steps listed below can be used to access the Compute Optimizer service:

Open the Compute Optimizer Console on AWS.
The Compute Optimizer landing page will appear when using the AWS Compute Optimizer for the first time.
Select "opt in."
Gathering the account's AWS resources, AWS Compute Optimizer will start the analysis automatically.
Use the AWS Compute Optimizer to opt out now. You can opt out by entering the following command at the Command Prompt Window if AWS CLI is already installed and set correctly:

aws compute-optimizer update-enrollment-status --status Inactive

Benefits of AWS Compute Optimizer

Cost Efficiency

Cost efficiency is one of the main benefits of using AWS Compute Optimizer. It helps you in avoiding expenses on capacity that is not needed by pointing out unused resources. For new and quickly expanding businesses who need to efficiently monitor their cloud spending, this solution is especially helpful.

Enhanced Performance

AWS Compute Optimizer guarantees the peak efficiency of your applications. It matches the demands of your workloads with the right EC2 instance types and Auto Scaling setups, so your resources are always in line.

Simplified Management

It can be difficult to manage a cloud infrastructure. Compute Optimizer makes managing your AWS infrastructure easier by automating this process and offering actionable information and recommendations.

What are the limitations of AWS compute optimizer?

It offers practical optimization suggestions, but there is no easy way to put the rightsizing step into practice. The instance settings must be changed by hand.
There are several regions where AWS Compute Optimizer is unavailable. Additionally, instance families M, C, R, T, and X are supported. Compute Optimizer only makes recommendations for the supported types if you combine the use of supported and unsupported instance families.
Recommendations compiled from only 14 days of CloudWatch data may not be accurate, particularly if abnormalities happened during those two weeks or if your workload varies seasonally.
Recommendations for network-optimized (-n) instances and Graviton optimization are not offered by the service.
Customizing suggestions and filters based on the AWS region is difficult.

AWS Compute Optimizer vs. Other AWS tools

Compared to other AWS tools like AWS Trusted Advisor, which tells users when CPU utilization exceeds a limit, and AWS Cost Explorer, which tracks CPU underutilization, AWS Compute Optimizer offers more features.

With data gathered from CloudWatch, AWS Compute Optimizer is skilled at examining CPU, storage IO, and network IO consumption. It should be noted that the same engine supports the recommendations made by AWS Compute Optimizer and AWS Cost Explorer resource rightsizing recommendations.

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Conclusion

The latest addition to the stack of built-in tools that assist users in effectively managing and controlling cloud expenses is AWS Compute Optimizer. This machine learning-based tool uses information gathered from CloudWatch to analyze CPU, Storage IO, and Network IO use.

Amazon Compute Optimizer can help businesses promptly rightsize their instances for all AWS accounts. It is free to use and offers recommendations. These recommendations help companies to optimize their computational resources based on workload patterns, which lowers operating expenses.