What Is AWS Compute Optimizer?
AWS Compute Optimizer uses machine learning to analyze the configuration and utilization patterns of your AWS resources, and provides recommendations on how to tweak them for improved performance and cost efficiency. Its primary goal is to help you get the most out of your AWS resources, without requiring you to spend hours manually analyzing and adjusting your configurations.
It’s important to note that using the AWS Compute Optimizer does not necessarily mean you have to make drastic changes to your current setup. Rather, it provides you with insightful recommendations that you can choose to implement based on your specific needs and goals. It can help you understand your AWS ecosystem better and utilize it more effectively.
Key Features of AWS Compute Optimizer
Recommendations for Optimizing Resource Allocation
One of the key features of the AWS Compute Optimizer is its ability to provide recommendations for optimizing resource allocation. The tool analyzes your current AWS usage and suggests how you can better allocate your resources to improve performance, reduce costs, or both. This feature is particularly beneficial for organizations that have complex AWS environments with numerous resources to manage.
For instance, if the AWS Compute Optimizer identifies that a certain EC2 instance is underutilized, it might recommend downsizing that instance to a smaller, less expensive one. Conversely, if it detects that an EC2 instance is consistently maxing out its resources, it might suggest upgrading to a larger, more powerful instance. These recommendations can help you avoid over or under-provisioning, which can lead to wasted resources or poor performance.
Performance Risk Assessment
Another important feature of the AWS Compute Optimizer is the performance risk assessment. This feature assesses the risk of any performance issues that may occur if you follow the optimizer's recommendations. It provides a risk score that indicates the likelihood of the recommended instance type not meeting the performance needs of your workloads.
For example, if you have a workload that requires a lot of compute power, the AWS Compute Optimizer will take this into account when making recommendations. If it suggests a smaller instance type, it will also present you with a risk score indicating the potential for performance degradation. This valuable insight allows you to make informed decisions about your resource allocation.
Usage Pattern Analysis
The AWS Compute Optimizer also performs a usage pattern analysis. This analysis examines the way you use your resources over time, identifying patterns and trends that can inform optimization strategies. It considers factors such as the time of day, day of the week, and overall usage trends when making recommendations.
For instance, if the AWS Compute Optimizer detects that your usage spikes during certain times of the day or week, it might suggest scaling strategies to accommodate these peaks. By identifying these patterns, the optimizer can help you align your resource allocation with your usage needs, ensuring you have the right resources available when you need them.
How AWS Compute Optimizer Analyzes AWS Resources
The AWS Compute Optimizer uses machine learning to analyze your AWS resources. It examines the historical utilization data of your EC2 instances and Auto Scaling groups, considering both the configuration of these resources and the performance of your workloads. Using this data, it identifies opportunities for optimization and provides recommendations tailored to your specific needs.
To perform this analysis, the AWS Compute Optimizer considers multiple factors. It looks at the specifications of your instances, including CPU, memory, and storage. It also considers the performance of your workloads, analyzing how they use these resources over time. By considering these factors in tandem, the AWS Compute Optimizer can provide insightful, actionable recommendations.
An important aspect of AWS Compute Optimizer is that it continuously analyzes your AWS resources, adjusting its recommendations as your needs and usage patterns change. This ongoing analysis ensures that the optimizer's recommendations remain relevant and useful, providing you with the guidance you need to optimize your AWS environment effectively.
Getting Started with AWS Compute Optimizer
Choose Compute Optimizer in the AWS Management Console
Before we can start using AWS Compute Optimizer, we first need to locate it in the AWS Management Console. Upon logging in, navigate to the services dropdown menu. Here, you'll find a list of all the services offered by AWS, categorized by type. Scroll or search for the Compute Optimizer under the Management and Governance section. Click on it to open the AWS Compute Optimizer dashboard.
The dashboard is the central hub for all activities related to Compute Optimizer. Here, you can opt-in to the service, generate and review recommendations, define your preferences, and visualize scenarios.
Opt-In for Compute Optimizer
Once you've located AWS Compute Optimizer in the AWS Management Console, the next step is to opt in. This is a crucial step because Compute Optimizer is an opt-in service. This means that it won't start analyzing your AWS resources and generating recommendations until you expressly opt in.
To opt in, simply go to the Compute Optimizer dashboard, and click on the Opt-in button. Once you've successfully opted in, Compute Optimizer will begin analyzing your AWS resources, and you can start using the service.
Automatically Generate Recommendations
Compute Optimizer will start to generate recommendations, which can be viewed on the dashboard. This may take several hours. A box will appear for each type of resource (e.g., EC2 instances, Auto Scaling groups), listing the number of recommendations. It will also indicate how many resources are optimized or over-provisioned. You can select the option at the bottom of the box to view the detailed recommendations.
Define Your Recommendation Preferences
AWS Compute Optimizer allows you to define your recommendation preferences. This feature is particularly useful for businesses with specific cost or performance needs. To define your preferences, navigate to the Compute Optimizer dashboard, click on the Preferences button, and select Recommendation preferences. Here, you can specify whether you prioritize cost or performance, and set a utilization target for your AWS resources.
After Compute Optimizer has analyzed your AWS resources and generated recommendations, the next step is to review these recommendations. You can view a summary of the recommendations on the Compute Optimizer dashboard, or you can download a detailed report.
To review the recommendations, click on the View recommendations for X on the dashboard, where X could be EC2 instances, Auto Scaling groups, Lambda functions, or ECS services on Fargate . Here, you can view the recommendations by resource type, and see the estimated cost savings and performance improvements.
Visualize a What-If Scenario
This feature allows you to simulate how changes to your AWS resources, based on Compute Optimizer's recommendations, would affect their cost and performance.
To visualize a what-if scenario, go to the Compute Optimizer dashboard, click on the view detail button, and compare the existing instance with the recommended scenario. Here, you can choose from several options that apply Compute Optimizer's recommendations, and see the predicted outcomes.
AWS Compute Optimizer is a powerful tool that can help you optimize your AWS resources for cost and performance. By following these steps, you can get started with Compute Optimizer and start making data-driven decisions for your AWS workloads.
Author Bio: Gilad David Maayan
Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP (News - Alert), Imperva, Samsung NEXT, NetApp and Check Point, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership. Today he heads Agile SEO, the leading marketing agency in the technology industry.