AWS rightsizing visibility tools (for example, AWS Compute Optimizer, Amazon S3 Storage Lens)

Can you describe what AWS Compute Optimizer is and how it can be advantageous for an organization?

AWS Compute Optimizer is a service that recommends optimal AWS resources for your workloads by analyzing your resource usage and employing machine learning to uncover the best-performing configurations. It can result in cost savings and enhanced performance by recommending instance types and sizes and providing insights into EBS volumes and Lambda functions.

How does AWS Compute Optimizer determine the right-sized resources for a workload?

AWS Compute Optimizer uses machine learning to analyze historical utilization metrics of your provisioned resources, such as CPU, memory, and I/O patterns. It then compares metrics to patterns of similar workloads and proposes recommendations for appropriately scaled resources that would maintain or enhance performance while potentially reducing costs.

What is Amazon S3 Storage Lens, and what insights can it provide to users?

Amazon S3 Storage Lens is a cloud storage analytics feature that provides visibility into object storage usage and activity trends across an entire AWS Organization or for specific buckets. It delivers insights such as data protection and cost efficiency, highlighting areas where storage might be optimized through tiering or deletion of infrequently accessed objects.

How would you use the AWS Compute Optimizer to manage costs effectively without compromising on performance?

You would set up the AWS Compute Optimizer to analyze your running EC2 instances, Lambda functions, and EBS volumes. Then, review its recommendations that project potential cost savings against the smallest performance impact (if any) and implement appropriate changes by resizing or changing resource types according to the suggestions.

What are some examples of the recommendations that the AWS Compute Optimizer might provide?

AWS Compute Optimizer might suggest changing an EC2 instance to a different family or size, moving an EC2 workload to a more performance-efficient instance type, modifying EBS volume types for better performance or cost, or adjusting memory and timeout settings for Lambda functions to optimize execution.

In the context of rightsizing, how does AWS ensure that performance benchmarks are not compromised when suggesting optimizations?

AWS uses advanced machine learning models which assess historical performance data against millions of workload configurations to ensure that recommended resources meet or exceed the performance of current workloads. Quality of service is prioritized alongside cost savings in AWS’s rightsizing recommendations.

How often does AWS Compute Optimizer refresh its optimization recommendations, and how can users act on these recommendations dynamically?

AWS Compute Optimizer updates its recommendations approximately every 24 hours. Users can automate actions based on these recommendations by using AWS Lambda to trigger corrective workflows, or through AWS Systems Manager to implement changes manually or automatically.

Does Amazon S3 Storage Lens have any metrics that help identify potential cost savings, and how can users leverage these metrics?

Yes, Amazon S3 Storage Lens provides cost-efficiency metrics such as the storage cost per GiB and recommendations on how to optimize storage costs, including infrequent access storage, deletion of incomplete multipart uploads, and cleanup of expired object delete markers. Users can act on these insights to implement lifecycle policies that automatically optimize storage costs.

What role does AWS Trusted Advisor play in rightsizing AWS resources compared to AWS Compute Optimizer?

AWS Trusted Advisor provides broader best-practice guidance across multiple AWS services, including cost optimization checks for underutilized resources. In contrast, AWS Compute Optimizer focuses specifically on rightsizing compute resources using machine learning. Trusted Advisor is more of a general health check, while Compute Optimizer provides targeted compute recommendations.

Can AWS Compute Optimizer be integrated with other AWS services, and what benefits would this integration bring?

Yes, AWS Compute Optimizer can be integrated with services like AWS Systems Manager for automated workflow execution based on optimizer recommendations or AWS Organizations for centralized resource management. Integrations help automate responses to recommended changes, saving time and ensuring consistent application of best practices.

Can you explain the significance of the Finding Reasons provided in AWS Compute Optimizer recommendations?

The Finding Reasons in AWS Compute Optimizer help users understand the rationale behind each recommendation. They provide detailed context on why a particular instance size or volume configuration is suggested, such as high CPU utilization or infrequent I/O activity, enabling users to make informed decisions on whether to apply the recommendations.

How can an organization ensure that AWS rightsizing recommendations take into account the unique workload requirements and constraints of the business?

Integrate workload-specific performance metrics and business KPIs into AWS CloudWatch along with AWS Compute Optimizer or S3 Storage Lens analysis. Ensure that performance and business needs such as peak load times or regulatory compliance are factored into any resource adjustments. Regularly review and adjust rightsizing actions to align with evolving business requirements and workload patterns.