AWS Compute Cost Optimization – Cloud Avenue

AWS offers a wide range of compute services, each with its unique features and pricing models. While compute resources are essential for running applications, it’s crucial to optimize costs and avoid overspending. In this blog post, we will explore strategies and best practices for optimizing AWS compute costs, helping you strike the right balance between performance and expenditure.

1. Right-sizing Instances: Choosing the appropriate instance type is key to cost optimization. Analyze your application’s resource requirements and match them with the right-sized instance. Avoid overprovisioning by selecting instances that align with your workload’s CPU, memory, and storage needs. You can leverage AWS Compute Optimizer to get the Right-Sizing recommendations for EC2, EBS, Lambda and Fargate. Since, the default EC2 metrices doesn’t covers the Memory utilization, you may have to install the AWS Cloudwatch Agent on your EC2 instances to get those metrices. Refer AWS documentation here to implement the same.

2. Move to latest generation and right Instance Family: AWS regularly introduces new generations of instances with improved performance, efficiency, and cost-effectiveness. By migrating your workloads to the latest generation of instances, you can take advantage of enhancements that offer better performance at the same or lower cost. Newer instance generations often incorporate the latest technologies, such as faster processors, increased memory, improved network capabilities, and enhanced storage options. These advancements can lead to better performance, reduced latency, and optimized resource utilization, resulting in potential cost savings.

Instance Family Examples	Processor families
M – General purpose C – Compute optimized R – Memory optimized I – Storage optimized T – Burstable performance P – GPU accelerated G – Graphics intensive Hpc – High performance computing Inf – AWS Inferentia	a – AMD processors g – AWS Graviton processors i – Intel processors

Additionally, selecting the right instance family for your workload can further optimize costs. AWS offers a wide range of instance families optimized for different use cases, such as general-purpose, compute-optimized, memory-optimized, and storage-optimized. By choosing the instance family that aligns closely with your workload requirements, you can ensure cost-efficient resource allocation without overprovisioning. Perform a thorough analysis of your workload characteristics, including CPU, memory, storage, and network requirements, to determine the optimal instance family and generation. Consider factors such as the required level of CPU performance, memory-intensive workloads, storage demands, and network bandwidth needs.

3. AWS Auto scaling: Implement autoscaling to dynamically adjust compute resources based on demand. By scaling up or down based on workload fluctuations, you can optimize costs by utilizing resources efficiently. Autoscaling allows you to maintain the desired performance while minimizing underutilization and overspending. Through autoscaling you can also split the large instances to smaller instance with a autoscaling rule if the utilization of the instance is less then 50% on average.

4. Embrace Graviton-based Instances: Consider utilizing AWS Graviton-based instances, which are powered by Arm-based processors. Graviton instances offer a cost-effective alternative to traditional x86 instances, providing similar performance at a lower price point, sometimes up to 50%. By leveraging Graviton instances, you can achieve cost savings for a wide range of workloads, including web servers, containerized applications, microservices, and more.
Graviton-based instances are designed to optimize performance and efficiency while maintaining compatibility with popular Linux-based applications and frameworks. They are particularly well-suited for applications that are not tied to specific x86 dependencies. To leverage Graviton instances, you can analyze your workload requirements and identify compatible instances from the AWS Graviton family, such as the Amazon EC2 M6g, C6g, and R6g instances.
It’s worth noting that not all workloads may be suitable for Graviton instances, especially if they rely heavily on x86-specific software or have specific performance requirements that align better with x86 processors. Therefore, it’s important to conduct proper testing and evaluation before migrating your workloads to Graviton instances to ensure compatibility and optimal cost savings. By considering Graviton-based instances as part of your AWS compute strategy, you can explore additional cost-saving opportunities and potentially reduce your compute costs without compromising performance or functionality. Refer Graviton Getting Started Guide

5. Leverage AMD-based Instances: If you applications have dependency on x86 consider instances powered by AMD EPYC processors, providing an alternative to Intel-based instances. AMD-based instances offer competitive performance at a lower price point, sometimes up to 40-50%. By leveraging AMD-based instances, you can achieve cost savings while maintaining performance levels comparable to Intel-based instances. To take advantage of AMD-based instances, you can evaluate your workload requirements and select instances from the appropriate AWS instance families that offer AMD processor options, such as the M5a, R5a, and T3a instances.

6. Utilize Spot Instances and Spot Fleet: Spot Instances provide access to unused AWS compute capacity at significantly reduced prices. Leverage Spot Instances for fault-tolerant and flexible workloads that can handle interruptions. By leveraging Spot Instances, you can achieve substantial cost savings, but ensure your application can tolerate potential termination notices.

Utilize Spot Fleets that combines Spot Instances with On-Demand and Reserved Instances to optimize cost, performance, and availability. By configuring Spot Fleets intelligently, you can ensure that your application maintains the desired performance level while maximizing cost savings by leveraging Spot Instances.

7. Use Amazon CodeGuru Profiler to improve the CPU and latency performance: You can use the Amazon CodeGuru Profiler to identify application’s most expensive lines of code and optimize it through intelligent recommendations. CodeGuru Profiler is a developer tool powered by machine learning (ML) that helps you to identify application performance issues and troubleshoot latency and CPU utilization issues. CodeGuru Profiler works for any application running on Amazon EC2, AWS Lambda, Amazon Elastic Container Service, AWS Fargate, or AWS Elastic Beanstalk. Refer this AWS blog on Investigating performance issues with Amazon CodeGuru Profiler.

8. Build on AWS Serverless: AWS Serverless services like Lambda provides serverless compute where you pay only for the actual execution time of your code. By leveraging Lambda for event-driven workloads or short-lived tasks, you can eliminate the costs associated with idle resources and achieve granular cost optimization.

Similarly, Containerize with AWS Fargate, which offers serverless container management, allowing you to run containers without managing underlying infrastructure. Fargate enables cost optimization by automatically scaling resources based on workload requirements and charging only for the resources consumed during the container’s execution.

9. Buy Saving Plans and Reserved Instances (RIs): AWS Compute Savings Plans offer flexibility and significant cost savings for consistent usage over a period. Compute Savings Plans provide savings in exchange for a per hour commitment of using compute power for a one or three year period. Savings plans doesn’t lock you to move between Regions, Instance families and other AWS computer services. Please refer below to understand the it is calculated.

EC2 Instance Savings Plans offer substantial savings of up to 72 percent off the On-Demand pricing. You need to make a commitment to a particular instance family within chosen AWS Region, such as M5 instances in Virginia. These savings plans are automatically applied to usage, regardless of the instance size (e.g., m5.xlarge, m5.2xlarge, etc.), operating system (Windows, Linux, etc.), and tenancy type (Host, Dedicated, Default) within the selected instance family and Region.

Reserved Instances offer significant cost savings compared to On-Demand instances. Analyze your usage patterns and consider purchasing RIs for long-term workloads with predictable demand. By committing to a specific term, you can benefit from substantial discounts.

Please note that these are irreversible plans, once purchased. Therefore, It is important to understand the right usage before making a decision to buy these plans.

10. Monitor and Analyze Usage: Utilize AWS Cost Explorer, AWS Trusted Advisor, and Amazon CloudWatch to gain insights into your compute resource usage and identify areas for optimization. Monitor resource utilization, identify idle or underutilized instances, and make informed decisions to adjust capacity based on usage patterns.

11. Regularly Review and Optimize: Compute resource requirements change over time, so it’s crucial to regularly review and optimize your compute infrastructure. Analyze usage patterns, evaluate instance types, monitor new AWS offerings, and adjust your resource allocations accordingly to ensure ongoing cost optimization.

Conclusion:

In conclusion, optimizing AWS compute costs involves several key strategies. These include right-sizing instances based on application requirements, migrating to the latest generation and selecting the right instance family, implementing autoscaling to adjust resources dynamically, considering Graviton and AMD-based instances for cost savings, leveraging Spot Instances and Spot Fleets, utilizing tools like Amazon CodeGuru Profiler for performance optimization, building on AWS Serverless services and containerization with AWS Fargate, utilizing Savings Plans and Reserved Instances for long-term workloads, monitoring and analyzing usage with AWS tools, and regularly reviewing and optimizing compute infrastructure. By implementing these practices, users can achieve cost-efficiency while maintaining performance in their AWS environments.