AWS Cost Optimizations with Graviton CPUs: A Guide

#AWS offers Graviton processors, a family of ARM-based CPUs that provide up to 40% better price performance compared to traditional x86-based instances. By adopting Graviton processors in your EC2 instances and Lambda functions, you can achieve significant cost savings while maintaining high performance. In this article, we will explore how to optimize your AWS infrastructure using Graviton processors, along with an extended example to demonstrate their effectiveness.

Understanding Graviton Processors

Graviton processors are custom-designed by AWS to deliver better performance per dollar compared to traditional x86-based instances. They are built on 64-bit ARM Neoverse cores, providing power-efficient and cost-effective computing. AWS offers two generations of Graviton processors: Graviton (A1 instances) and Graviton2 (M6g, C6g, R6g, and T4g instances). Graviton2 instances offer up to 40% better price-performance compared to the latest x86-based instances.

Example Use-Cases:

Four example use cases and recommend the appropriate Graviton instances for each scenario:

Web Application Hosting:

You are hosting a web application that requires a balance between compute, memory, and networking resources. Your application serves both dynamic and static content, processes user input, and interacts with a database.

Recommended Instance Family: M6g (General Purpose)

Reason: M6g instances provide a well-balanced combination of compute, memory, and networking resources, making them suitable for hosting web applications.

Scientific Simulation:

You are running a scientific simulation that involves complex mathematical computations, such as fluid dynamics, weather modeling, or molecular dynamics. The simulation requires substantial CPU power to perform calculations quickly and accurately.

Recommended Instance Family: C7g (Compute Optimized)

Reason: C7g instances offer high-performance processors designed for compute-intensive tasks, making them an ideal choice for running scientific simulations.

In-memory Database:

You are operating an in-memory database, such as Redis or Memcached, to store and quickly access data for your application. The database requires high memory capacity and low-latency access to maintain fast response times.

Recommended Instance Family: R6g (Memory Optimized)

Reason: R6g instances provide a higher memory-to-vCPU ratio, making them suitable for memory-intensive workloads like in-memory databases.

Development Environment:

You are setting up a development environment for your team to build, test, and deploy applications. The environment requires a baseline level of CPU performance, but occasionally needs to burst above the baseline to handle increased demand during specific tasks, such as compiling code or running tests.

Recommended Instance Family: T4g (Burstable Performance)

Reason: T4g instances provide a baseline level of CPU performance with the ability to burst above the baseline when needed, making them a cost-effective choice for development environments.

Graviton Family Overview

M7g

M7g instances are part of the Graviton2-based instance families in AWS. They are designed for general-purpose workloads that require a balance between compute, memory, and networking resources. M7g instances are powered by the AWS Graviton3 processor.

M7g instances are well-suited for various use cases, including:

1. Application servers: Running web applications, enterprise applications, or small to mid-sized databases that require balanced resources.
2. Containerized microservices: Deploying and managing containers that run microservices, which can benefit from the improved price-performance offered by Graviton2 processors.
3. Caching fleets: Operating in-memory caching services like Redis or Memcached, where the balance of compute and memory resources is critical.
4. Data processing tasks: Running tasks that require moderate CPU and memory resources, such as ETL jobs and log analysis.
5. Development and testing environments: Providing cost-effective resources for developers to build and test applications in the cloud.

T4g

T4g instances are part of the Graviton2-based instance families in AWS, designed for burstable performance. These instances provide a baseline level of CPU performance with the ability to burst above the baseline when needed, depending on the available CPU credits. T4g instances are powered by AWS Graviton2 processors, which are built on a 64-bit ARM Neoverse core and deliver improved price-performance compared to x86-based instances.

T4g instances are well-suited for various use cases, including:

1. Small-scale workloads: Running applications or services with low to moderate CPU utilization that occasionally require higher CPU performance for short periods.
2. Development and testing environments: Providing cost-effective resources for developers to build, test, and deploy applications in the cloud.
3. Low-traffic web applications: Hosting websites or web applications with low to moderate levels of traffic that experience occasional spikes in demand.
4. Microservices: Deploying and managing containerized microservices that can benefit from the burstable CPU performance provided by T4g instances.
5. Batch processing: Running batch processing jobs or data processing tasks that can take advantage of the available CPU credits during periods of increased demand.

C7g / C7gn

C7g instances are part of the Graviton3-based instance families in AWS. They are designed for compute-optimized workloads, offering high-performance processors for tasks that require substantial CPU power. C7g instances are powered by the AWS Graviton3 processor.

C7g instances are well-suited for various use cases, including:

1. Compute-intensive applications such as machine learning
2. High-performance computing (HPC): Running scientific simulations, financial modeling, and other compute-intensive workloads that benefit from the powerful Graviton2 processors.
3. Batch processing: Executing large-scale batch processing jobs, data transformation tasks, or data analytics that require significant CPU power.
4. Video encoding: Performing video encoding, transcoding, or streaming tasks that demand high CPU performance.
5. Gaming servers: Hosting multiplayer game servers, where low latency and high-performance processors are crucial.
6. Scientific modeling: Running complex computational models for research, engineering, or data analysis that require high CPU performance.

R7g

R7g instances are a Graviton3-based instance familiy. They are designed for memory-optimized workloads that require a higher memory-to-vCPU ratio.

R7g instances are well-suited for various use cases, including:

1. High-performance databases: Running database workloads such as relational and NoSQL databases that require high memory capacity and performance.
2. In-memory databases: Operating in-memory databases like Redis or Memcached, where high memory capacity and low-latency access are crucial.
3. Real-time big data analytics: Analyzing large datasets in real-time, where high memory capacity is needed to store and process data efficiently.
4. Data warehousing: Supporting large-scale data warehousing solutions that require significant memory resources for processing and querying data.
5. Other memory-heavy applications: Running applications like caching servers, search engines, or recommendation engines that benefit from the increased memory capacity provided by R6g instances.

X2gd

X2gd instances are a Graviton2-based instance familiy. They are providing the Lowest cost per GiB of memory in Amazon EC2.

Migrating to Graviton-based EC2 Instances

To start saving costs with Graviton processors, you need to migrate your existing workloads to Graviton-based EC2 instances. Evaluate your current workloads to ensure compatibility with ARM architecture, and perform necessary code adjustments or recompilations if required. For containerized applications, use ARM-compatible base images for your Docker containers.

Extended Example: Suppose you run a web application on an x86-based EC2 instance, such as a c5.large (2 vCPUs and 4 GiB memory). After assessing your workload's compatibility with ARM architecture, you can migrate to a Graviton2-based instance, such as a c6g.large (2 vCPUs and 4 GiB memory), to achieve better price-performance.

Optimizing Lambda Functions with Graviton2

AWS Lambda now supports Graviton2 processors, allowing you to achieve better performance at a lower cost. To optimize your Lambda functions, you can create a new function or update an existing one to use the ARM64 architecture. Ensure that your function's code and dependencies are compatible with ARM before making the switch.

Extended Example: You have a Lambda function written in Python that processes user data for your web application. To optimize this function for Graviton2, ensure that your Python packages and libraries are ARM-compatible, and then update the function's runtime configuration to use the ARM64 architecture.

Monitoring and Fine-tuning Graviton-based Infrastructure

After migrating to Graviton-based EC2 instances and Lambda functions, monitor their performance using AWS CloudWatch. Analyze the metrics, such as CPU utilization, memory usage, and response times, to ensure that your infrastructure is running efficiently. Adjust the instance sizes or Lambda function configurations if necessary to maintain optimal performance at the lowest cost.

Extended Example: After migrating your web application to a c7g.large instance and updating your Lambda function to use the ARM64 architecture, monitor their performance using CloudWatch. If you notice that the CPU utilization for the c6g.large instance is consistently below 20%, consider downgrading to a smaller instance type, such as a c6g.medium (1 vCPU and 2 GiB memory), to further reduce costs.

Example Calculation

In this example calculation, we will compare the costs of running a web application on AWS using regular x86-based instances versus Graviton-based instances. We will consider a scenario where an application runs on two instances 24/7 for 30 days. For simplicity, we will focus on the On-Demand pricing of EC2 instances in the US East (Northern Virginia) region.

1. Regular x86-based instances

Instance type: c5.large (2 vCPUs and 4 GiB memory)

On-Demand price: $0.085 per hour

Number of instances: 2

Total hours per month: 2 instances * 24 hours * 30 days = 1440 hours

Cost: 1440 hours * $0.085 per hour = $122.40

1. Graviton-based instances

Instance type: c6g.large (2 vCPUs and 4 GiB memory)

On-Demand price: $0.068 per hour

Number of instances: 2

Total hours per month: 2 instances * 24 hours * 30 days = 1440 hours

Cost: 1440 hours * $0.068 per hour = $97.92

Cost savings calculation:

Savings = (Cost of x86-based instances - Cost of Graviton-based instances) / Cost of x86-based instances * 100%

Savings = ($122.40 - $97.92) / $122.40 * 100% = 20%

By using Graviton-based c6g.large instances instead of x86-based c5.large instances, you can save approximately 20% on your EC2 costs in this example. Keep in mind that cost savings will vary depending on the instance types and specific workloads. In some cases, Graviton2 instances can deliver up to 40% better price-performance compared to x86-based instances.