Ability to Optimize Services for Cost, Performance, and Sustainability – Introduction to Serverless on AWS
Ability to Optimize Services for Cost, Performance, and Sustainability
Cost and performance optimization techniques are common in software develop‐ ment. When you optimize to reduce cost, you target reducing the processing power, memory consumption, and storage allocation. With a traditional application, you exercise these changes at a high level, impacting all parts of the application. You cannot select a particular function or database table to apply the changes to. In such situations, if not adequately balanced, the changes you make could lead to perfor‐ mance degradations or higher costs elsewhere. Serverless offers a better optimization model.
Serverless enables deeper optimization
A serverless application is composed of several managed services, and it may contain multiple resources from the same managed service. For example, in Figure 1-10, you saw three Lambda functions from the AWS Lambda service. You are not forced to optimize all the functions in the same way. Instead, depending on your requirements, you can optimize one function for performance and another for cost, or give one function a longer timeout than the others. You can also allow for different concurrent execution needs.
Based on their characteristics, you can exhibit the same principle with other resources, such as queues, tables, APIs, etc. Imagine you have several microservices, each containing several serverless resources. For each microservice, you can optimize each individual resource at a granular level. This is optimization at its deepest and finest level in action!
Optimizing for Sustainability
In addition to the traditional cost and performance optimization strategies, there is now a need to consider sustainability as well. Chapter 10 explains this in detail, presenting several patterns for developing and operating your applications in a sus‐ tainable AWS cloud environment.
Optimizing for sustainability may have an impact on cost and performance. As a general principle, when you optimize to reduce cost, you are likely to consume fewer compute resources, resulting in lower power consumption, thus promoting sustaina‐ bility. However, optimizing for higher performance often results in consuming more energy and may not align with sustainability goals. In such situations, you’ll need to make trade-offs: identify the expected performance level, and optimize up to it and not beyond.