Google Cloud Architecture Framework: Performance optimization

Last reviewed 2024-12-06 UTC

This pillar in the Google Cloud Architecture Framework provides recommendations to optimize the performance of workloads in Google Cloud.

This document is intended for architects, developers, and administrators who plan, design, deploy, and manage workloads in Google Cloud.

The recommendations in this pillar can help your organization to operate efficiently, improve customer satisfaction, increase revenue, and reduce cost. For example, when the backend processing time of an application decreases, users experience faster response times, which can lead to higher user retention and more revenue.

The performance optimization process can involve a trade-off between performance and cost. However, optimizing performance can sometimes help you reduce costs. ​​For example, when the load increases, autoscaling can help to provide predictable performance by ensuring that the system resources aren't overloaded. Autoscaling also helps you to reduce costs by removing unused resources during periods of low load.

Performance optimization is a continuous process, not a one-time activity. The following diagram shows the stages in the performance optimization process:

Performance optimization process

The performance optimization process is an ongoing cycle that includes the following stages:

  1. Define requirements: Define granular performance requirements for each layer of the application stack before you design and develop your applications. To plan resource allocation, consider the key workload characteristics and performance expectations.
  2. Design and deploy: Use elastic and scalable design patterns that can help you meet your performance requirements.
  3. Monitor and analyze: Monitor performance continually by using logs, tracing, metrics, and alerts.

  4. Optimize: Consider potential redesigns as your applications evolve. Rightsize cloud resources and use new features to meet changing performance requirements.

    As shown in the preceding diagram, continue the cycle of monitoring, re-assessing requirements, and adjusting the cloud resources.

For performance optimization principles and recommendations that are specific to AI and ML workloads, see AI and ML perspective: Performance optimization in the Architecture Framework.

The recommendations in the performance optimization pillar of the Architecture Framework are mapped to the following core principles:

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