Testing considerations for sustainable workloads on Azure
Organizations developing and deploying solutions to the cloud also need reliable testing. Learn about the considerations and recommendations for running workload tests and how to optimize for a more sustainable testing model.
Important
This article is part of the Azure Well-Architected sustainable workload series. If you aren't familiar with this series, we recommend you start with what is a sustainable workload?
Testing efficiency
Run integration, performance, load, or any other intense testing during low-carbon periods
Running integration, performance, load, or any other intense testing capability may result in much processing. A well-crafted design for testing the deployed workloads can help ensure full utilization of the available resources, reducing carbon emissions.
Green Software Foundation alignment: Carbon awareness
Recommendation:
- Where you have the data available to you, plan for running testing when the data center's energy mix primarily uses renewable energy. It may, for example, be more beneficial to run testing during the night in some regions.
Automate CI/CD to scale worker agents as needed
Running underutilized or inactive CI/CD agents results in more emissions.
Green Software Foundation alignment: Hardware efficiency
Recommendation:
- Keeps the compute utilization high, based on the current demand, avoiding unnecessary capacity allocation.
- Only scale out when necessary, and when not testing, scale in. Ultimately this ensures there's no idle compute resources in test environments.
- Consider optimized platform services like containers over testing in a VM, utilizing the platform to reduce maintenance.
Consider caching when using CI/CD agents
Using caching mechanisms during CI/CD can reduce compute time and, thus, carbon emissions.
Green Software Foundation alignment: Energy Efficiency
Recommendation:
- Store results from steps in a cache and re-use them between different CI/CD runs when possible: when there are steps that take CPU time to produce an artifact that does not often change between different runs, it is wise to save it for future usage so that CPU time is not wasted on every run producing the same artifact, over and over.
- If the CI/CD agent is self-hosted, use a cache local to the agent to further reduce data transfers and emissions. This ensures that the cache is not transferred over the network, which can be a significant source of emissions.
Split large code repositories
Splitting large repositories can help the CI/CD phases, where only the parts of the code that have changed are compiled. This reduces compute time, which ultimately lowers carbon emissions.
Green Software Foundation alignment: Energy Efficiency
Recommendation:
- Split large code repositories, separating main code from libraries and dependencies.
- Publish and re-use artifacts and libraries of code that are common across multiple repositories.
Recommendation:
- Split large repositories of code into smaller ones, separating main code from libraries and dependencies.
- Publish and re-use artifacts and libraries of code that are common across multiple repositories.
Profiling and measuring
Measuring, profiling, and testing workloads are imperative to understanding how to best use allocated resources.
Assess where parallelization is possible
Without properly profiling and testing workloads, it's difficult to know if it's making the best use of the underlying platform and deployed resources.
Green Software Foundation alignment: Measuring sustainability
Recommendation:
- Test your applications to understand concurrent requests, simultaneous processing, and more.
- If you're running Machine Learning (ML) for tests, consider machines with a GPU for better efficiency gains.
- Identify if the workload is performance intensive and work toward optimization.
- Consider this tradeoff: Running GPU-based machines for ML tests may increase the cost.
Assess with chaos engineering
Running integration, performance and load tests increase the reliability of a workload. However, the introduction of chaos engineering can significantly help improve reliability and resilience and how the applications react to failures. In doing so, the workload can be optimized to handle failures gracefully and with less wasted resources.
Green Software Foundation alignment: Measuring sustainability
Recommendation:
- Use load testing or chaos engineering to assess how the workload handles platform outages and traffic spikes or dips. This helps increase service resilience and the ability to react to failures, allowing for a more optimized fault handling.
- Consider this tradeoff: Injecting fault during chaos engineering and increasing the load on any system also increases the emissions used for the testing resources. Evaluate how and when you can utilize chaos engineering to increase the workload reliability while considering the climate impact of running unnecessary testing sessions.
- Another angle to this is using chaos engineering to test energy faults or moments with higher carbon emissions: consider setting up tests that will challenge your application to consume the minimum possible energy. Define how the application will react to such conditions with a specific "eco" version informing users that they're emitting the minimum possible carbon by sacrificing some features and possibly some performance. This can also be your benchmark application for scoring its sustainability.
Establish CPU and Memory thresholds in testing
Help build tests for testing sustainability in your application. Consider having a baseline CPU utilization measurement, and detect abnormal changes to the CPU utilization baseline when tests run. With a baseline, suboptimal decisions made in recent code changes can be discovered earlier.
Adding tests and quality gates into the deployment and testing pipeline helps avoid deploying non-sustainable solutions, contributing to lowered emissions.
Green Software Foundation alignment: Energy efficiency
Recommendation:
- Monitor CPU and memory allocations when running integration tests or unit tests.
- Find abnormally high resource consumption areas in the application code and focus on mitigating those first.
- Configure alerts or test failures if surpassing the established baseline values, helping avoid deploying non-sustainable workloads.
- Consider this tradeoff: As applications grow, the baseline may need to shift accordingly to avoid failing the tests when introducing new features.
Next step
Review the design considerations for operational procedures.