Elevating Performance Testing of Microservices With AI

Quarterly Performance Testing Expert Series - Vol 4/4 2023

QASource Engineering Team
QASource Engineering Team | October 25, 2023
Elevating Performance Testing of Micro-Services With AI

In today's software industry, microservices architecture has revolutionized by offering significant benefits over the traditional monolithic approach. This architecture enables developers to easily integrate new features or updates without impacting other services, making it simpler to scale their application services.

To ensure smooth interactions between services, as well as responsiveness, reliability, stability, and scalability of the application, the microservices performance testing is pivotal, much like other types of testing for this architecture.

In addition to the traditional approach of performance testing, artificial intelligence plays a crucial role in streamlining testing processes, reducing both testing time and complexity that can be achieved by simulating real-world usage scenarios, where predictive analysis mitigates performance bottlenecks, and auto-scaling configurations adapt dynamically to changing workloads.

Let's first examine the projected growth of microservices architecture, expected to increase from USD 5.49 billion to USD 21.61 billion at an 18.66% CAGR from 2021 to 2030:

Microservices Architecture Market Size Trend
CAGR of 18.66%


How AI Is Boosting Performance Testing of Microservices

Due to distributed nature of microservices architecture, the way data flows through different microservices is complex due to the service paths being dynamic and depending upon user choice of using a service or functionality. So, this needs a different test strategy and inclusion of AI performance testing tools help in making testing more efficient.

Microservices Performance Testing With AI

How AI Boosting Performance Testing of Microservices

Below are some important areas where AI can help us in:

  • Predicting the traffic patterns and auto-scale microservices configurations accordingly
  • Optimizing the resource allocation
  • Anomaly detection for microservices that results in errors/crashes or slowness
  • Reducing mean time to resolution(MTTR) by quickly analyzing various data sources like logs, metrics and traces
  • Recommendations for fixing the performance bottlenecks using AI powered APM tools

Key Scenarios for Performance Testing

Below are the major scenarios that need to be covered while preparing the test plan for performance testing of microservices application:

  • Mock dependent external services to have complete workflow of the realistic scenario
  • Outage to test microservices failover:
    • Induce data store outage
    • Empty data stores
    • Dependent service is down
    • Stop or restart microservices
  • With and without cache, especially to expose inefficient caching of database microservices
  • Run continuous tests to ensure the micro-services perform well and there are no resource leaks
  • High number of database connections to reveal database responsiveness
  • Load all microservices simultaneously to check for latency on overall network

Microservices Performance Bottlenecks

There are several factors that can make a service to perform slow or fast depending upon supporting resources and total traffic on the network. A few common problems are mentioned below:


Tools and KPIs for Microservices Load Testing

With the adoption of AI capabilities by existing tools like NeoLoad, Functionize, New Relic, and AppDynamics, you can get better results and more insights for your application. Below are some useful KPIs to measure during load testing:


Best Practices To Test Microservices-based Applications

In order to conduct the Performance testing properly for microservices-based applications, here are some best practices that need to be followed:


High risk or high usage micro-services should be load tested first


Use service virtualization for mocking the dependent functionality still under development


Components like 'Identity Provider', 'Service Discovery' and 'CDN' should be taken into consideration


Thorough knowledge of dependencies between the microservices and plan the testing accordingly


Test performance at UI level as well rather than just focusing on back end architecture


Core objective of microservices performance testing is to expose the components that are not optimized for scalability and prevent the crashes due to heavy load in production. So, performance testing is vital for microservices-based applications and need to be conducted at efficiently to ensure scalability, reliability, and responsiveness.

QASource team has good experience in performance testing for different domains. With powers of AI, they can assist in your performance testing needs to ensure the best user experience, test data preparation with generative AI and real-time monitoring into play using right set of tools. To learn more, get in touch now.

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