Learning-based Automated Generation of Critical Workload Configurations for Microservices Performance Testing

Performance testing is an essential activity in the engineering of microservice applications to identify deviations from the specified ranges of relevant metrics and to analyse resources usage. It demands for high automation to fit within the short microservices development-operation cycles. Engineers are often interested in identifying critical workloads - ideally, in the “minimal“ load configurations causing tests to expose performance issues. Triggering performance issues is challenging, requiring proper workload characterization and test design. We present the microWave framework for learning-based automated generation of critical performance testing workloads for microservices. The framework can harness various learning strategies: we analyze a Deep Neural Network, a Large Language Model and a Causal Reasoning strategy. We evaluate them experimentally on four subjects, using a random approach and a manually-crafted ground truth as baselines. The results show that the strategies exhibit different behavior depending on the data they learn from. When inferring from past executions data including performance issues, the causal model performs better. The random predictor is preferable when no data is available; however, it is more costly as it requires more tests. The results allow to draw practical recommendations for testers on how to select the most suitable strategy depending on the needs.