Automating Microservices Test Failure Analysis using Kubernetes Cluster Logs
This work addresses the challenge of time-consuming failure analysis in complex microservices systems for developers and operators, but it is incremental as it applies existing methods to a new domain.
The study tackled the problem of manually analyzing microservices test failures in Kubernetes by comparing five classification algorithms to automate failure reason identification, finding that Random Forest achieved good accuracy with lower computational resource requirements.
Kubernetes is a free, open-source container orchestration system for deploying and managing Docker containers that host microservices. Kubernetes cluster logs help in determining the reason for the failure. However, as systems become more complex, identifying failure reasons manually becomes more difficult and time-consuming. This study aims to identify effective and efficient classification algorithms to automatically determine the failure reason. We compare five classification algorithms, Support Vector Machines, K-Nearest Neighbors, Random Forest, Gradient Boosting Classifier, and Multilayer Perceptron. Our results indicate that Random Forest produces good accuracy while requiring fewer computational resources than other algorithms.