Automating Microservices Test Failure Analysis using Kubernetes Cluster
  Logs
Pawan Kumar Sarika Deepika Badampudi Sai Prashanth Josyula Muhammad Usman
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.