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.

Pawan Kumar Sarika

Ericsson AB

Sweden

Deepika Badampudi

Blekinge Institute of Technology

Sweden

Sai Prashanth Josyula

Blekinge institute of technology

Sweden

Muhammad Usman

Blekinge Institute of Technology

Sweden