An Unsupervised Anomaly-Based Detection System for Microservices Applications on Kubernetes

Microservices-based applications, which consist of small autonomous containerized services, have become prevalent in recent years. Kubernetes, a popular container orchestration engine, provides a wealth of features at the disposal of the developers of a microservice-based application, such as automatic load balancing, self-healing, service discovery, and more. As clusters and microservices grow in size and complexity so do monitoring costs. The flood of data and their dynamic nature require flexible and scalable monitoring approaches. The use of anomaly detection techniques and their integration with the Prometheus monitoring system for Kubernetes present a promising direction. A distributed, unsupervised, anomaly-based detection system for microservices applications in Kubernetes is presented, with experiments conducted to assess the models' ability to detect abnormalities and their severity.