DLA: Detecting and Localizing Anomalies in Containerized Microservice Architectures Using Markov Models

Abstract

Container-based microservice architectures are emerging as a new approach for building distributed applications as a collection of independent services that works together. As a result, with microservices, we are able to scale and update their applications based on the load attributed to each service. Monitoring and managing the load in a distributed system is a complex task as the degradation of performance within a single service will cascade reducing the performance of other dependent services. Such performance degradations may result in anomalous behaviour observed for instance for the response time of a service. This paper presents a Detection and Localization system for Anomalies (DLA) that monitors and analyzes performance-related anomalies in container-based microservice architectures. To evaluate the DLA, an experiment is done using R, Docker and Kubernetes, and different performance metrics are considered. The results show that DLA is able to accurately detect and localize anomalous behaviour.