AUTOMATIC ANOMALY DETECTION AND ROOT CAUSE ANALYSIS FOR MICROSERVICE CLUSTERS

Abstract: Large microservice clusters deployed in the cloud can be very difficult to both monitor and debug. Monitoring theses clusters is a fi€rst step towards detection of anomalies, deviations from normal behaviour. Anomalies are oft‰en indicators that a component is failing or is about to fail and should hence be detected as soon as possible. Th‘ere are oft‰en lots of metrics available to view. Furthermore, any errors that occur oft‰en propagate to other microservices making it hard to manually locate the root cause of an anomaly, because of this automatic methods are needed to detect and correct the problems. Th‘e goal of this thesis is to create a solution that can automatically monitor a microservice cluster, detect anomalies, and fi€nd a root cause. Th‘e anomaly detection is based on an unsupervised clustering algorithm that learns the normal behaviour of each service and then look for data that falls outside that behaviour. Once an anomaly is detected the proposed method tries to match the data against prede€fined root causes. ‘The proposed solution is evaluated in a real microservice cluster deployed in the cloud, using Kubernetes together with a service mesh and several other tools to help gather metrics and trace requests in the system.