Abstract
Microservice-based architectures feature function-ally independent, well-defined and fine-grained components suit-able for loosely coupled deployments and for building reli-able cloud-native applications. Despite the advantages of this approach, component interactions introduce complexity, thus turning boundary -spanning service operation into a daunting challenge. As systems grow in size, complexity can easily outgrow the cognitive capacity of human operators, who are unable to effectively diagnose faulty microservices. We address this problem by proposing a novel framework to diagnose faulty microservices. Through failure injection and an experimental assessment, our layered diagnosis framework using service response analysis, timing constraints, causality and a ranking algorithm from traces, is able to effectively diagnose faulty microservices. Empirical evaluation of the proposed approach, by examining 130 experi-ments in a representative microservice application in the presence of faults, shows that it can achieve approximately 89% specificity and 77% recall.
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