DeLag: Using Multi-Objective Optimization to Enhance the Detection of Latency Degradation Patterns in Service-based Systems

Abstract--Performance debugging in production is a fundamental activity in modern service-based systems. The diagnosis of performance issues is often time-consuming, since it requires thorough inspection of large volumes of traces and performance indices. In this paper we present DeLag, a novel automated search-based approach for diagnosing performance issues in service-based systems. DeLag identifies subsets of requests that show, in the combination of their Remote Procedure Call execution times, symptoms of potentially relevant performance issues. We call such symptoms Latency Degradation Patterns. DeLag simultaneously searches for multiple latency degradation patterns while optimizing precision, recall and latency dissimilarity. Experimentation on 700 datasets of requests generated from two microservice-based systems shows that our approach provides better and more stable effectiveness than three state-of-the-art approaches and general purpose machine learning clustering algorithms. DeLag is more effective than all baseline techniques in at least one case study (with p 0.05 and non-negligible effect size). Moreover, DeLag outperforms in terms of efficiency the second and the third most effective baseline techniques on the largest datasets used in our evaluation (up to 22%). In order to support this fastpaced issue, and initial understanding, scoping and localization release cycle, IT organizations often employ several are among the most time-consuming phases during debugging. Unfortunately, frequent software releases often service-based systems [9], [10], [11], [12], [13], [14], [15], the hamper the ability to deliver high quality software [3]. For reduction of the manual effort and the time needed is still example, widely used performance assurance techniques, critical. Also, given the complexity of these systems rely on pattern mining to spot patterns in trace attributes and their workloads [6], it is often unfeasible to proactively (e.g., request size, response size, RPCs execution times) detect performance issues in a testing environment [7].