Using Distributed Tracing to Identify Inefficient Resources Composition in Cloud Applications

Abstract

Cloud-Applications are the new industry standard way of designing Web-Applications. With Cloud Computing, Applications are usually designed as microservices, and developers can take advantage of thousands of such existing microservices, involving several hundred of cross-component communications on different physical resources.Microservices orchestration (as Kubernetes) is an automatic process, which manages each component lifecycle, and notably their allocation on the different resources of the cloud infrastructure. Whereas such automatic cloud technologies ease development and deployment, they nevertheless obscure debugging and performance analysis. In order to gain insight on the composition of services, distributed tracing recently emerged as a way to get the decomposition of the activity of each component within a cloud infrastructure. This paper aims at providing methodologies and tools (leveraging state-of-the-art tracing) for getting a wider view of application behaviours, especially focusing on application performance assessment.In this paper, we focus on using distributed traces and allocation information from microservices to model their dependencies as a hierarchical property graph. By applying graph rewriting operations, we managed to project and filter communications observed between microservices at higher abstraction layers like the machine nodes, the zones or regions. Finally, in this paper we propose an implementation of the model running on a microservices shopping application deployed on a Zonal Kubernetes cluster monitored by OpenTelemetry traces. We propose using the flow hierarchy metric on the graph model to pinpoint cycles that reveal inefficient resource composition inducing possible performance issues and economic waste.