DyMonD

Abstract

Cloud applications are often implemented as distributed services that call each other, creating complex application call graphs. Tracking such call graphs is crucial to diagnose and resolve performance issues. This paper presents DyMonD, a holistic framework that dynamically monitors the software layer of the cloud network to track dependencies between application components and derive performance metrics. It adapts a deep learning model to identify the service type of each component, and visualizes all information in form of a call graph. Our evaluation results confirm that DyMonD can infer the proper call graph and identify the services at run-time with acceptable overhead and good accuracy.