CARE: Infusing Causal Aware Thinking to Root Cause Analysis in Cloud System

Abstract

With millions of customers accessing online service all over the world, ensuring high service availability is very critical for cloud system. In recent years, empowered by advanced data mining and machine learning technology, there emerges extensive study on data-driven solution to detect anomalous system behavior and diagnose the root cause. However, without any surveilance of data generation process, the existing passive data-driven approach may lead to biased analysis result induced by observed and unobserved confounding factors in the dynamic and heterogeneous system, and thus affect service availability with misleading mitigation actions. In this paper, we propose to infuse causal thinking to the current data-driven solution for cloud system. We developed CARE, a causal aware root cause discovery engine, which utilizes Random Control Trial to proactively generate less ambiguous data for further analysis. A case study shows the application of CARE to Microsoft Office365.