Root cause analysis in NFV

In this paper we consider a root-cause analysis framework for NFV infrastructure. As monitoring machinery for NFV has matured the next step is to leverage on such data to automatically optimize failure detection, analysis, and overall resiliency. The complex architecture and dynamics of NFV poses significant challenges from the point of view of causality inference. In particular the need for an approach that does not depend on domain knowledge or human intervention is of high importance. We propose in this context a step-wise data-driven root-case analysis approach based on correlation clustering, and time sensitivity analysis of alarm data. Our suggested approach recovers templates of causality relationship between network resources alarms which allows the determination of template rules for performing root cause analysis. We demonstrate our approach on real data generated from NFV where our approach generates causality templates that can be validated as well as new ones.