Digital-Twin assisted Root Cause Analysis of Anomalies in NFV Environment

Abstract

Network Function Virtualization (NFV) enables the employment of novel service types with lower deployment cost and faster time-to-value, but it introduces new fault management problems and challenges. Anomalies of virtual machines (VMs) can be caused by faulty components of their located physical servers or anomaly propagation from other ones. If the data patterns of VMs are detected as anomalous, we need to locate the root causes precisely to recover the networks as soon as possible. In this paper, we first introduce digital twin to capture the real-time anomaly-fault dependency matrix for the networks. Assisted by the dependency matrix, a dynamic set-covering (DSC) problem is formulated and modeled with a set of parallel hidden Markov models to find a minimal set of faulty components at each observation epoch, which can cover all anomalous VMs. We introduce alternating direction method of multipliers to decompose the DSC problem into a set of independent sub-problems and solve it in a distributed fashion. Simulation results show the availability and superiority of the proposed digital-twin assisted root cause analysis algorithm for NFV environment.