Real-Time Analysis of Multiple Root Causes for Anomalies Assisted by Digital Twin in NFV Environment

Abstract

Network Function Virtualization (NFV) is a promising paradigm that 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. In NFV environment, anomalies occurred in virtual machines (VMs) can be caused by faulty components of their hosting servers or anomaly propagation from other ones. If the performance of VMs degrades, we need to find out the root causes accurately, i.e., locate the exact faulty components, to recover the networks as soon as possible. In this paper, we first use digital twin to establish a virtual instance of the physical network to capture the real-time anomaly-fault dependency relationship. When the network environment changes, transfer learning is leveraged to utilize the learned knowledge of dependency relationship in historical periods to avoid huge time and computation cost of learning from scratch. Assisted by the learned dependency relationship, a dynamic set-covering (DSC) based root caused analysis problem is formulated and modeled with a set of parallel hidden Markov models to capture the dynamics of component states, which can best explain the sequence of anomalous VMs. We use alternating direction method of multipliers to decompose the DSC problem into a set of independent sub-problems and solve it in a distributed fashion. Since the state variables of each component in the DSC problem are coupled between any two successive time epochs, each sub-problem is solved by the Viterbi decoding and an incremental function is designed to construct the feasible solution that covers all anomalous VMs. Simulation results show the availability and superiority of the digital-twin assisted root cause analysis algorithm for NFV environment.