Bi-LSTM/GRU-based anomaly diagnosis for virtual network function instance

Abstract

In edge-cloud networks, Network Function Virtualization (NFV) technology provides users with high-performance Virtual Network Functions (VNFs) to replace dedicated hardware devices. In this paper, we focus on the VNF instance (VNFI) anomaly diagnosis (AD). In large-scale production data centers, VNFI anomalies are inevitable, causing degradation in network service performance, while accurate VNFI AD can minimize the losses. The AD task can be abstracted as a multi-classification problem, where the VNFI state is classified as normal or a specific fault type. Existing studies have not proposed mature VNFI AD systems and feature selection criteria, and the accuracy of AD tasks can still be improved. To address these gaps, we first design the Distributed Online Anomaly Diagnosis and Broadcasting (DOADB) system, providing online VNFI AD daemons, offline parameter training, and anomaly broadcasting. Then, we establish VNFI feature selection criteria and construct a compelling VNFI feature dataset containing simulated and real-time data for each VNFI. Based on the DOADB system and feature dataset, we adopt the Bi-LSTM and Bi-GRU networks to realize the AD multi-classification algorithm, leveraging the bidirectional network structure and long-term memory to enhance AD accuracy. Experimental results demonstrate that the proposed system and algorithm can provide effective AD daemons for VNFIs and outperform existing algorithms in both accuracy and macro F1-score metrics.