Cascading Segmented and Successive Inheritance Restoration Methods for NFV-Based Fog Computing

Abstract

Fog computing enhanced by network function virtualization (NFV) is pivotal for latency-sensitive applications such as autonomous vehicles and real-time healthcare. However, node failures attributed to hardware malfunctions, cyberattacks, or natural disasters disrupt services, degrade performance, and violate the service function chain (SFC) integrity. Existing solutions suffer from progressive redundancy, lack ultra-low latency restoration, efficient resource utilization, and scalability under high traffic. Hence, this work proposes novel post-fault restoration methods, termed as cascading segmented (CS) and successive inheritance (SI). The first partitions SFCs into localized segments and maps them onto fog nodes, while embedding their backup VNFs between the segments. The second employs a rolling mapping strategy for single-hop failover, where each node hosts both a primary VNF and the backup of its predecessor VNF. If failure occurs, restoration is immediate by discarding the failed node(s) and progressing forward with the SFC route without establishing a new path. Remarkably, the restored paths are shorter than the pre-fault primary paths. The methods yield noticeable restoration rates, reduced delays and costs compared to conventional methods, for single, dual-clustered, and dual-distributed failures occurring under high-congestion scenarios.