CorrFault-GNN: Topology-Aware Correlated Failure Prediction and Proactive Fault-Tolerant Scheduling for Fog Computing
TL;DR
The results show that CorrFault-GNN improves task success, latency, energy efficiency, and deadline satisfaction compared with representative reactive, proactive, and learning-based baselines, and that its advantage grows as infrastructure sharing and failure correlation increase.
Abstract
Fog computing enables low-latency processing for Internet of Things (IoT) applications by moving computation closer to end devices. However, fog infrastructures are often deployed on commodity and geographically distributed resources, making them vulnerable to correlated node failures caused by shared power systems, network switches, cooling units, or physical proximity. Most existing fault-tolerant scheduling methods treat node failures as independent events, which limits their ability to anticipate multi-node outages in shared-infrastructure fog environments. This paper presents CorrFault-GNN, a topology-aware fault-tolerant scheduling framework for predicting and mitigating correlated failures in fog computing. The framework models the fog infrastructure as a dynamic weighted graph that captures power, network, and geographic dependencies among fog nodes. A Temporal Graph Convolutional Network (T-GCN) learns spatial and temporal failure patterns and predicts node-level failure risks one scheduling epoch ahead. These predictions drive a proactive migration module that moves tasks away from high-risk nodes, while a Criticality-Aware Reactive Fallback handles unexpected failures. The framework is evaluated in three-tier IoT–Fog–Cloud simulations with correlated failure traces derived from cloud failure data. The results show that CorrFault-GNN improves task success, latency, energy efficiency, and deadline satisfaction compared with representative reactive, proactive, and learning-based baselines, and that its advantage grows as infrastructure sharing and failure correlation increase.