ESFMTO: A reliable task offloading strategy based on edge server failure model in IIoT

The extensive use of automation equipment and sensors in the Industrial Internet of Things (IIoT) has led to a significant increase in data volume, which has placed higher demands on the real-time processing capability. Edge computing enables real-time response and rapid decision-making by offloading data processing to the edge of the network. However, the complexity of the industrial production environment leads to edge server failures, which seriously affects the system stability and security. To address this issue, this paper develops an edge server failure model for IIoT, analyzing the interaction between the hardware failure and the container failure during the failure occurrence and recovery. Further, based on the edge server failure model, a task offloading strategy named ESFMTO is proposed, which employs the SAC-BNN (Soft Actor-Critic with Bayesian Neural Network) algorithm. The probability distribution of the task completion time is updated through the Bayesian Neural Network (BNN), accurately evaluating the Conditional Value at Risk (CVaR). During the training process of Deep Reinforcement Learning (DRL), a perturbation neural network is introduced to perturb the input state, which enhances the system robustness under uncertain failure conditions. Unlike existing approaches that often assume each edge server hosts a single container, the paper explicitly considers multi-container deployment in IIoT, bridging the gap between theoretical assumptions and real-world industrial requirements. Experimental results demonstrate that SAC-BNN outperforms existing methods in dealing with the edge server failure, reducing CVaR by at least around 0.9 and improving task completion rates by at least 13.86% compared to the baseline algorithms.