Distributed Learning-Based Matching for Task Offloading in Dynamic Fog Computing Networks
Hoa Tran‐Dang, Dong‐Seong Kim · 2025
Task offloading in dynamic fog computing networks (FCNs) presents significant challenges due to continuously changing task requirements and fluctuating computing resources. This paper proposes Distributed Learning-based Matching (DL-MATCH) framework that enables adaptive task offloading through a multi-stage, one-to-many matching mechanism. In DL-MATCH, task nodes (TNs) employ distributed learning to estimate the acceptance probability of helper nodes (HNs) based on historical interactions. By leveraging multi-stage interaction and reward-based learning, DL-MATCH efficiently adapts to unknown preferences on both sides, optimizing task allocation while minimizing offloading delays. Simulation results demonstrate that DL-MATCH outperforms baseline approaches in terms of task completion rate and resource utilization, making it a promising solution for dynamic task offloading in fog computing environments.