Learning-Based Task Offloading for Delay-Sensitive Applications in Dynamic Fog Networks

Abstract

Fog computing has the potential to liberate the computation-intensive mobile devices by task offloading. In this paper, we propose an online learning based task offloading algorithm for delay-sensitive applications in dynamic fog networks, which combines with the Combinatorial Multi-Armed Bandits (CMAB) framework. First, the proposed algorithm learns the sharing computing resources of fog nodes at a negligible computational cost. Then, we aim to minimize the task's offloading latency by jointly optimizing the task allocation decision and the spectrum scheduling. Finally, simulation results show that the proposed algorithm achieves much better delay performance than the traditional Upper Confidence Bound (UCB) algorithm and maintains ultra-low offloading delay in dynamic system state.