Online Learning for Computation Peer Offloading with Semi-bandit Feedback
Hongbin Zhu, Kai Kang, Xiliang Luo, Hua Lin Qian · 2019
Fog computing is emerging as a promising paradigm to perform distributed, low-latency computation. Efficient computation peer offloading is critical to fully utilize the computational resources in fog networks. In this paper, we consider computation peer offloading problem in a fog network with time-varying stochastic time of arrival tasks and channel conditions. Such time-varying conditions are not available to all fog nodes. In order to minimize the latency of accomplishing arrival tasks, we propose an online algorithm based on combinatorial upper confidence bounds algorithm with two uncertain variables under the non-stationary bandit model. The proposed computation offloading policy is optimized based on historical feedback. The performance of the proposed scheme is validated through numerical simulations.