Learning Based Fluctuation-aware Computation offloading for Vehicular Edge Computing System
Zhitong Liu, Xuefei Zhang, Jian Zhang, Dian Tang, Xiaofeng Tao
Abstract
Vehicular edge computing (VEC) is a promising paradigm to satisfy the ever-growing computing demands by offloading computation tasks to vehicles equipped with computing servers. One of the major challenges in VEC system is the highly dynamic and uncertain moving route of vehicular servers. In order to address this challenge, a particular kind of vehicles (i.e., buses) is adopted as moving servers with the pre-designated route and timetable. On this basis, a fluctuation-aware learningbased computation offloading (FALCO) algorithm based on multi-armed bandit (MAB) theory is proposed. Specifically, base stations (BSs) are regarded as agents to learn the state of moving server so as to construct a stable observation set in the dynamic vehicular environment. In addition, the softmax function is applied to indicate the probability for each decision, which provides more flexible policies for obtaining better results. Simulation results demonstrate that our proposed FALCO algorithm can improve delay performance compared with the other existing learning algorithms.