Multi-Armed Bandit Based Task Offloading By Probabilistic V2X Communication in Vehicle Edge Cloud System
Jiayou Xie, Qi Si, Yulian Tang · 2020
Compared with traditional cloud computing, service providers in edge computing are closer to the requester of the demand, and can better meet the requirements of communication transmission delay. The Internet of Vehicles is one of the important application scenarios of 5G, the vehicle edge nodes of it have a large number of free resources, but its highly dynamic characteristics will lead to environmental uncertainty, which will affect task offloading decisions. In this article, in the vehicle edge cloud computing system, a V2V or V2I communication method is selected based on probability to achieve low-latency and high-reliability communication requirements, and the Multi-Armed Bandit (MAB) method is used to learn the surrounding environment of the vehicle so that it can adapt dynamically changing environments to make task offloading decisions. After a large number of simulations, our solution can adaptively offload tasks to minimize the expected delay and obtain a sub-linearly increasing learning regret curve in a high-speed mobile vehicle networking scenario.