Online Learning Enabled Task Offloading for Vehicular Edge Computing
Rui Zhang, Peng Cheng, Zhuo Chen, Sige Liu, Yonghui Li, Branka Vucetic
Abstract
Vehicular edge computing pushes the cloud computing capability to the distributed network edge nodes, enabling computation-intensive and latency-sensitive computing services for smart vehicles through task offloading. However, the inherent mobility introduces fast variation of network structure, which are usually unknown a priori. In this letter, we formulate the vehicular task offloading as a mortal multi-armed bandit problem, and develop a new online algorithm to enable distributed decision making on the node selection. The key is to exploit the contextual information of edge nodes and transform the infinite exploration space to a finite one. Theoretically, we prove that the proposed algorithm has a sublinear learning regret. Simulation results verify its effectiveness.