A Convex-based Computation Offloading Mechanism in Vehicular Edge Computing

Abstract

Mobile edge computing (MEC) has been an effective paradigm for supporting computation-intensive vehicular applications by offloading resources at network edge. However, due to the unique characteristics of vehicular networks, including high mobility of vehicles, dynamic distribution of vehicle densities and heterogeneous network resources, it is still challenging to implement efficient computation offloading mechanism in MEC-assisted vehicular networks. In this paper, we investigate a novel scenario of computation offloading in MEC-assisted architecture, where task upload coordination between multiple vehicles, task migration between MEC/cloud servers and heterogeneous computation capabilities of MEC/cloud severs, are comprehensively investigated. On this basis, we formulate cooperative computation offloading (CCO) problem by modeling the procedure of task upload, migration and computation based on queuing theory, which aims at minimizing the delay of task completion. To tackle the CCO problem, we propose a probabilistic computation offloading (PCO) algorithm, which achieves the optimal allocation probability in an iterative way based on a convex framework called Alternating Direction Method of Multipliers (ADMM). Last but not the least, we implement the simulation model. The comprehensive simulation results show the superiority of the proposed algorithm under a wide range of scenarios.