A Probabilistic Approach for Cooperative Computation Offloading in MEC-Assisted Vehicular Networks

DOI number:10.1109/TITS.2020.3017172

Affiliation of Author(s):Southwest Jiaotong Univ, Sch Informat Sci & Technol

Journal:IEEE Transactions on Intelligent Transportation Systems

Key Words:ServersTask analysis,Computational modeling,Computer architecture,DelaysVehicle dynamics,Processor scheduling,Vehicular networks,mobile edge computing,computation offloading,queuing theory,optimization

Abstract:Mobile edge computing (MEC) has been an effective paradigm for supporting computation-intensive applications by offloading resources at network edge. Especially in vehicular networks, the MEC server, is deployed as a small-scale computation server at the roadside and offloads computation-intensive task to its local server. However, due to the unique characteristics of vehicular networks, including high mobility of vehicles, dynamic distribution of vehicle densities and heterogeneous capacities of MEC servers, it is still challenging to implement efficient computation offloading mechanism in MEC-assisted vehicular networks. In this article, 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 enables MEC server to independently make online scheduling based on the derived allocation probability. Specifically, the PCO transforms the objective function into augmented Lagrangian and achieves the optimal solution 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.

Co-author:Penglin Dai,Kaiwen Hu,Xiao Wu,Huanlai Xing,Fei Teng,Zhaofei Yu

Document Code:10.1109/TITS.2020.3017172

Volume:23

Issue:2

Page Number:899-911

ISSN No.:1524-9050

Translation or Not:no

Date of Publication:2022-02-16

Included Journals:SCI