A Distributed Algorithm for Task Offloading in Vehicular Networks With Hybrid Fog/Cloud Computing

Impact Factor:11.471

DOI number:10.1109/TSMC.2021.3097005

Affiliation of Author(s):Southwest Jiaotong Univ, Sch Comp & Artificial Intelligence

Journal:IEEE Transactions on Systems, Man, and Cybernetics: Systems

Key Words:Task analysis,Computational modeling,Cloud computing,Delays,Servers,Computer architecture,Energy consumption,Distributed scheduling,fog computing,task offloading,vehicular networks

Abstract:Fog computing has been an effective paradigm of real-time applications in the IoT area, which enables task offloading at network edge devices. Particularly, many emerging vehicular applications require real-time interaction between the terminal users and computation servers, which can be implemented in fog-based architecture. However, it is still challenging to apply fog computing in vehicular networks due to high mobility of vehicles and uneven distribution of vehicle density, which may result in performance degradation, such as unbalanced workload and unexpected task failure. In this article, we investigate a new service scenario of task offloading under a three-layer service architecture, where the resources of vehicular fog (VF), fog server (FS), and central cloud (CC) are utilized in a cooperative way. On this basis, we formulate the probabilistic task offloading (PTO) problem by synthesizing task transmission, computation, and result retrieval, as well as characterizing the heterogeneity of computation servers. The objective of the PTO is to minimize the weighted sum of execution delay, energy consumption, and payment cost. To resolve the PTO problem, we propose a comprehensive task offloading algorithm by combining the alternating direction method of multipliers (ADMMs) and particle swarm optimization (PSO), called ADMM-PSO. The basic idea of the ADMM-PSO is to divide the PTO problem into multiple unconstrained subproblems and achieve the optimal solution in the form of an iterative coordination process. For each iteration, the solution is achieved by solving each subproblem with the PSO and updated based on a designed rule, which is able to converge to the optimal solution when the stop criterion is satisfied. Finally, we build the simulation model and implement the proposed algorithm for performance evaluation. The simulation results demonstrate the superiority of the proposed algorithm under a wide range of service scenarios.

Co-author:Zongkai Liu,Penglin Dai,Huanlai Xing,Zhaofei Yu,Wei Zhang

Document Code:10.1109/TSMC.2021.3097005

Volume:52

Issue:7

Page Number:4388-4401

ISSN No.:2168-2216

Translation or Not:no

Date of Publication:2021-07-31

Included Journals:SCI