A Distributed Algorithm for Task Offloading in Vehicular Networks With Hybrid Fog/Cloud Computing
针对车联网中车辆高移动性和密度不均导致的任务卸载性能下降问题，提出一种结合ADMM和粒子群优化的分布式算法，在三层服务架构下最小化执行延迟、能耗和支付成本的加权和。
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.