A Task Offloading Scheme in Vehicular Fog and Cloud Computing System

Abstract

Vehicular fog and cloud computing (VFCC) system, which provides huge computing power for processing numerous computation-intensive and delay sensitive tasks, is envisioned as an enabler for intelligent connected vehicles (ICVs). Although previous works have studied the optimal offloading scheme in the VFCC system, no existing work has considered the departure of vehicles that are processing tasks, i.e., the occupied vehicles. However, vehicles leaving the system with uncompleted tasks will affect the overall performance of the system. To solve the problem, in this paper, we study the optimal offloading scheme that considers the departure of occupied vehicles. We first formulate the task offloading problem as an semi-Markov decision process (SMDP). Then we design the value iteration algorithm for the SMDP to maximize the total long-term reward of the VFCC system. Finally, the numerical results demenstrate that the proposed offloading scheme can achieve higher system reward than the greedy scheme. INDEX TERMS Vehicular fog computing, cloud computing, task offloading, semi-Markov decision process.

Resource pooling in vehicular fog computing

“…It will incur the inefficiency and low success rate of VFC and yet few of works have considered it in existing works. To tackle this problem, authors in [17] investigate the offloading scheme in VFC system with consideration of the departure of vehicles with offloaded tasks. They formulate it as a semi-Markov decision process that is solved by a designed value iteration algorithm targeted at optimizing the total reward of the system.…”

Dispersed Computing for Tactical Edge in Future Wars: Vision, Architecture, and Challenges

“…Gao et al [17] aimed to minimize the time-average power consumptions with stability guarantee for all queues in the system and exploited unique problem structures and proposed an efficient and distributed predictive offloading and resource allocation scheme for multitiered FC. Wu et al [18] designed a value iteration algorithm of the semi-Markov decision process to maximize the total long-term reward for the task offloading problem of the vehicular fog and cloud computing system. Zeng et al [19] considered a FC framework to support a softwaredefined embedded system, where task images lay in the storage server while computations can be conducted on either an embedded device or a computation server, which is significant to design an efficient task scheduling and resource management strategy with minimized task completion time for promoting the user experience.…”

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

“…To solve the BLP, they exploit distributed deep Q learning method, where the state space and the action space represent tasks' requirements and the binary offloading decision respectively, and use the optimization objective of BLP to be represented as the reward. This work assumes that there is no departure of vehicles in the system even though vehicles are highly dynamic, which limit its application as it would affect the overall performance when a vehicle leaves the system with uncompleted tasks [134].…”