Delay Sensitive Task Offloading in the 802.11p Based Vehicular Fog Computing Systems

TL;DR

This paper proposes an optimal task offloading scheme for vehicular fog computing systems using 802.11p, aiming to maximize long-term rewards by balancing delay and resource considerations.

Contribution

It introduces a semi-Markov decision process model and an iterative Bellman equation-based algorithm for delay-sensitive task offloading in vehicular fog computing.

Findings

The proposed scheme effectively reduces delay in task offloading.

Numerical results show improved long-term system rewards.

The method outperforms baseline approaches in simulations.

Abstract

Vehicular fog computing (VFC) is envisioned as a promising solution to process the explosive tasks in autonomous vehicular networks. In the VFC system, task offloading is the key technique to process the computation-intensive tasks efficiently. In the task offloading, the task is transmitted to the VFC system according to the 802.11p standard and processed by the computation resources in the VFC system. The delay of task offloading, consisting of the transmission delay and computing delay, is extremely critical especially for some delay-sensitive applications. Furthermore, the long-term reward of the system (i.e., jointly considers the transmission delay, computing delay, available resources, and diversity of vehicles and tasks) becomes a significantly important issue for providers. Thus, in this article, we propose an optimal task offloading scheme to maximize the long-term reward of the system where 802.11p is employed as the transmission protocol for the communications between vehicles. Specifically, a task offloading problem based on a semi-Markov decision process (SMDP) is formulated. To solve this problem, we utilize an iterative algorithm based on the Bellman equation to approach the desired solution. The performance of the proposed scheme has been demonstrated by extensive numerical results.