Multi-Task Offloading over Vehicular Clouds under Graph-based Representation

TL;DR

This paper introduces a graph-based multi-task offloading framework for vehicular clouds, balancing task completion time and data costs, with solutions tailored for different traffic scenarios.

Contribution

It presents a novel graph modeling approach for tasks and VCs, along with algorithms for optimal and low-complexity offloading under various traffic conditions.

Findings

Optimal solutions in low-traffic scenarios

Low-complexity algorithms for rush-hour cases

Proven improvements over baseline methods

Abstract

Vehicular cloud computing has emerged as a promising paradigm for realizing user requirements in computation-intensive tasks in modern driving environments. In this paper, a novel framework of multi-task offloading over vehicular clouds (VCs) is introduced where tasks and VCs are modeled as undirected weighted graphs. Aiming to achieve a trade-off between minimizing task completion time and data exchange costs, task components are efficiently mapped to available virtual machines in the related VCs. The problem is formulated as a non-linear integer programming problem, mainly under constraints of limited contact between vehicles as well as available resources, and addressed in low-traffic and rush-hour scenarios. In low-traffic cases, we determine optimal solutions; in rush-hour cases, a connection-restricted randommatching-based subgraph isomorphism algorithm is proposed that presents low computational complexity. Evaluations of the proposed algorithms against greedy-based baseline methods are conducted via extensive simulations.