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arXiv · 1912.06243

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

Minghui Liwang·Zhibin Gao·Seyyedali Hosseinalipour·Huaiyu Dai
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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.

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Minghui Liwang, Zhibin Gao, Seyyedali Hosseinalipour, Huaiyu Dai. 2019-12-12. Multi-Task Offloading over Vehicular Clouds under Graph-based Representation. https://arxiv.org/abs/1912.06243

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