1 research outputs found
Multi-Task Offloading over Vehicular Clouds under Graph-based Representation
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