Allocation of Computation-Intensive Graph Jobs over Vehicular Clouds
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Recent years have witnessed dramatic growth in smart vehicles and computation-intensive jobs, which pose new challenges to the provision of efficient services related to the internet of vehicles. Graph jobs, in which computations are represented by graphs consisting of components (denoting either data sources or data processing) and edges (corresponding to data flows between the components) are one type of computation-intensive job warranting attention. Limitations on computational resources and capabilities of on-board equipment are primary obstacles to fulfilling the requirements of such jobs. Vehicular clouds, formed by a collection of vehicles allowing jobs to be offloaded among vehicles, can substantially alleviate heavy on-board workloads and enable on-demand provisioning of computational resources. In this article, we present a novel framework for vehicular clouds that maps components of graph jobs to service providers via opportunistic vehicle-to-vehicle communication. Then, graph job allocation over vehicular clouds is formulated as a form of non-linear integer programming with respect to vehicles' contact duration and available resources, aiming to minimize job completion time and data exchange cost. The problem is approached from two scenarios: low-traffic and rush-hours. For the former, we determine the optimal solutions for the problem. In the latter case, given intractable computations for deriving feasible allocations, we propose a novel low-complexity randomized algorithm. Numerical analysis and comparative evaluations are performed for the proposed algorithms under different graph job topologies and vehicular cloud configurations.
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