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Energy Efficient Processing Allocation in Opportunistic Cloud-Fog-Vehicular Edge Cloud Architectures

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Document pages: 23 pages

Abstract: This paper investigates distributed processing in Vehicular Edge Cloud(VECs), where a group of vehicles in a car park, at a charging station or at aroad traffic intersection, cluster and form a temporary vehicular cloud bycombining their computational resources in the cluster. We investigated theproblem of energy efficient processing task allocation in VEC by developing aMixed Integer Linear Programming (MILP) model to minimize power consumption byoptimizing the allocation of different processing tasks to the availablenetwork resources, cloud resources, fog resources and vehicular processingnodes resources. Three dimensions of processing allocation were investigated.The first dimension compared centralized processing (in the central cloud) todistributed processing (in the multi-layer fog nodes). The second dimensionintroduced opportunistic processing in the vehicular nodes with low and highvehicular node density. The third dimension considered non-splittable tasks(single allocation) versus splittable tasks (distributed allocation),representing real-time versus non real-time applications respectively. Theresults revealed that a power savings up to 70 can be achieved by allocatingprocessing to the vehicles. However, many factors have an impact on the powersaving such the vehicle processing capacities, vehicles density, workload size,and the number of generated tasks. It was observed that the power saving isimproved by exploiting the flexibility offered by task splitting among theavailable vehicles.

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