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Vehicular edge computing is a new distributed processing architecture thatexploits the revolution in the processing capabilities of vehicles to provideenergy efficient services and low delay for Internet of Things (IoT)-basedsystems. Edge computing relies on a set of distributed processing nodes (i.e.vehicles) that are located close to the end user. In this paper, we consider avehicular edge cloud (VEC) consisting of a set of vehicle clusters that form atemporal vehicular cloud by combining their computational resources in thecluster. We tackle the problem of processing allocation in the proposedvehicular edge architecture by developing a Mixed Integer Linear Programming(MILP) optimization model that jointly minimizes power consumption, propagationdelay, and queuing delay. The results show that the closer the processing node(PN) is to the access point (AP), the lower the power consumption and delay, asthe distance and number of hops affect the propagation delay and queuing delay.However, the queuing delay at the AP becomes a limiting factor when it operatesat a low service rate compared to the traffic arrival rate. Thus, processingtasks at the vehicular nodes (VN) was avoided whenever the objective functionincluded queueing delay and the AP operated at a low service rate. Increase inthe AP service rate results in a lower queuing delay and better VN utilization.

Optimization of Processing Allocation in Vehicular Edge Cloud based Architecture