Distributed processing in vehicular cloud networks

Vehicular Clouds processing is a new field of research that aims to exploit the vehicles' onboard computational resources as a part of a cooperative distributed cloud computing environment. In this paper, we propose a vehicular cloud network architecture where a group of vehicles near a traffic light cluster and form a temporal vehicular cloud by aggregating their computational resources in that cluster. The goal of the proposed architecture is to minimize the processing and network power consumed in the data center of a cloud operator. To this end, arriving processing tasks are optimally assigned to the centralized cloud and/or the formed vehicular clouds to reduce the total power consumption of the centralized cloud by reducing its average processing workload and network traffic. Furthermore, task assignment among vehicular clouds is constrained by tasks completion time. Our proposed system is analyzed using a mixed integer linear programming (MILP) model where two task assignment approaches were considered: single task assignment and distributed task assignment. In the first approach, each task is not split among multiple clouds, while splitting is allowed in the second approach. It was found that the power consumption of the centralized cloud is reduced by 45% (in the first approach) and 60% (in the second approach) compared to the case where all tasks are assigned to the centralized cloud only. The higher power saving of the centralized cloud in the second approach comes from the ability of vehicular clouds to host more processing workload, an average of 37% more workload, compared to the single task assignment approach.