An efficient model for vehicular cloud computing with prioritizing computing resources

Abstract

In recent years, even though there has been a lot of progress in automotive industry and their offered services but not all of their computational capacities have yet been used. The vehicle’s onboard computation capacity is underutilized, the power which can be used efficiently and it significantly reduces energy consumption. Considering the novelty of Vehicular Cloud Computing (VCC), the problems like its real cost and different kind of resource allocations in different applications remain an unexplored area. The mentioned problems with the global need for energy management have motivated us to propose an efficient model that considers expenses and response times which also appropriately utilizes onboard computation capacity for VCC. The proposed model is using VCC in a manner that the onboard computational capability is fully used. Since offloading tasks to Vehicular Cloud and remote cloud have additional cost, the goal is to do tasks locally and offload fewer tasks to the vehicular cloud and remote cloud. The model prioritizes computing resources and uses the onboard computing power, which was often ignored in the previous studies. Onboard computing resource provides reasonable response time and makes the model economically beneficial. After the model presentation and structure, simulation of the proposed model with the CloudAnalyst software and the results are presented and compared with appropriate references at the end. The results show that the proposed model can show a view of VCC with its advantages and disadvantages in a practical manner, it also displays the statistical data which compared to other scenarios, shows the superiority of the model. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.