Vehicular datacenter modeling for cloud computing: Considering capacity and leave rate of vehicles

In this paper, we propose a vehicular datacenter model in a parking lot, where vehicles can be considered as a resource for cloud computing. One of the crucial issues facing the vehicular datacenter is failures caused by arrival and departure of dynamic resources. These failures result in performance degradation of the execution time because the task must be restarted. In order to reduce execution time and mitigate the effects of uncertainty, we propose a vehicular datacenter model that makes use of a checkpoint mechanism. We first characterize the dynamic vehicles in parking lots considering each vehicle's capacity and leave rate. We derive the expected execution time to analyze the characteristics of vehicles and propose a resource selection strategy based on that time. We also derive the optimal number of checkpoints for each vehicle that maximizes the efficiency of the checkpoint. We demonstrate the results of our analysis through various evaluations.