Modeling and predicting fault tolerance in Vehicular Cloud Computing

Statistics show that most vehicles spend many hours per day in a parking garage, parking lot, or driveway. At the moment, the computing resources of these vehicles are untapped. Inspired by the success of conventional cloud services, a group of researchers have recently introduced the concept of a Vehicular Cloud. The defining difference between vehicular and conventional clouds lie in the distributed ownership and, consequently, the unpredictable availability of computational resources. As cars enter and leave the parking lot, new computational resources become available while others depart creating a dynamic environment where the task of efficiently assigning jobs to cars becomes very challenging. In this paper we propose a fault-tolerant job assignment strategy, based on redundancy, that mitigates the effect of resource volatility of resource availability in vehicular clouds. We offer a theoretical analysis of the mean time to failure of this strategy. A comprehensive set of simulations have confirmed the accuracy of our theoretical prediction.