Reasoning about Mean Time to Failure in Vehicular Clouds

In this work, we envision a vehicular cloud involving cars in the parking lot of a major airport. The owners of these cars are typically on travel for several days, providing a pool of cars that can serve as the basis for a data center at the airport. We assume that the cars that participate in the vehicular cloud are plugged into a standard power outlet and are provided wireless connection to a central server at the airport. The defining difference between vehicular and conventional clouds lies 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 cars to jobs becomes very challenging. Our main contribution is a family of redundancy-based job assignment strategies that mitigate the effect of resource volatility in vehicular clouds. We offer a theoretical analysis of the mean time to failure of these strategies. A comprehensive set of simulations has confirmed the accuracy of our theoretical predictions.