Enhancing Reliability and Availability Through Redundancy in Vehicular Clouds

Abstract

The past eight years have seen the emergence of vehicular clouds as a topic of research in its own right. Vehicular clouds were inspired by the insight that presentday vehicles feature an impressive array of on-board compute, storage and sensing capabilities. These on-board capabilities are a vast untapped resource that, at the moment, is wasted. One of the defining ways in which vehicular clouds differ from conventional clouds is resource volatility. As vehicles enter and leave the cloud, new compute resources become available while others depart, creating a volatile environment where the tasks of enhancing reliability and availability become very challenging. It is intuitively clear that the longer and more predictable the vehicle residency times in the cloud are, the easier it is to ensure reliability and system availability. In this work we look at vehicular clouds with short and unpredictable vehicular residency times. We propose to enhance the reliability and availability of these types of vehicular clouds through a family of redundancy-based job assignment strategies that attempt to mitigate the effect of resource volatility. We offer a theoretical prediction of the Mean Time To Failure (MTTF) of these strategies. We also show how to fine-tune the granularity of the redundancy in order to meet QoS requirements specified in terms of a minimum MTTF for a given user job. Extensive simulations, using vehicle residency data derived from shopping mall statistics, have confirmed the accuracy of our analytical predictions.