Approximating Expected Job Completion Time in Dynamic Vehicular Clouds

Abstract

Motivated by the success of conventional cloud computing, vehicular clouds were introduced as a group of vehicles whose corporate computing, sensing, communication and physical resources can be coordinated and dynamically allocated to authorized users. One of the attributes that set vehicular clouds apart 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 task of reasoning about fundamental performance metrics becomes very challenging. Just as in conventional clouds, job completion time ranks high among the fundamental quantitative performance figures of merit. With this in mind, the main contribution of this work is to offer easy-to-compute approximations of job completion time in a dynamic vehicular cloud model involving vehicles on a highway. We assume estimates of the first moment of the time it takes the job to execute without any overhead attributable to the working of the vehicular cloud. A comprehensive set of simulations have shown that our approximations are very accurate.