Reasoning About Expected Job Completion Time in Dynamic Vehicular Clouds

Abstract

Roughly a decade ago, inspired by the phenomenal success of cloud computing, a group of researchers have defined Vehicular Clouds as a group of vehicles whose sensing, communication, and computing resources can be coordinated and allocated to authorized users. While both conventional and Vehicular Clouds are instances of utility computing, a number of important characteristics set vehicular clouds apart from their conventional counterparts. These characteristics include the mobility of vehicles and the volatility of resources that fluctuate with the arrival and departure of vehicles. As in the conventional version of cloud computing, approximating job completion time is one of the key performance metrics of interest. Unfortunately, estimating job completion time with any degree of accuracy and confidence requires complete knowledge of the distributions of several relevant random variables. Typically, however, these probability distributions are not known. Luckily, in many cases of practical relevance, accumulated empirical evidence allows to estimate the first few moments of these random variables. The main contribution of this paper is to offer an accurate approximation of the expected job completion time in Dynamic Vehicular Clouds built on top of vehicles on a highway. For this purpose, we rely on empirical estimates of the first moment of the user job execution time in the absence of any overhead attributable to the Dynamic Vehicular Cloud itself. Our extensive simulations have confirmed that our approximations of the expected job execution time are very accurate.