Toward Approximating Job Completion Time in Vehicular Clouds

Abstract

Motivated by the phenomenal success of conventional cloud computing, vehicular clouds (VCs) were introduced as a group of vehicles whose corporate computing, sensing, communication, and physical resources can be coordinated and dynamically allocated to authorized users. Just as in conventional clouds, job completion time ranks high among the fundamental quantitative performance figures of merit. Recently, the authors have analytically investigated the effect of a redundancy-based job assignment on job completion time in VCs. However, these analytical expressions require full knowledge of the distribution functions of various random variables contributing to job completion time. In a practical context, the data center manager does not know these distribution functions. Instead, using accumulated empirical data, they may be able to estimate the first and, perhaps, the second moments of these random variables. Yet, getting a handle on the expected job completion time is a very important problem that must be addressed. Consequently, it is of great theoretical interest and practical relevance to be able to approximate the expression of job completion time. With this in mind, the main contribution of this paper is to offer easy-to-compute approximations of job completion time when estimates of the first or the first two moments of the intervening random variables are available. A comprehensive set of simulations have shown that our approximations are very close to the analytical predictions.