Performance Analysis of Task Scheduling Algorithms for Energy Efficiency in Mobile Cloud Computing Environment

Abstract

Today different types of mobile applications are developed to bring direct communication between the clients and consumers for gaming, business, marketing, education etc. However, the use of these applications can lead to faster battery drain in the mobiles due to background running of some applications even after it is closed. Scheduling of the some tasks over the cloud can helps to mitigate the concerns of fast battery drain of mobile (and is referred as mobile cloud computing (MCC)). However, processing of all tasks over the cloud takes leads to higher execution time due to unnecessary computations. Thus, this manuscript introduces a dynamic approach of hamming distance termination (HDT) (D-HDT) and Genetic Algorithm (GA) approach to achieve less computational time and energy minimization by avoiding unnecessary computation during task scheduling over the cloud. The significance of the proposed approach is that it follows the random allocation and HDT to schedule the tasks more quickly. Also, D-HDT approach able to manage the larger task scheduling over the cloud without degrading the computational efficiency. A comparative analysis is conducted between D-HDT and GA approach by considering the energy minimization in the MCC under computational time constraints. The outcome analysis suggests that the proposed system results in better computational efficiency by taking less number of iterations.