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Mathematical Problems in Engineering
Volume 2015, Article ID 969027, 13 pages
Research Article

An Adaptive Procedure for Task Scheduling Optimization in Mobile Cloud Computing

Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Republic of Korea

Received 3 October 2014; Accepted 11 March 2015

Academic Editor: Oleg V. Gendelman

Copyright © 2015 Pham Phuoc Hung and Eui-Nam Huh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Nowadays, mobile cloud computing (MCC) has emerged as a new paradigm which enables offloading computation-intensive, resource-consuming tasks up to a powerful computing platform in cloud, leaving only simple jobs to the capacity-limited thin client devices such as smartphones, tablets, Apple’s iWatch, and Google Glass. However, it still faces many challenges due to inherent problems of thin clients, especially the slow processing and low network connectivity. So far, a number of research studies have been carried out, trying to eliminate these problems, yet few have been found efficient. In this paper, we present an enhanced architecture, taking advantage of collaboration of thin clients and conventional desktop or laptop computers, known as thick clients, particularly aiming at improving cloud access. Additionally, we introduce an innovative genetic approach for task scheduling such that the processing time is minimized, while considering network contention and cloud cost. Our simulation shows that the proposed approach is more cost-effective and achieves better performance compared with others.