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JACIII Vol.20 No.2 pp. 262-270
doi: 10.20965/jaciii.2016.p0262
(2016)

Paper:

Third-Party Broker-Based Resource Management in Mobile Computing

Yong-Hua Xiong*1,*2, Lei Li*3, Ke-Yuan Jiang*2, and Hong Yu*4

*1School of Automation, China University of Geosciences
Lumo Road, Wuhan, Hubei 430074, China

*2Department of Computer Information Technology & Graphics, Purdue University Calumet
Hammond 46323, USA

*3School of Information Science and Engineering, Central South University
Yuelu Mountain, Changsha, Hunan 410083, China

*4School of Automation, Huazhong University of Science and Technology
Yujiashan, Wuhan, Hubei 430074, China

Received:
November 10, 2015
Accepted:
December 10, 2015
Online released:
March 18, 2016
Published:
March 20, 2016
Keywords:
resource management optimization, broker technology, mobile computing
Abstract

During the last decade, mobile user experience created a new profit structure, thanks to the massive growth in the use of smart mobile devices and the large amount of mobile data it has generated. Owing to this growth, mobile resource management (e.g., bandwidth, computing resources, etc.) has become a big challenge in mobile computing. This article proposes a broker technology that is based on a resource management optimization method and that effectively utilizes local area network (LAN) resources to perform computing tasks for a single mobile client. This technology thus extends the computing paradigm of mobile transparent computing (MTC) with the characteristics of local mobile device computing and remote server storage to include LAN computing and remote server storage as well. Through the cooperation between the management center, request manager, connection manager, and processor manager of a third-party broker (TPB) for managing, respectively the computing resources, user requests, network connections, and other services in MTC, the TPB is able to support mobile computation-intensive and resource-consuming tasks, and handle fluctuation in users’ requests. Several experiments were carried out in a testbed environment of MTC to demonstrate the validity and efficiency of the proposed architecture and method. Experiment results showed that the TPB is effective and stable in optimizing resource utilization.

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Last updated on Nov. 10, 2017