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Mobility-aware balanced scheduling algorithm in mobile Grid based on mobile agent

Published online by Cambridge University Press:  03 October 2014

Jonghyuk Lee ,
Sungjin Choi ,
Taeweon Suh  and
Heonchang Yu
Jonghyuk Lee
Affiliation:
Department of Computer Science Education, Korea University, Anam-dong, Sungbuk-gu, Seoul 136-701, Korea, e-mail: spurt@korea.ac.kr, suhtw@korea.ac.kr, yuhc@korea.ac.kr
Sungjin Choi
Affiliation:
Cloud Service Business Unit, KT 17 Umyeon-dong, Seocho-gu, Seoul 137-792, Korea e-mail: lotieye@gmail.com
Taeweon Suh
Affiliation:
Department of Computer Science Education, Korea University, Anam-dong, Sungbuk-gu, Seoul 136-701, Korea, e-mail: spurt@korea.ac.kr, suhtw@korea.ac.kr, yuhc@korea.ac.kr
Heonchang Yu
Affiliation:
Department of Computer Science Education, Korea University, Anam-dong, Sungbuk-gu, Seoul 136-701, Korea, e-mail: spurt@korea.ac.kr, suhtw@korea.ac.kr, yuhc@korea.ac.kr
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Abstract

The emerging Grid is extending the scope of resources to mobile devices and sensors that are connected through loosely connected networks. Nowadays, the number of mobile device users is increasing dramatically and the mobile devices provide various capabilities such as location awareness that are not normally incorporated in fixed Grid resources. Nevertheless, mobile devices exhibit inferior characteristics such as poor performance, limited battery life, and unreliable communication, compared with fixed Grid resources. Especially, the intermittent disconnection from network owing to users’ movements adversely affects performance, and this characteristic makes it inefficient and troublesome to adopt the synchronous message delivery in mobile Grid. This paper presents a mobile Grid system architecture based on mobile agents that support the location management and the asynchronous message delivery in a multi-domain proxy environment. We propose a novel balanced scheduling algorithm that takes users’ mobility into account in scheduling. We analyzed users mobility patterns to quantitatively measure the resource availability, which is classified into three types: full availability, partial availability, and unavailability. We also propose an adaptive load-balancing technique by classifying mobile devices into nine groups depending on availability and by utilizing adaptability based on the multi-level feedback queue to handle the job type change. The experimental results show that our scheduling algorithm provides a superior performance in terms of execution times to the one without considering mobility and adaptive load-balancing.

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