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Two‐phase grouping‐based resource management for big data processing in mobile cloud computing
Author(s) -
Park JiSu,
Kim Hyongsoon,
Jeong YoungSik,
Lee Eunyoung
Publication year - 2014
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.2627
Subject(s) - computer science , mobile device , distributed computing , overhead (engineering) , big data , mobile computing , cloud computing , mobile cloud computing , data processing , resource (disambiguation) , computer network , data mining , database , operating system
SUMMARY Big data is generated from recent social network services, and distributed processing techniques have been studied to analyze it. In particular, because of the fast spread of mobile devices, a huge amount data is generated in a mobile environment. The distributed processing technologies such as MapReduce are applied to mobile devices, thanks to the improved computing power of mobile devices. However, mobile devices have several problems such as the movement problem and the utilization problem. Especially, the utilization problem and the movement problem of mobile devices cause system faults more frequently because of dynamic changes, and system faults prevent applications using mobile devices from being processed reliably. Therefore, to cope with these significant problems of mobile devices, we propose a grouping technique based on the utilization and movement rates. In our proposed scheme, mobile devices are separated into groups by cut‐off points based on entropy values. We also propose a two‐phase grouping method in order to reduce the overhead of group management. The experimental result shows that our algorithm outperforms traditional grouping techniques with maintaining stable big data processing and managing reliable resource. Copyright © 2013 John Wiley & Sons, Ltd.