ScholarWorks@동국대학교

초록

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 (c) 2013 John Wiley & Sons, Ltd.

키워드