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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
Job Allocation Mechanism for Battery Consumption Minimization of Cyber-Physical-Social Big Data Processing Based on Mobile Cloud Computing
ترجمه فارسی عنوان مقاله:
مکانیسم تخصیص کار برای حداقل سازی مصرف باتری پردازش داده های بزرگ سایبر فیزیکی-اجتماعی بر اساس محاسبات ابری موبایل
منبع:
IEEE - Received December 30, 2017, accepted January 29, 2018, date of publication February 8, 2018, date of current version May 9, 2018:
نویسنده:
GANGMAN YI 1, HYUN-WOO KIM1,2, JONG HYUK PARK 3, (Member, IEEE), AND YOUNG-SIK JEONG 1, (Member, IEEE)
چکیده انگلیسی:
The rapid development of information & communication technology has led to the
wide popularity of mobile devices, which have helped to improve business efficiency and enabled
simple mobility as small and light devices and convenience of being available anytime, anywhere for cyber-physical-social big data. There are many ongoing studies on mobile cloud computing (MCC) to overcome the limited computing capability and storage capacity and internal
battery limitation by taking advantage of the popularity of mobile devices for the processing cyber-physicalsocial big data. MCC consists of service-oriented architecture, agent-client architecture, and collaborative architecture, with job splitting and allocation as the critical factor. As such, job allocation techniques considering the performance resources of mobile devices have been studied. Note, however, that there is
a problem of job reallocation due to continuous battery consumption, since the studies consider only the
performance resources of mobile devices at the time of job allocation or take into account the performance
resources and remaining battery power only. This paper proposes the job allocation mechanism (JAM) for
battery consumption minimization of cyber-physical-social big data processing in MCC, which continuously
reflects the battery consumption rate to process jobs with mobile devices only without an external cloud
server in a collaborative architecture-based MCC environment. JAM allocates jobs considering the periodic
measurement of battery consumption and surplus resource to minimize the problem of job reallocation due
to battery rundown of the mobile devices. This paper designs and implements a system for verifying JAM
and demonstrated that the job processing speed increased in an MCC environment for cyber-physical-social
big data.
INDEX TERMS: Mobile cloud computing, job allocation, battery consumption, cyber-physical-social big data
قیمت: رایگان
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