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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2017
عنوان انگلیسی مقاله:
Computation partitioning for mobile cloud computing in a big data environment
ترجمه فارسی عنوان مقاله:
پارتیشن بندی محاسبات برای محاسبات ابری سیار در یک محیط داده های بزرگ
منبع:
IEEE - This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2017.2651880, IEEE Transactions on Industrial Informatics
نویسنده:
Jianqiang Li, Luxiang Huang, Yaoming Zhou, Suiqiang He, Zhong Ming
چکیده انگلیسی:
The growth of mobile cloud computing (MCC) is
challenged by the need to adapt to the resources and environment
that are available to mobile clients while addressing the dynamic
changes in network bandwidth. Big data can be handled via MCC.
In this paper, we propose a model of computation partitioning for
stateful data in the dynamic environment that will improve
performance. First, we constructed a model of stateful data
streaming and investigated the method of computation
partitioning in a dynamic environment. We developed a definition
of direction and calculation of the segmentation scheme, including
single frame data flow, task scheduling and executing efficiency.
We also defined the problem for a multi-frame data flow
calculation segmentation decision that is optimized for dynamic
conditions and provided an analysis. Second, we proposed a
computation partitioning method for single frame data flow. We
determined the data parameters of the application model, the
computation partitioning scheme, and the task and work order
data stream model. We followed the scheduling method to provide
the optimal calculation for data frame execution time after
computation partitioning and the best computation partitioning
method. Third, we explored a calculation segmentation method
for single frame data flow based on multi-frame data using
multi-frame data optimization adjustment and prediction of
future changes in network bandwidth. We were able to
demonstrate that the calculation method for multi-frame data in a
changing network bandwidth environment is more efficient than
the calculation method with the limitation of calculations for
single frame data. Finally, our research verified the effectiveness
of single frame data in the application of the data stream and
analyzed the performance of the method to optimize the
adjustment of multi-frame data. We used a mobile cloud
computing platform prototype system for face recognition to
verify the effectiveness of the method.
Index Terms: Big data | computation partitioning | data stream | dynamic environment | mobile cloud computing | stateful
قیمت: رایگان
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