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首页> 外文期刊>Journal of computer sciences >An Optimized Mobile Cloud Computational Offloading Framework using K-Means Algorithm
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An Optimized Mobile Cloud Computational Offloading Framework using K-Means Algorithm

机译:使用k均值算法优化的移动云计算卸载框架

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摘要

Offloading the execution of heavy computational modules from mobile devices to Mobile Cloud Computing (MCC) is inevitable in today's era as it mainly focuses in consuming less battery power and execution time. But, the problem incurred with identifying the most optimal cloud device to map each module still remains a challenge in cloud computing environment. In this paper, a novel MCC offloading framework is proposed to fasten the allocation and execution of high computational modules that runs in the mobile device, effectively on the cloud. The framework employs K-Means clustering algorithm to group the nearest cloud virtual machines that best suits for executing modules of software running in the mobile. The objective of the paper is to maximize the energy savings by extending the battery life and execution speed of mobile device when executing heavy computational modules. The optimal selection of cloud device is attained by grouping the requirements of each module with the nearest cloud devices offering the same requirements using K-Means Algorithm. The proposed framework is compared with the existing mobile computation offloading frameworks with respect to energy saving, execution time and energy consumption. The results show that the proposed work executes the modules of computationally intensive modules in minimum time span with maximized energy savings than the existing frameworks.
机译:从移动设备卸载到移动云计算(MCC)在当今的时代不可避免地卸载重载模块(MCC),因为它主要专注于消耗较少的电池电源和执行时间。但是,识别要映射每个模块的最佳云设备的问题仍然是云计算环境中的挑战。在本文中,提出了一种新的MCC卸载框架,用于在云上有效地固定在移动设备中运行的高计算模块的分配和执行。该框架采用K-Means Clustering算法将最接近的云虚拟机组分组,该计算机最适合执行移动中运行的软件模块。本文的目的是通过在执行重计算模块时扩展移动设备的电池寿命和执行速度来最大化节能。通过将每个模块的要求与使用K-Means算法提供相同要求的最近的云设备来分组每个模块的要求,实现了云设备的最佳选择。将所提出的框架与关于节能,执行时间和能量消耗的现有移动计算卸载框架进行比较。结果表明,在最短的时间范围内,所提出的工作在最小的时间范围内执行计算密集型模块的模块,而不是现有的框架。

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