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A New Scalable, Distributed, Fuzzy C-Means Algorithm-Based Mobile Agents Scheme for HPC: SPMD Application

机译:一种适用于HPC的基于可扩展,分布式,模糊C均值算法的新移动代理方案:SPMD应用

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The aim of this paper is to present a mobile agents model for distributed classification of Big Data. The great challenge is to optimize the communication costs between the processing elements (PEs) in the parallel and distributed computational models by the way to ensure the scalability and the efficiency of this method. Additionally, the proposed distributed method integrates a new communication mechanism to ensure HPC (High Performance Computing) of parallel programs as distributed one, by means of cooperative mobile agents team that uses its asynchronous communication ability to achieve that. This mobile agents team implements the distributed method of the Fuzzy C-Means Algorithm (DFCM) and performs the Big Data classification in the distributed system. The paper shows the proposed scheme and its assigned DFCM algorithm and presents some experimental results that illustrate the scalability and the efficiency of this distributed method.
机译:本文的目的是提出一种用于大数据分布式分类的移动代理模型。巨大的挑战是通过确保此方法的可伸缩性和效率来优化并行和分布式计算模型中处理元素(PE)之间的通信成本。此外,所提出的分布式方法集成了一种新的通信机制,以通过协作移动代理团队利用其异步通信功能来实现并行程序的HPC(高性能计算)作为分布式程序。该移动代理团队实现了模糊C均值算法(DFCM)的分布式方法,并在分布式系统中执行了大数据分类。本文展示了提出的方案及其分配的DFCM算法,并提供了一些实验结果,说明了该分布式方法的可扩展性和效率。

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