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General criteria-based clustering method for multi-node computing system

机译:基于通用准则的多节点计算系统聚类方法

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Synchronization/desynchronization and clustering are important techniques in multi-node computing systems, especially for sensor networks (SN) which is broadly considered to be a type of multi-node computing environment. However, most of the existing algorithms' clustering criteria are limited to the node location information and ignore the nature and characteristics of the nodes as well as the requirements of the applications. In this paper, an autonomic concurrent General Criteria-based Clustering (GCC) method is proposed for multi-node computing systems. The GCC method is based on the neuron oscillator pulse-coupling model and its clustering criteria can come from any node-related data or properties. The cluster member nodes share similar physical or logical properties and represent those relationships in the form of Logical Clusters (LCs). Due to the neuron dynamic system basis of the method, there is concurrency that exists both on the whole network and on each individual node. The simulation shows that the GCC method can generate diverse logical clusters and synchronization/desynchronization coexistence results with acceptable time and energy usage.
机译:同步/去同步和集群化是多节点计算系统中的重要技术,特别是对于传感器网络(SN),传感器网络被广泛认为是一种多节点计算环境。但是,大多数现有算法的聚类标准仅限于节点位置信息,而忽略了节点的性质和特征以及应用程序的要求。本文提出了一种针对多节点计算系统的自主并发基于通用标准的聚类方法。 GCC方法基于神经元振荡器脉冲耦合模型,其聚类标准可以来自任何与节点相关的数据或属性。群集成员节点共享相似的物理或逻辑属性,并以逻辑群集(LC)的形式表示这些关系。由于该方法的神经元动态系统基础,整个网络和每个单个节点上都存在并发性。仿真表明,GCC方法可以生成各种逻辑簇,并且同步/非同步共存结果具有可接受的时间和能耗。

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