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A clustering approach for identifying and quantifying irregularities in interconnection networks

机译:一种用于识别和量化互连网络中不规则性的聚类方法

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摘要

Support for arbitrary topologies has become more popular for system-area networks but very little has been done in trying to characterize their behavior and performance. Traditional parameters like diameter and bisection width are not sufficient for characterizing the irregularities that abound in such networks and fail to give much insight into throughput performance. A clustering approach for partitioning a network into clusters of richly-connected regions is proposed as a means of defining two performance-correlated characterization metrics: intercluster bandwidth index and intercluster link-cost index. The two characterization metrics are shown to have a strong correlation to saturation throughput when link and load distribution of a network is imbalanced. Simulation results also show that the clustering algorithm can be applied to a variety of network configurations and traffic scenarios, particularly irregular ones. With the proposed characterization metrics that correlate more strongly with performance, it is possible to classify networks into categories having similar performance.
机译:对任意拓扑的支持在系统区域网络中变得越来越流行,但是在试图描述其行为和性能方面所做的工作很少。传统参数(例如直径和二等分宽度)不足以表征此类网络中存在的不规则现象,并且无法充分了解吞吐性能。提出了一种将网络划分为高连接区域集群的集群方法,作为定义两个与性能相关的表征指标的方法:集群间带宽指数和集群间链路成本指数。当网络的链路和负载分配不平衡时,这两个表征指标显示为与饱和吞吐量有很强的相关性。仿真结果还表明,该聚类算法可以应用于多种网络配置和流量场景,尤其是不规则场景。利用与性能更紧密相关的建议特性指标,可以将网络分类为具有相似性能的类别。

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