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Modeling and Analysis of Communication Networks in Multicluster Systems under Spatio-Temporal Bursty Traffic

机译:时空突发流量下多集群系统通信网络的建模与分析

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摘要

Multicluster systems have emerged as a promising infrastructure for provisioning of cost-effective high-performance computing and communications. Analytical models of communication networks in cluster systems have been widely reported. However, for tractability and simplicity, the existing models are based on the assumptions that the network traffic follows the nonbursty Poisson arrival process and the message destinations are uniformly distributed. Recent measurement studies have shown that the traffic generated by real-world applications reveals the bursty nature in both the spatial domain (i.e., nonuniform distribution of message destinations) and temporal domain (i.e., bursty message arrival process). In order to obtain a comprehensive understanding of the system performance, a novel analytical model is developed for communication networks in multicluster systems in the presence of the spatio-temporal bursty traffic. The spatial traffic burstiness is captured by the communication locality and the temporal traffic burstiness is modeled by the Markov-modulated Poisson process. After validating its accuracy through extensive simulation experiments, the model is used to investigate the impact of bursty message arrivals and communication locality on network performance. The analytical results demonstrate that the communication locality can relieve the degrading effects of bursty message arrivals on the network performance.
机译:多集群系统已经成为一种有前途的基础架构,用于提供经济高效的高性能计算和通信。集群系统中通信网络的分析模型已被广泛报道。但是,为了便于处理和简化,现有模型基于以下假设:网络流量遵循非突发性Poisson到达过程,并且消息目标均匀分布。最近的测量研究表明,由实际应用程序生成的流量揭示了在空间域(即消息目标的不均匀分布)和时间域(即突发消息到达过程)中的突发性。为了获得对系统性能的全面了解,针对时空突发流量的存在,针对多集群系统中的通信网络开发了一种新颖的分析模型。通过通信局部性来捕获空间业务量突发性,并且通过马尔可夫调制的泊松过程对时间业务量突发性进行建模。通过广泛的仿真实验验证其准确性后,该模型用于研究突发消息到达和通信位置对网络性能的影响。分析结果表明,通信局部性可以缓解突发消息到达对网络性能的不利影响。

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