Software Defined Networking (SDN) is an emerging architecture for the next-generation Internet, providing unprecedented network programmability to handle the explosive growth of Big Data driven by the popularisation of smart mobile devices and the pervasiveness of content-rich multimedia applications. In order to quantitatively investigate the performance characteristics of SDN networks, several research efforts from both simulation experiments and analytical modelling have been reported in the current literature. Among those studies, analytical modelling has demonstrated its superiority in terms of cost-effectiveness in the evaluation of large-scale networks. However, for analytical tractability and simplification, existing analytical models are derived based on the unrealistic assumptions that the network traffic follows the Poisson process which is suitable to model non-bursty text data and the data plane of SDN is modelled by one simplified Single Server Single Queue (SSSQ) system. Recent measurement studies have shown that, due to the features of heavy volume and high velocity, the multimedia big data generated by real-world multimedia applications reveals the bursty and correlated nature in the network transmission. With the aim of the capturing such features of realistic traffic patterns and obtaining a comprehensive and deeper understanding of the performance behaviour of SDN networks, this paper presents a new analytical model to investigate the performance of SDN in the presence of the bursty and correlated arrivals modelled by Markov Modulated Poisson Process (MMPP). The Quality-of-Service performance metrics in terms of the average latency and average network throughput of the SDN networks are derived based on the developed analytical model. To consider realistic multi-queue system of forwarding elements, a Priority-Queue (PQ) system is adopted to model SDN data plane. To address the challenging problem of obtaining the key performance metrics, e.g., queue length distribution of PQ system with a given service capacity, a versatile methodology extending the Empty Buffer Approximation (EBA) method is proposed to facilitate the decomposition of such a PQ system to two SSSQ systems. The validity of the proposed model is demonstrated through extensive simulation experiments. To illustrate its application, the developed model is then utilised to study the strategy of the network configuration and resource allocation in SDN networks
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机译:软件定义网络(SDN)是下一代Internet的新兴体系结构,它提供了前所未有的网络可编程性,以应对由智能移动设备的普及和内容丰富的多媒体应用程序的普及所推动的大数据的爆炸性增长。为了定量研究SDN网络的性能特征,当前文献中已经报道了来自仿真实验和分析建模的一些研究成果。在这些研究中,分析模型已证明在评估大型网络的成本效益方面具有优势。但是,为了便于分析和简化分析,基于不现实的假设得出了现有的分析模型,即网络流量遵循适合于对非突发文本数据建模的Poisson流程,而SDN的数据平面由一个简化的Single Server Single建模。队列(SSSQ)系统。最近的测量研究表明,由于体积大和速度快的特性,现实世界中的多媒体应用程序生成的多媒体大数据揭示了网络传输中的突发性和相关性。为了捕获现实流量模式的这些特征并获得对SDN网络性能行为的全面而深入的了解,本文提出了一种新的分析模型,以研究在存在突发性和相关到达模型的情况下SDN的性能。由Markov Modulated Poisson Process(MMPP)提出。基于开发的分析模型,得出了基于SDN网络的平均延迟和平均网络吞吐量的服务质量性能指标。为了考虑现实的转发元素多队列系统,采用优先级队列(PQ)系统对SDN数据平面进行建模。为了解决获得关键性能指标(例如具有给定服务容量的PQ系统的队列长度分布)的挑战性问题,提出了一种扩展空缓冲区近似(EBA)方法的通用方法,以促进将这种PQ系统分解为两个SSSQ系统。通过大量的仿真实验证明了该模型的有效性。为了说明其应用,然后使用开发的模型来研究SDN网络中的网络配置和资源分配策略。
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