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Poisson shot-noise process based flow-level traffic matrix generation for data center networks

机译：基于Poisson散粒噪声处理的数据中心网络流级流量矩阵生成

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The number of data centers has been increased for various reasons such as cloud computing, big-data analysis, multimedia service, etc. With public interests on data center, many novel technologies for data center networks have been proposed and deployed to support data center operations more efficiently and effectively. However, the construction of data center network incurs significant costs. Moreover, various technologies interplay each other to achieve multiple objectives, and it makes difficult to validate and/or verify characteristics of data center network. In addition, it difficult to perform experiments with a number of hosts and switches. Therefore, it is necessary to observe the characteristics of target data center network before building it. A common approach to evaluate data center is to run simulations that should be similar with real-world data center environment. However, generating traffic with the characteristics of data center networks is not matured yet. People still employ a traffic generator based on the characteristics of Internet traffic. We design a traffic generator that shows more accurate characteristics of data center network traffic. Various traffic characteristics exploited explored by several studies are considered. The proposed method generates flow-level network traffic matrix based on Poisson Shot-Noise model. We implemented the traffic generator using Python programming language to create traffic matrix. To evaluate the proposed method, we compare the results with real data center network traffic. Our results show that the generated traffic owns similar characteristics with the real network traffic in terms of flow size, duration, and the mean and variance of total traffic rate.
机译：出于各种原因，例如云计算，大数据分析，多媒体服务等，数据中心的数量已经增加。随着数据中心的公众利益，已经提出并部署了许多用于数据中心网络的新技术来支持数据中心的运营更有效地但是，数据中心网络的建设会产生巨大的成本。此外，各种技术相互影响以实现多个目标，并且使得难以验证和/或验证数据中心网络的特性。另外，很难对许多主机和交换机进行实验。因此，有必要在构建目标数据中心网络之前先对其进行观察。评估数据中心的一种常用方法是运行模拟，该模拟应该与实际的数据中心环境相似。但是，生成具有数据中心网络特征的流量还不成熟。人们仍然根据互联网流量的特点使用流量生成器。我们设计了一种流量生成器，该流量生成器显示了数据中心网络流量的更准确特征。考虑了一些研究探索的各种交通特征。该方法基于泊松散粒噪声模型生成流量级网络流量矩阵。我们使用Python编程语言实现了流量生成器，以创建流量矩阵。为了评估所提出的方法，我们将结果与实际数据中心网络流量进行了比较。我们的结果表明，在流量大小，持续时间以及总流量速率的均值和方差方面，生成的流量具有与实际网络流量相似的特征。

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