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An Architectural Framework for Accurate Characterization of Network Traffic

机译：准确表征网络流量的体系结构框架

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摘要

In networks carrying large volume of traffic, accurate traffic characterization is necessary for understanding the dynamics and patterns of network resource usage. Previous approaches to flow characterization are based on random sampling of the packets (e.g., Cisco's NetFlow) or inferring characteristics solely based on long lived flows (LLFs) or on lossy data structures (e.g., bloom filters, hash tables). However, none of these approaches takes into account the heavy-tailed nature of the Internet traffic and separates the estimation algorithm from the flow measurement architecture.In this paper, we propose an alternate approach to traffic characterization by closely linking the flow measurement architecture with the estimation algorithm. Our measurement framework stores complete information related to short lived flows (SLFs) while collecting partial information related to LLFs. For real-time separation of LLFs and SLFs, we propose a novel algorithm based on typical sequences from Information theory. The distribution (pdf) and sample space of the underlying traffic is estimated using the non-parametric Parzen window technique and likelihood function defined over the Coupon collector problem. We validate the accuracy and performance of our estimation technique using traffic traces from the internal LAN in our laboratory and from National Library for Applied Network Research (NLANR).
机译：在承载大量流量的网络中，准确的流量表征对于了解网络资源使用的动态和模式非常必要。以前的流特征描述方法是基于数据包的随机采样（例如Cisco NetFlow），或者仅基于长期流（LLF）或有损数据结构（例如Bloom过滤器，哈希表）来推断特征。但是，这些方法都没有考虑到Internet流量的重尾特性，并将估计算法与流量测量体系结构分开。在本文中，我们通过将流量测量体系结构与流量测量体系结构紧密联系，提出了一种流量表征的替代方法。估计算法。我们的测量框架存储与短寿命流（SLF）有关的完整信息，同时收集与LLF相关的部分信息。对于LLF和SLF的实时分离，我们提出了一种基于信息理论中典型序列的新颖算法。使用非参数Parzen窗口技术和在Coupon收集器问题上定义的似然函数，估算基础流量的分布（pdf）和样本空间。我们使用实验室内部局域网和国家应用网络研究图书馆（NLANR）的流量跟踪来验证估计技术的准确性和性能。

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