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Traffic matrix estimation in large-scale IP networks

机译:大规模IP网络中的流量矩阵估计

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摘要

Understanding traffic matrices is useful for network design and so forth, but it is difficult to directly measure traffic matrices in IP networks. There is an existing method to estimate traffic matrices by link traffic information which is easy to measure, and routing information. However, this method requires significantly longer computation time as network size grows. This paper proposes large-scale traffic matrix estimation methods using a divide-and-conquer approach. The network is divided into multiple blocks, traffic matrices are estimated per block and estimation results are combined. In terms of division and combination, this paper proposes two detailed methods. By simulation, it is shown that both of the proposed methods can significantly improve computation time, while keeping estimation accuracy of the existing method. In particular, one of the proposed methods can perform traffic matrix estimation within practical computation time in a network of thousands of nodes.
机译:了解流量矩阵对于网络设计等很有用,但是很难直接测量IP网络中的流量矩阵。存在一种通过易于测量的链路交通信息和路由信息来估计交通矩阵的方法。但是,随着网络规模的增长,此方法需要相当长的计算时间。本文提出了一种采用分治法的大规模交通矩阵估计方法。将网络划分为多个块,每个块估计流量矩阵,然后合并估计结果。在划分和组合方面,本文提出了两种详细的方法。通过仿真表明,两种方法都可以显着提高计算时间,同时保持现有方法的估计精度。特别地,所提出的方法之一可以在成千上万个节点的网络中的实际计算时间内执行业务量矩阵估计。

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