基于征服者流量的排序度量启发式方法将综合生成的流量矩阵分配给拓扑

S. Zargar, M. Moghaddam
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引用次数: 1

摘要

本文介绍了综合生成的流量分配问题,提出了基于征服者流量排序度量的启发式方法来解决该问题。改进了以往的排序度量启发式方法,尝试将综合生成的流量分配到基于流量类型的拓扑中。在我们的工作中,传输控制协议(TCP)和用户数据报协议(UDP)作为当今互联网的两种主要流量类型,我们考虑为它们生成特殊的流量矩阵。通过为每种类型的流量生成专门的流量矩阵,流量矩阵可以更好地描述网络状况,并考虑到流量类型的约束。我们将我们提出的算法与之前的两种算法,负载最小化解决方案(LMS)和排名指标启发式(RMH)进行了比较,并给出了仿真结果。仿真结果表明,在存在征服者流量(TCP和UDP)的所有情况下,我们提出的启发式算法都表现得更好,并且比其他两种算法正确识别更多的流量。此外,结果表明,在没有征服者流量的情况下,使用LMS效果更好。原始分配的流量数量与RMH、LMS和基于征服者流量的RMH分配的流量之间的总绝对错误率是我们所做的另一个比较,它证明了我们的启发式方法分配的流量更接近原始速率。
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A Conqueror Traffic Based Ranking Metrics Heuristic for Assigning Synthetically Generated Traffic Matrices to a Topology
In this paper we introduced the synthetically generated traffic assignment problem and we proposed conqueror traffic based ranking metrics heuristic method for solving this problem. We modified previous ranking metrics heuristic method and we tried to assign the synthetically generated traffic to a topology based on traffic type. In our work, transmission control protocol (TCP) and user datagram protocol (UDP), as two major traffic types of today's Internet, are considered to generate special traffic matrices for them. By generating special traffic matrices for each type of traffic, traffic matrices may better describe the network conditions and consider the traffic type constraints. We made a comparison between our proposed algorithm and the two previous algorithms, called Load Minimization Solution (LMS) and ranking metrics heuristic (RMH), using simulation and results are presented. Simulation results indicate that our proposed heuristic method performs better in all cases which conqueror traffic (TCP & UDP) exists and correctly identifies more flow rates than the two other algorithms. Moreover, results show that, it is better to use LMS in the case of no conqueror traffic exists. The total absolute error rates between the amount of originally assigned flow rates and the rates that the RMH, LMS, and our conqueror traffic based RMH are assigned is another comparison we made which, proves that our heuristic method assigns the rates much closer to the original rates.
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