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Multihop Virtual Topology Design in WDM Optical Networks for Self-Similar Traffic

机译:WDM光网络中自相似流量的多跳虚拟拓扑设计

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摘要

In this paper, we consider the problem of designing virtual topologies for multihop optical WDM networks when the traffic is self-similar in nature. Studies over the last few years suggest that the network traffic is bursty and can be much better modeled using self similar process instead of Poisson process. We examine buffer sizes of a network and observe that, even with reasonably low buffer overflow probability, the maximum buffer size requirement for self-similar traffic can be very large. Therefore, a self-similar traffic model has an impact on the queuing delay which is usually much higher than that obtained with the Poisson model. We investigate the problem of constructing the virtual topology with these two types of traffic and solve it with two algorithmic approaches: Greedy (Heuristic) algorithm and Evolutionary algorithm. While the greedy algorithm performs a least-cost search on the total delay along paths for routing traffic in a multihop fashion, the evolutionary algorithm uses genetic methods to optimize the average delay in a network. We analyze and compare our proposed algorithms with an existing algorithm via different performance parameters. Interestingly, with both the proposed algorithms the difference in the queuing delays, caused by self-similar and Poisson traffic, results in different multihop virtual topologies.
机译:在本文中,我们考虑了在流量本质上是自相似的情况下为多跳光学WDM网络设计虚拟拓扑的问题。过去几年的研究表明,网络流量是突发性的,可以使用自相似过程而不是泊松过程进行​​更好的建模。我们检查了网络的缓冲区大小,发现即使缓冲区溢出概率相当低,自相似流量的最大缓冲区大小要求也可能非常大。因此,自相似流量模型对排队延迟的影响通常比使用Poisson模型获得的排队延迟高得多。我们研究了使用这两类流量构建虚拟拓扑的问题,并通过两种算法来解决:贪婪(启发式)算法和进化算法。贪婪算法对路径的总延迟执行最低成本的搜索,以多跳方式路由流量,而进化算法使用遗传方法来优化网络中的平均延迟。我们通过不同的性能参数来分析和比较我们提出的算法与现有算法。有趣的是,使用这两种算法,由于自相似和Poisson流量造成的排队延迟差异导致了不同的多跳虚拟拓扑。

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