Routing and Spectrum Assignment Algorithm with Traffic Prediction and Periodic Rerouting in Elastic Optical Networks

Shuiyan Zhang, Zhanqi Xu, Shuang Du, Jie Wu
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引用次数: 5

Abstract

Routing and spectrum assignment (RSA) is a key issue in elastic optical networks (EONs). A new RSA algorithm entitled traffic prediction and periodic rerouting (TPPR) is proposed to optimize the utilization efficiency of network resource in this paper. In the proposed TPPR algorithm, we use the radial basis function neural network (RBFNN) to predict traffic variations on each fiber link, which is beneficial to select a better lightpath from all candidate lightpaths for the current arrival connection request. Simultaneously, we also periodically update link weights and thus reroute all candidate lightpaths for each source-destination nodes pair after the proposed TPPR deals with a batch of connection requests. Besides, distance-adaptive is also used in TPPR algorithm. Under both the Poisson traffic model and the self-similar traffic model, simulation results show that the proposed algorithm can effectively reduce the blocking probability and improve resource utilization.
弹性光网络中基于流量预测和周期重路由的路由和频谱分配算法
路由和频谱分配(RSA)是弹性光网络中的一个关键问题。为了优化网络资源的利用效率,提出了一种新的RSA算法——流量预测和周期重路由(TPPR)。在提出的TPPR算法中,我们使用径向基函数神经网络(RBFNN)来预测每条光纤链路上的流量变化,这有利于从所有候选光路中为当前到达的连接请求选择更好的光路。同时,我们还定期更新链路权重,从而在提出的TPPR处理一批连接请求后,为每个源-目的节点对重新路由所有候选光路。此外,TPPR算法还采用了距离自适应。在泊松流量模型和自相似流量模型下,仿真结果表明,该算法能有效降低阻塞概率,提高资源利用率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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