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利用反向传播算法合理分配缓冲区

Managing Buffer Appropriately with the Back Propagation Learning Algorithm
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摘要 该文提出了一种新的缓冲区分配方法,即动态神经共享(DynamicNeuralSharing,DNS)方法。这种方法利用反向传播算法合理分配缓冲区资源,从而减少自相似业务的分组丢失率。通过两组仿真实验发现,与完全分割(CompletePartitioning,CP),完全共享(CompleteSharing,CS),部分共享(PartialSharing,PS)这些传统的缓冲区分配方法相比,DNS在减少分组丢失和体现公平性(每个源都占有一定数量的缓冲区资源)之间达到了更好的平衡。 A novel buffer management algorithm named DNS(Dynamic Neural Sharing) is suggested in this paper. This algorithm utilizes the Back Propagation learning Algorithm(BPA) to manage buffer appropriately, thus reduce the packet loss in self-similar teletraffic patterns. A conclusion is drawn through two emulations that the DNS addresses the trade off between packet loss and fairness issues better than those traditional algorithms such as CP(Complete Partitioning), CS(Complete Sharing) and SPS(State Partial Sharing).
出处 《电子与信息学报》 EI CSCD 北大核心 2006年第8期1418-1421,共4页 Journal of Electronics & Information Technology
基金 国家863计划项目(2002AA122021) 电子科技大学青年科技基金(YF020504)资助课题
关键词 反向传播算法 分组丢失 DNS 自相似 ON-OFF源 Back Propagation learning Algorithm(BPA), Packet loss, Dynamic Neural Sharing (DNS), Self-similar, ON-OFF source
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参考文献5

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