基于重要性抽样的MMPP/D/1队列丢包概率在线估计

Hung Nguyen Ngoc, K. Nakagawa
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引用次数: 0

摘要

本文提出了一种利用重要性抽样(IS)估计MMPP/D/1排队系统丢包概率的新方法。为了估计罕见事件,我们不提高流量的到达率,但降低排队数据包的服务率。在[5]中,作者也提出了FIFO队列长度尾部概率的在线估计。然而,作者使用的到达过程是泊松过程,在我们的方法中比MMPP到达过程更简单。最后,我们实现了我们的算法,并将我们的实验精度和模拟时间与蒙特卡罗方法和传统的IS方法进行了比较。
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Online Estimation for Packet Loss Probability of MMPP/D/1 Queuing by Importance Sampling
In this paper, we propose a new method to estimate the packet loss probability of the MMPP/D/1 queuing system by Importance Sampling (IS). In order to estimate rare event we do not increase the arrival rate of traffic, but we decrease service rate of queuing packet. In [5], the authors also proposed an online estimation for the tail probability of FIFO queue length. However, the authors used arrival process is a Poisson process, it is simpler than MMPP arrival process in our method. Finally, we implement our algorithm and compare accuracy and simulation time of our experiments to the Monte Carlo method (MC) and conventional IS method.
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