Markovian Arrival Process Parameter Estimation With Group Data
Markovian Arrival Process Parameter Estimation With Group Data
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DOI：
10.1109/tnet.2008.2008750
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发表时间：
2009-08
期刊：
影响因子：
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通讯作者：
H. Okamura;T. Dohi;Kishor S. Trivedi
中科院分区：
文献类型：
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作者：
H. Okamura;T. Dohi;Kishor S. Trivedi
本文解决了马尔可夫到达过程（MAP）的参数估计问题。在网络流量测量实验中，人们经常遇到组数据，其中一组的到达时间被收集为一个容器。尽管在许多情况下都会观察到群体数据，但几乎所有现有的 MAP 估计方法都是基于非群体数据。本文提出了一种将 MAP 和马尔可夫调制泊松过程 (MMPP) 拟合以对数据进行分组的数值程序。所提出的算法基于期望最大化 (EM) 方法，是现有 EM 算法的自然但重要的扩展，用于估计 MAP 和 MMPP 的参数。特别是对于 MMPP 估计，我们提供了基于所提出的 EM 算法的有效近似。我们通过数值实验检查了所提出算法的性能，并提供了使用真实流量数据进行流量分析的示例。
This paper addresses a parameter estimation problem of Markovian arrival process (MAP). In network traffic measurement experiments, one often encounters the group data where arrival times for a group are collected as one bin. Although the group data are observed in many situations, nearly all existing estimation methods for MAP are based on nongroup data. This paper proposes a numerical procedure for fitting a MAP and a Markov-modulated Poisson process (MMPP) to group data. The proposed algorithm is based on the expectation-maximization (EM) approach and is a natural but significant extension of the existing EM algorithms to estimate parameters of the MAP and MMPP. Specifically for the MMPP estimation, we provide an efficient approximation based on the proposed EM algorithm. We examine the performance of proposed algorithms via numerical experiments and present an example of traffic analysis with real traffic data.