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Markovian Arrival Process Parameter Estimation With Group Data
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DOI：10.1109/TNET.2008.2008750.png)
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Abstract
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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.
Keywords:
Expectation-maximization (EM) algorithm
group data
Markov-modulated Poisson process (MMPP)
Markovian arrival process (MAP)
maximum-likelihood (ML) estimation
network traffic
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