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Temporal dependence network loss tomography using maximum pseudo likelihood method

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2 Author(s)
Gaolei Fei ; Key Lab. of Opt. Fiber Sensing & Commun., Univ. of Electron. Sci. & Technol. of China, Chengdu, China ; Guangmin Hu

Understanding network link loss is particularly important for optimizing delay-sensitive applications. This paper addresses the problem of estimating temporal dependence characteristic of link loss by using network tomography. First, the k-th order Markov Chain (k >; 1) is introduced to model the packet loss process. The model considers the dependence of k + 1 consecutive packets, and is capable of capturing the temporal dependence characteristic of link loss accurately if k is large enough. Second, we propose a maximum pseudo likelihood inference based method to estimate the state transition probabilities of the k-th order Markov Chain link loss model from the unicast end-to-end measurements. The analytical and simulation results show the good performance of our method.

Published in:

Information Networking (ICOIN), 2012 International Conference on

Date of Conference:

1-3 Feb. 2012