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Maximum Pseudo Likelihood Estimation in Network Tomography
| Content Provider | CiteSeerX |
|---|---|
| Abstract | Abstract — Network monitoring and diagnosis are key to improving network performance. The difficulties of performance monitoring lie in today’s fast growing Internet, accompanied by increasingly heterogeneous and unregulated structures. Moreover, these tasks become even harder since one cannot rely on the collaboration of individual routers and servers to directly measure network traffic. Even though the aggregative nature of possible network measurements gives rise to inverse problems, existing methods for solving inverse problems are usually computationally intractable or statistically inefficient. In this paper, a pseudo likelihood approach is proposed to solve a group of network tomography problems. The basic idea of pseudo likelihood is to form simple subproblems and ignore the dependences among the subproblems to form a product likelihood of the subproblems. As a result, this approach keeps a good balance between the computational complexity and the statistical efficiency of the parameter estimation. Some statistical properties of the pseudo likelihood estimator, such as consistency and asymptotic normality, are established. A pseudo expectation-maximization (EM) algorithm is developed to maximize the pseudo log-likelihood function. Two examples with simulated or real data are used to illustrate the pseudo likelihood proposal: (1) inference of the internal link delay distributions through multicast end-to-end measurements; (2) origin-destination matrix estimation through link traffic counts. Index Terms — End-to-end measurement, multicast tree, network tomography, origin-destination matrix, pseudo likelihood. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Network Tomography Maximum Pseudo Likelihood Estimation Pseudo Likelihood Aggregative Nature Product Likelihood Statistical Property Internal Link Delay Distribution Network Tomography Problem Individual Router Unregulated Structure Origin-destination Matrix Estimation Origin-destination Matrix Network Performance Multicast End-to-end Measurement Computational Complexity Pseudo Likelihood Estimator Statistical Efficiency Asymptotic Normality Possible Network Measurement Pseudo Expectation-maximization Multicast Tree Pseudo Likelihood Proposal Performance Monitoring Lie Abstract Network Monitoring Parameter Estimation Inverse Problem Network Traffic Simple Subproblems Good Balance Link Traffic Count Pseudo Log-likelihood Function Index Term End-to-end Measurement Pseudo Likelihood Approach |
| Content Type | Text |
