Local area network analysis using end-to-end delay tomography
ACM SIGMETRICS Performance Evaluation Review - Special issue on the First ACM SIGMETRICS Workshop on Large Scale Network Inference (LSNI 2005)
A fast lightweight approach to origin-destination IP traffic estimation using partial measurements
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Moment estimation in delay tomography with spatial dependence
Performance Evaluation
High quality queueing information from accelerated active network tomography
Proceedings of the 4th International Conference on Testbeds and research infrastructures for the development of networks & communities
A bottom-up inference of loss rate
Computer Communications
MAP-MRF super-resolution image reconstruction using maximum pseudo-likelihood parameter estimation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Statistical estimation of delays in a multicast tree using accelerated EM
Queueing Systems: Theory and Applications
Network tomography: identifiability and Fourier domain estimation
IEEE Transactions on Signal Processing
Stochastic Composite Likelihood
The Journal of Machine Learning Research
Recurrent neural network inference of internal delays in nonstationary data network
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Bottom up algorithm to identify link-level transition probability
ICCNMC'05 Proceedings of the Third international conference on Networking and Mobile Computing
| Hi-index | 35.69 |
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 measure network traffic directly. 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. 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.