On the convergence of the coordinate descent method for convex differentiable minimization
Journal of Optimization Theory and Applications
Network tomography on general topologies
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Multicast-based inference of network-internal delay distributions
IEEE/ACM Transactions on Networking (TON)
Network Delay Tomography from End-to-End Unicast Measurements
IWDC '01 Proceedings of the Thyrrhenian International Workshop on Digital Communications: Evolutionary Trends of the Internet
Network tomography from measured end-to-end delay covariance
IEEE/ACM Transactions on Networking (TON)
Moment estimation in delay tomography with spatial dependence
Performance Evaluation
A bottom-up inference of loss rate
Computer Communications
Maximum pseudo likelihood estimation in network tomography
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Unicast-based inference of network link delay distributions with finite mixture models
IEEE Transactions on Signal Processing
Sequential Monte Carlo inference of internal delays innonstationary data networks
IEEE Transactions on Signal Processing
Multicast-based inference of network-internal loss characteristics
IEEE Transactions on Information Theory
Inference of Link Delay in Communication Networks
IEEE Journal on Selected Areas in Communications
| Hi-index | 35.68 |
The statistical problem for network tomography is to infer the distribution of X, with mutually independent components, from a measurement modelY = AX, where is a given binary matrix representing the routing topology of a network under consideration. The challenge is that the dimension of X is much larger than that ofY and thus the problem is often ill-posed. This paper studies some statistical aspects of network tomography. We first develop a unifying theory on the identifiability of the distribution of X. We then focus on an important instance of network tomography--network delay tomography, where the problem is to infer internal link delay distributions using end-to-end delay measurements. We propose a novel mixture model for link delays and develop a fast algorithm for estimation based on the General Method of Moments. Through extensive model simulations and real Internet trace driven simulation, the proposed approach is shown to be favorable to previous methods using simple discretization for inferring link delays in a heterogeneous network.