Maximum likelihood network topology identification from edge-based unicast measurements
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Network tomography on general topologies
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Direct measurement vs. indirect inference for determining network-internal delays
Performance Evaluation
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
Identifying lossy links in wired/wireless networks by exploiting sparse characteristics
Computer Networks: The International Journal of Computer and Telecommunications Networking
Stability analysis of discrete-time recurrent neural networks with stochastic delay
IEEE Transactions on Neural Networks
Network tomography: identifiability and Fourier domain estimation
IEEE Transactions on Signal Processing
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
Estimation of the available bandwidth ratio of a remote link or path segments
Computer Networks: The International Journal of Computer and Telecommunications Networking
| Hi-index | 35.69 |
On-line, spatially localized information about internal network performance can greatly assist dynamic routing algorithms and traffic transmission protocols. However, it is impractical to measure network traffic at all points in the network. A promising alternative is to measure only at the edge of the network and infer internal behavior from these measurements. We concentrate on the estimation and localization of internal delays based on end-to-end delay measurements from a source to receivers. We propose a sequential Monte Carlo (SMC) procedure capable of tracking nonstationary network behavior and estimating time-varying, internal delay characteristics. Simulation experiments demonstrate the performance of the SMC approach