A hierarchical multicast monitoring scheme
COMM '00 Proceedings of NGC 2000 on Networked group communication
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
Direct measurement vs. indirect inference for determining network-internal delays
Performance Evaluation
Multicast-based inference of network-internal delay distributions
IEEE/ACM Transactions on Networking (TON)
Network radar: tomography from round trip time measurements
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
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
Identifying lossy links in wired/wireless networks by exploiting sparse characteristics
Computer Networks: The International Journal of Computer and Telecommunications Networking
Multicast-based inference of network-internal delay performance using the method of moment
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
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Packet delay greatly influences the overall performance of network applications. It is therefore important to identify causes and location of delay performance degradation within a network. Existing techniques, largely based on end-to-end delay measurements of unicast traffic, are well suited to monitor and characterize the behavior of particular end-to-end paths. Within these approaches, however, it is not clear how to apportion the variable component of end-to-end delay as queueing delay at each link along a path. Moreover, they suffer of scalability issues if a significant portion of a network is of interest. In this paper, we show how end-to-end measurements of multicast traffic can be used to infer the packet delay distribution and utilization on each link of a logical multicast tree. The idea, is to exploit the inherent correlation between multicast observations to infer performance of paths between branch points in a tree spanning a multicast source and its receivers. The method does not depend on cooperation from intervening network elements; because of the bandwidth efficiency of multicast traffic, it is suitable for large scale measurements of both end-to-end and internal network dynamics. We establish desirable statistical properties of the estimator, namely consistency and asymptotic normality. We evaluate the estimator through simulation and observe that it is robust with respect to moderate violations of the underlying model.