Congestion avoidance and control
SIGCOMM '88 Symposium proceedings on Communications architectures and protocols
Multicast-based inference of network-internal delay distributions
IEEE/ACM Transactions on Networking (TON)
Measuring ISP topologies with rocketfuel
IEEE/ACM Transactions on Networking (TON)
Network tomography from aggregate loss reports
Performance Evaluation - Performance 2005
GRE encapsulated multicast probing: a scalable technique for measuring one-way loss
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Multicast-based inference of network-internal loss characteristics
IEEE Transactions on Information Theory
Multicast topology inference from measured end-to-end loss
IEEE Transactions on Information Theory
Explicit Loss Inference in Multicast Tomography
IEEE Transactions on Information Theory
Inverse problems in queueing theory and Internet probing
Queueing Systems: Theory and Applications
Optimal probing for unicast network delay tomography
INFOCOM'10 Proceedings of the 29th conference on Information communications
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Multicast-based inference has been proposed as a method of estimating average loss rates of internal network links, using end-to-end loss measurements of probes sent over a multicast tree. We show that, in addition to loss rates, temporal characteristics of losses can also be estimated. Knowledge of temporal loss characteristics has applications for services such as voip which are sensitive to loss bursts, as well as for bottleneck detection. Under the assumption of mutually independent, but otherwise general, link loss processes, we show that probabilities of arbitrary loss patterns, mean loss-run length, and even the loss-run distribution, can be recovered for each link. Alternative estimators are presented which trade-off efficiency of data use against implementation complexity. A second contribution is a novel method of reducing the computational complexity of estimation, which can also be used by existing minc estimators. We analyse estimator performance using a combination of theory and simulation.