Explicit Loss Inference in Multicast Tomography

Duffield, Nick; Horowitz, J.; Presti, Francesco Lo; Towsley, Don

doi:10.1109/tit.2006.878228

Cited by 28 publications

(90 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(10) is an explicit function of the measurement results, so this method only requires simple arithmetic computation. This Also this estimator is a consistent estimator of the real link loss rate and has the same asymptotic variance as the MLE, to first order in the link-loss rates [13]. This ensures the explicit estimator has nearly the same accuracy comparing to the MLE method for the divide sub-trees.…”

Section: Monitorsmentioning

confidence: 81%

The Inference of Link Loss Rates with Internal Monitors

Chen

Lin

et al. 2008

IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

View full text Add to dashboard Cite

Network tomography has been widely used recently as an method to infer the network internal link-level characteristics by end-to-end measurement. In this paper, we consider the problem of estimating link loss rates using network tomography. The existing methods make the inference based on the whole tree of network, which is very complex for large scale network. To overcome this limitation, we propose a low complexity inference approach named LCIA. In the LCIA, we deploy monitors at internal nodes to reduce the complexity of inferring the link loss rates. It mainly consists of two steps. The first step is to deploy monitors at specific internal nodes to divide the original tree into several sub-trees with minimum depth. The second step is to infer the link loss rates of sub-trees by a new estimator which is an explicit function of loss measurements. The LCIA has the following features. First, it greatly reduces the inference complexity as the inference on the sub-trees is much simpler. Second, it improves the accuracy of the estimated results since the variance of loss estimator on sub-trees with lower depth is smaller than that on the original tree. The analytical and simulation results demonstrate that the LCIA outperforms the existing methods both on computation complexity and inference accuracy.

show abstract

Section: Monitorsmentioning

confidence: 81%

The Inference of Link Loss Rates with Internal Monitors

Chen

Lin

et al. 2008

IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

View full text Add to dashboard Cite

show abstract

“…Finally, one can construct an even simpler estimator, also with an explicit solution, by temporally extending the estimator of [9]. Let…”

Section: Simpler Temporal Estimatorsmentioning

confidence: 99%

“…Second, a variant of the MINC approach was recently proposed in [9], which constructed an estimator as an explicit function of the measured data as means of avoiding numerical root finding. In fact, we show using experiments that subtree-partition results in lower asymptotic variance (as the number of probes grows large) than the explicit estimator of [9].…”

Section: Introductionmentioning

confidence: 99%

Multicast inference of temporal loss characteristics

Arya

Duffield

Veitch³

2007

Performance Evaluation

Self Cite

View full text Add to dashboard Cite

“…If so, what is that and how different between the estimates obtained by the explicit MLE estimator and the estimator presented in [13] when n < ∞. The two issues will be addressed in this paper.…”

Section: Introductionmentioning

confidence: 96%

“…Unfortunately, there has been little progress in this regard until [12], where a connection between observations and the degree of the polynomial is established that provides the theoretical foundation to reduce the degree of the polynomial obtained from the likelihood equation. Prior to [12], the authors of [13] introduced an explicit estimator built on the law of large numbers. The estimator has been proved to be a consistent estimator and has the same asymptotic variances as that of an MLE to first order.…”

Section: Introductionmentioning

confidence: 99%

An efficient loss rate estimator in multicast tomography and its validity

Zhu

2011

2011 IEEE 3rd International Conference on Communication Software and Networks

View full text Add to dashboard Cite

Network tomography receives a considerable attention in recent years that provides a viable methodology to discover network charateristics, such as the loss rate of a link, the delay distribution of a link, from end-to-end observations. In the loss rate estimation, the previous studies show that to find the maximum likelihood estimate (MLE) of a link/path, we need to solve a polynomial form with a degree that is one less than the number of descendants connected to the link/path. Since there is no analytical solution to a polynomial of 5 degree or higher, the main concern is focused on the methods to obtain MLE without using iterative approximation. An explicit estimator based on the Law of Large Numbers has been proposed. However, the estimate obtained from the estimator is not a MLE. If n < ∞, the estimator may fail or produce an estimate that is significantly different from the MLE. In this paper, we propose an algorithm that can obtain the estimate analytically. In addition, we will point out the restriction of the previous estimators.

show abstract

Explicit Loss Inference in Multicast Tomography

Cited by 28 publications

References 14 publications

The Inference of Link Loss Rates with Internal Monitors

The Inference of Link Loss Rates with Internal Monitors

Multicast inference of temporal loss characteristics

An efficient loss rate estimator in multicast tomography and its validity

Contact Info

Product

Resources

About