Multicast inference of temporal loss characteristics

Arya, Vijay; Duffield, Nick; Veitch, Darryl

doi:10.1016/j.peva.2007.06.002

Cited by 30 publications

(34 citation statements)

References 11 publications

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“…The Bernoulli model assumes that the packet losses are temporal independent, and all packets may be lost with a fixed probability. However, literatures [12,13] pointed out that a packet loss is usually an indication of possible congestion buildup, and the next packet may also be lost with a high probability. That is, the packet losses exhibit the characteristic of temporal dependence.…”

Section: Related Workmentioning

confidence: 99%

“…The Gilbert model characterises the temporal dependence of two adjacent packets, but it may not be able to accurately describe the packet loss process of all links [8]. Arya et al [13] proposed a multicast-based method to estimate the temporal loss characteristics such as the probability of two successive packet losses and the mean duration of loss-runs. However, the multicast-based feature makes it almost infeasible because multicast routing is not widely deployed in today's Internet.…”

Section: Related Workmentioning

confidence: 99%

“…Gilbert model-based methods [12] considers the dependence of two consecutive packets temporal loss characteristics inference methods [13] considers the dependence of a series of consecutive packets multicast-based feature makes it almost infeasible in practice using scenario. However, our simulation results show that even though in a non-ideal scenario, our method still achieves a good performance.…”

Section: Features Limitationsmentioning

confidence: 99%

“…X v (l ) ¼ 0 if the lth probe packet is lost on link v, and 1 otherwise. As in [13], we make the following two assumptions: † Spatial independence: X v (l ) and X w (l ) are independent if v, w [ V\{0} and v = w. † Stationary and ergodic: X v is stationary and ergodic for all v [ V\{0}.…”

Section: Features Limitationsmentioning

confidence: 99%

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Accurate and effective inference of network link loss from unicast end-to-end measurements

Fei

2012

IET Communications

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Understanding the network link loss is particularly important for optimising delay-sensitive applications. This study addresses the issue of estimating temporal dependence characteristic of link loss by using network tomography. Different from existing works of network loss tomography, the authors use a kth order Markov Chain (k-MC for short, k . 1) to model the packet loss process, and propose a constrained optimisation-based method to estimate the state transmission probabilities of the k-MC link loss model. The authors also propose a top -down algorithm in order to ensure that our method can be applied to large networks. Compared with existing loss tomography methods, our method is capable of obtaining more accurate packet loss probability estimates. The ns-2 simulation results show the good performance of our method.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Features Limitationsmentioning

confidence: 99%

Section: Features Limitationsmentioning

confidence: 99%

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Accurate and effective inference of network link loss from unicast end-to-end measurements

Fei

2012

IET Communications

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“…Since the probes are multicast to the receivers, the observations at the receivers are strongly correlated. Given the network topology and the observations of the probes at receivers, statistical inference can be applied to estimate the network characteristics, such as link-level loss rates [2], delay distribution [3], [4], [5], [6], [7], and loss pattern [8]. In this paper, our focus is on using active approach to estimate the loss rate of a path/link.…”

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

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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.

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Robust estimator for loss tomography

Zhu

2013

2013 6th International Congress on Image and Signal Processing (CISP)

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Multicast inference of temporal loss characteristics

Cited by 30 publications

References 11 publications

Accurate and effective inference of network link loss from unicast end-to-end measurements

Accurate and effective inference of network link loss from unicast end-to-end measurements

An efficient loss rate estimator in multicast tomography and its validity

Robust estimator for loss tomography

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