Analysis of a fluid approximation to flow control dynamics

Bolot, Jean-Chrysostome; Shankar, A. Udaya

doi:10.1109/infcom.1992.263530

Cited by 49 publications

(19 citation statements)

References 27 publications

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“…(which extends the models in [8,15]). We explain briefly in the next section the linearized model; the detailed derivation of the non-linear model, as well as its linear approximation is presented in [2].…”

Section: W E Consider a Communication Network With A Linearized Dynammentioning

confidence: 51%

“…for high variability of (input) transmission rate, which is known to be undesirable; see the discussion in [8]. In many proposed telecommunication networks, the source will pay more for higher burstiness (variability) of its input rate, since highly variable input rate has typically a bad influence on other traffic.…”

Section: )mentioning

confidence: 99%

“…The larger g is, the more costly the deviations between the input and output rates will be. Indirectly, this may result in reducing thé variations of the queue size (see (8)). …”

Section: W Hen We Vary 9 (As We Will Do In the Sequel) This Will Be Dmentioning

confidence: 99%

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Optimal rate control for high speed telecommunication networks

Altman

Başar²

Proceedings of 1995 34th IEEE Conference on Decision and Control

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Section: W E Consider a Communication Network With A Linearized Dynammentioning

confidence: 51%

Section: )mentioning

confidence: 99%

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Optimal rate control for high speed telecommunication networks

Altman

Başar²

Proceedings of 1995 34th IEEE Conference on Decision and Control

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“…These properties are also correct for long-range dependent processes [21]. The application of the spectral window Λ(ω) gives the consistent estimate [31] I N (ω) = Choosing the Bartlett-Priestley spectral window [31] gives the following expression σ 2 [I N (ω)] ≈ [(6M)/(5M)]S 2 (ω) for the variance. The variance still depends on the power spectral density itself.…”

Section: Eurasip Journal On Advances In Signal Processingmentioning

confidence: 94%

Multilayer Statistical Intrusion Detection in Wireless Networks

Hamdi

Meddeb-Makhlouf

Boudriga

2008

EURASIP J. Adv. Signal Process.

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The rapid proliferation of mobile applications and services has introduced new vulnerabilities that do not exist in fixed wired networks. Traditional security mechanisms, such as access control and encryption, turn out to be inefficient in modern wireless networks. Given the shortcomings of the protection mechanisms, an important research focuses in intrusion detection systems (IDSs). This paper proposes a multilayer statistical intrusion detection framework for wireless networks. The architecture is adequate to wireless networks because the underlying detection models rely on radio parameters and traffic models. Accurate correlation between radio and traffic anomalies allows enhancing the efficiency of the IDS. A radio signal fingerprinting technique based on the maximal overlap discrete wavelet transform (MODWT) is developed. Moreover, a geometric clustering algorithm is presented. Depending on the characteristics of the fingerprinting technique, the clustering algorithm permits to control the false positive and false negative rates. Finally, simulation experiments have been carried out to validate the proposed IDS.

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“…In a nutshell, SAC will try to aggressively soak up bandwidth if it predicts the future network state to be \idle," adjusting the level of aggressiveness as a function of the predicted idleness. Congestion control has been an active area of networking research spanning over two decades with a urry of work carried out in the late '80s and early '90s [5,6,15,18,23,24,26,29,30,33,35,37]. Gerla and Kleinrock [15] laid down much of the early groundwork and Jacobson [23] has been instrumental in inuencing the practical mechanisms that have survived until today.…”

Section: Multiple Time Scale Congestion Controlmentioning

confidence: 99%

Multiple time scale congestion control for self-similar network traffic

Tuan

Park

1999

Performance Evaluation

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Analytical and empirical studies have shown that self-similar trac can have a detrimental impact on network performance including amplied queuing delay and packet loss rate. Given the ubiquity of scale-invariant burstiness observed across diverse networking contexts, nding eective trac control algorithms capable of detecting and managing self-similar trac has become an important problem.In this paper, we study congestion control algorithms for improving network performance|in particular, throughput|under self-similar trac conditions. Although scale-invariant burstiness implies the existence of concentrated periods of contention and idleness, the long-range dependence associated with self-similar trafc leaves open the possibility that correlation structure at larger time scales may be exploited for performance enhancement purposes. We construct a 2-level multiple time scale congestion control protocol that exercises congestion control concurrently across two time scales an order of magnitude apart. The rst component|acting at the smaller time scale|is a generic linear increase/exponential decrease feedback congestion control that uses implicit prediction aorded by feedback to aect rate control sensitive to changes in network state at 20{200ms time scales. The second component|acting at 2{5s time scales|uses explicit prediction to detect persistent shifts in overall network contention and uses this information to modulate the aggressiveness exhibited by the rst component.We show that cooperative interaction between the two congestion control modules acting on information at dierent time scales leads to improved performance visa-vis the case when the large time scale component is absent. We show that the improvement factor increases with long-range dependence and we show that as the number of ows engaging in multiple time scale congestion control (MTSC) increases, both fairness and eciency are preserved.

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Analysis of a fluid approximation to flow control dynamics

Cited by 49 publications

References 27 publications

Optimal rate control for high speed telecommunication networks

Optimal rate control for high speed telecommunication networks

Multilayer Statistical Intrusion Detection in Wireless Networks

Multiple time scale congestion control for self-similar network traffic

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