Self-Similarity, Packet Loss, Jitter, and Packet Size: Empirical Relationships for VoIP

Toral, Homero; Torres, D.; Hernández, César; Estrada, Leopoldo

doi:10.1109/conielecomp.2008.20

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…Over the past decade, self-similar traffic is researched in the field of terrestrial and wired networks [3], but there is little research on self-similar traffic in wireless network [4][5], especially satellite network. According to VoIP (Voice over IP) traffic in terrestrial network, H. Toral [6] analyzes the effect of self-similar traffic on packet loss ratio, delay and jitter. Q. Liang [7] analyzes the influence of wireless gateway on self-similar traffic, but it is one special instance without generality.…”

Section: Introductionmentioning

confidence: 99%

Research on Aggregation and Propagation of Self-Similar Traffic in Satellite Network

Na¹,

Liu²,

Cui³

et al. 2015

IJHIT

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It has already been confirmed that the traffic in high-speed terrestrial network presents self-similarity, but there is little research on self-similarity of traffic in satellite network. Considering time-varying network topology and link status, this paper analyzes the aggregation and propagation of self-similar traffic between nodes in satellite network. Furthermore, a sort of special network node called ground gateway is modeled, based on which the characteristics of the output traffic that the input traffic from terrestrial network passes gateway into satellite network are analyzed. Theoretically analyses demonstrate that after aggregation and propagation between satellite nodes, traffic is still self-similar, and the self-similarity of the output traffic generated by gateway from terrestrial network to satellite network is more often than not weakened.

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Section: Introductionmentioning

confidence: 99%

Research on Aggregation and Propagation of Self-Similar Traffic in Satellite Network

Na¹,

Liu²,

Cui³

et al. 2015

IJHIT

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show abstract

“…Despite the difficulties in establishing any kind of explicit relation between the Hurst exponent and parameters of communication systems, Toral et al [19] demonstrated that there is an empirical relationship between the Hurst exponent of traffic flows and the packet loss rate.…”

Section: Introductionmentioning

confidence: 99%

Generalized Hurst Hypothesis: Description of Time-Series in Communication Systems

2021

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In this paper, we focus on the generalization of the Hurst empirical law and suggest a set of reduced parameters for quantitative description of long-time series. These series are usually considered as a specific response of a complex system (economic, geophysical, electromagnetic and other systems), where successive fixations of external factors become impossible. We consider applying generalized Hurst laws to obtain a new set of reduced parameters in data associated with communication systems. We analyze three hypotheses. The first one contains one power-law exponent. The second one incorporates two power-law exponents, which are in many cases complex-conjugated. The third hypothesis has three power-law exponents, two of which are complex-conjugated as well. These hypotheses describe with acceptable accuracy (relative error does not exceed 2%) a wide set of trendless sequences (TLS) associated with radiometric measurements. Generalized Hurst laws operate with R/S curves not only in the asymptotic region, but in the entire domain. The fitting parameters can be used as the reduced parameters for the description of the given data. The paper demonstrates that this general approach can also be applied to other TLS.

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“…The measure used to estimate the degree of self-similarity and LRD is the Hurst exponent (Hurst parameter), denoted by H. It was proven in [ 11 ] that an empirical relationship between Hurst exponent and the Packet Loss Rate exists. A study considering Voice over IP (VoIP) has shown that the increase in the Hurst parameter values resulted in the increase in the Packet Loss Rates and the degradation of the quality of voice.…”

Section: Introductionmentioning

confidence: 99%

Long-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis

Filus

Domański

Domańska

et al. 2020

Entropy

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The paper examines the ability of neural networks to classify Internet traffic data in terms of self-similarity expressed by the Hurst exponent. Fractional Gaussian noise is used for the generation of synthetic data for modeling the genuine ones. It is presented that the trained model is capable of classifying the synthetic data obtained from the Pareto distribution and the real traffic data. We present the results of training for different optimizers of the cost function and a different number of convolutional layers in the neural network.

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Self-Similarity, Packet Loss, Jitter, and Packet Size: Empirical Relationships for VoIP

Cited by 11 publications

References 10 publications

Research on Aggregation and Propagation of Self-Similar Traffic in Satellite Network

Research on Aggregation and Propagation of Self-Similar Traffic in Satellite Network

Generalized Hurst Hypothesis: Description of Time-Series in Communication Systems

Long-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis

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