Sampling vs sketching: An information theoretic comparison

Tune, Paul; Veitch, Darryl

doi:10.1109/infcom.2011.5935020

Cited by 26 publications

(23 citation statements)

References 25 publications

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“…Network traffic [5] over high-speed Internet typically can possess the characteristics self-similarity and long range dependence, and simultaneously exhibit high bursty over different time scales. It is a challenging problem to measure the network traffic directly in real networks [6]. Network traffic modeling and prediction have become a research hotspot and draw widespread attention.…”

Section: Introductionmentioning

confidence: 99%

Predicting Bursty Network Traffic with Self-similarity Characteristic over Echo State Covariation Orthogonality Network

Sun¹,

Li²,

Zhang³

2017

IJMUE

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Section: Introductionmentioning

confidence: 99%

Predicting Bursty Network Traffic with Self-similarity Characteristic over Echo State Covariation Orthogonality Network

Sun¹,

Li²,

Zhang³

2017

IJMUE

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“…Tune et al used the information theoretic to describe network traffic [8]. Takeda et al used the partial measurement information to estimate the end-to-end traffic matrix and proposed the corresponding prediction algorithm [6].…”

Section: Introductionmentioning

confidence: 99%

“…Step 6: According to Equation (8), perform the inversion transformation of the wavelet, and then attain the low-, medium-, and high-frequency signals of () Step 7: According to Equations (9)- (11), build the models corresponding to the low-, medium-, and high-frequency signals of () Step 9 According to Equations (12), calculate the timedomain signal () zt % .…”

mentioning

confidence: 99%

A Wavelet Analysis-Based Dynamic Prediction Algorithm to Network Traffic

Meng

Zhao

et al. 2016

ITM Web Conf.

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Abstract. Network traffic is a significantly important parameter for network traffic engineering, while it holds highly dynamic nature in the network. Accordingly, it is difficult and impossible to directly predict traffic amount of end-to-end flows. This paper proposes a new prediction algorithm to network traffic using the wavelet analysis. Firstly, network traffic is converted into the time-frequency domain to capture timefrequency feature of network traffic. Secondly, in different frequency components, we model network traffic in the time-frequency domain. Finally, we build the prediction model about network traffic. At the same time, the corresponding prediction algorithm is presented to attain network traffic prediction. Simulation results indicates that our approach is promising.

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“…However, Equations (1) and (2) only represent the statistical nature of the end-to-end traffic x . As mentioned in [10][11][12], the end-to-end network traffic holds the correlation property.…”

mentioning

confidence: 99%

A Bayes Theory-Based Modeling Algorithm to End-to-end Network Traffic

et al. 2016

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Abstract.Recently, network traffic has exponentially increasing due to all kind of applications, such as mobile Internet, smart cities, smart transportations, Internet of things, and so on. the end-to-end network traffic becomes more important for traffic engineering. Usually end-to-end traffic estimation is highly difficult. This paper proposes a Bayes theory-based method to model the end-to-end network traffic. Firstly, the end-to-end network traffic is described as a independent identically distributed normal process. Then the Bases theory is used to characterize the end-to-end network traffic. By calculating the parameters, the model is determined correctly. Simulation results show that our approach is feasible and effective.

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Sampling vs sketching: An information theoretic comparison

Cited by 26 publications

References 25 publications

Predicting Bursty Network Traffic with Self-similarity Characteristic over Echo State Covariation Orthogonality Network

Predicting Bursty Network Traffic with Self-similarity Characteristic over Echo State Covariation Orthogonality Network

A Wavelet Analysis-Based Dynamic Prediction Algorithm to Network Traffic

A Bayes Theory-Based Modeling Algorithm to End-to-end Network Traffic

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