Limitations of a Mapping Algorithm with Fragmentation Mimics (MAFM) when modeling statistical data sources based on measured packet network traffic

Fras, Matjaž; Mohorko, J.; Čučej, Ž.

doi:10.1016/j.comnet.2013.07.032

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…For values 0.5 < H < 1 , the autocorrelation function r(k) decays hyperbolically to ck −2H−2 as k increases, which means that the autocorrelation function is not summable [6]. This is opposite to the property of shortrange dependence (SRD), where the autocorrelation function decays exponentially, and the equation 2has a finite value.…”

Section: Long-range Dependence (Lrd)mentioning

confidence: 92%

Generalized Self-Similar First Order Autoregressive Generator (GSFO-ARG) for Internet Traffic

Popoola

Yahya

Popoola

et al. 2020

Stat., optim. inf. comput.

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Internet traffic data such as the number of transmitted packets and time spent on the transmission of Internet protocols (IPs) have been shown to exhibit self-similar property which can contain the long memory property, particularly in a heavy Internet traffic. Simulating this type of dataset is an important aspect of delay avoidance planning, especially when trying to mimic real-life processing of packets on the Internet. Most of the existing procedures often assumed the process follows a Gaussian distribution, and thus long memory processes such as Fractional Brownian Motion (FBM) and Fractional Gaussian Noise (FGN) among others are used. These approaches often result in estimation errors arising from the use of inappropriate distribution. However, it has been established that the distribution of Internet processes are heavy-tailed. Therefore, in this paper, a new method that is capable of generating heavy-tailed self-similar traffic is proposed based on the first-order autoregressive AR (1) process. The proposed method is compared with some of the existing methods at varying values of the self-similar index and sample sizes. The imposed self-similarity indices were estimated using the Range/Standard deviation statistic (R/S). Performance analysis was achieved using the absolute percentage errors. The results showed that the proposed method has a lower average error when compared with other competing methods.

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Section: Long-range Dependence (Lrd)mentioning

confidence: 92%

Generalized Self-Similar First Order Autoregressive Generator (GSFO-ARG) for Internet Traffic

Popoola

Yahya

Popoola

et al. 2020

Stat., optim. inf. comput.

View full text Add to dashboard Cite

show abstract

“…Messages are transmitted from ES1, ES2 and ES3 to ES4 through SW1 and SW2. In this case, messages of all VLs are generated according to Pareto and exponential distributions, which form a typical self-similar traffic and is frequently used in network traffic analysis (see Addie et al [2], Field et al [10], Nadarajah [27], Yamkhin [37], and Fras et al [11,12] for details). Our proposed algorithm is applicable to other heavytailed traffic distributions only if its background traffic is self-similar.…”

Section: Case Studymentioning

confidence: 99%

A delay reliability estimation method for Avionics Full Duplex Switched Ethernet based on stochastic network calculus

Wu¹,

Huang²,

Li³

et al. 2015

Eksploatacja i Niezawodnosc - Maintenance and Reliability

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Limitations of a Mapping Algorithm with Fragmentation Mimics (MAFM) when modeling statistical data sources based on measured packet network traffic

Cited by 2 publications

References 36 publications

Generalized Self-Similar First Order Autoregressive Generator (GSFO-ARG) for Internet Traffic

Generalized Self-Similar First Order Autoregressive Generator (GSFO-ARG) for Internet Traffic

A delay reliability estimation method for Avionics Full Duplex Switched Ethernet based on stochastic network calculus

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