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Record length requirement of long-range dependent teletraffic

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  • Li, Ming

Abstract

This article contributes the highlights mainly in two folds. On the one hand, it presents a formula to compute the upper bound of the variance of the correlation periodogram measurement of teletraffic (traffic for short) with long-range dependence (LRD) for a given record length T and a given value of the Hurst parameter H (Theorems 1 and 2). On the other hand, it proposes two formulas for the computation of the variance upper bound of the correlation periodogram measurement of traffic of fractional Gaussian noise (fGn) type and the generalized Cauchy (GC) type, respectively (Corollaries 1 and 2). They may constitute a reference guideline of record length requirement of traffic with LRD. In addition, record length requirement for the correlation periodogram measurement of traffic with either the Schuster type or the Bartlett one is studied and the present results about it show that both types of periodograms may be used for the correlation measurement of traffic with a pre-desired variance bound of correlation estimation. Moreover, real traffic in the Internet Archive by the Special Interest Group on Data Communication under the Association for Computing Machinery of US (ACM SIGCOMM) is analyzed in the case study in this topic.

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  • Li, Ming, 2017. "Record length requirement of long-range dependent teletraffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 164-187.
    • Handle: RePEc:eee:phsmap:v:472:y:2017:i:c:p:164-187
    DOI: 10.1016/j.physa.2016.12.069
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    3. Song, Wanqing & Li, Ming & Li, Yuanyuan & Cattani, Carlo & Chi, Chi-Hung, 2019. "Fractional Brownian motion: Difference iterative forecasting models," Chaos, Solitons & Fractals, Elsevier, vol. 123(C), pages 347-355.
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    5. Liu, He & Song, Wanqing & Li, Ming & Kudreyko, Aleksey & Zio, Enrico, 2020. "Fractional Lévy stable motion: Finite difference iterative forecasting model," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    6. Li, Ming, 2021. "Generalized fractional Gaussian noise and its application to traffic modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    7. Xiaoling Tao & Yang Peng & Feng Zhao & Peichao Zhao & Yong Wang, 2018. "A parallel algorithm for network traffic anomaly detection based on Isolation Forest," International Journal of Distributed Sensor Networks, , vol. 14(11), pages 15501477188, November.

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