Abstract
Recent measurement studies of network traffic and variable bit rate video indicate that the traffic exhibits long-range dependence (LRD). It becomes more and more important to model this kind of traffic. This paper presents a traces-generating framework based on TES (Transform-Expand-Samples) and simple synthetic Markov-Gaussian processes for modeling LRD traffic with variability over several time scales. All of the traffic studies showed that the measurement exhibits approximate second-order self-similarity. The network resource is limited and the real long-range dependent traffic has no room under the circumstances. The proposed framework can fit both the probability density function of the empirical traces and the autocorrelation function spanning over several time scales. Besides, we discuss the validity of approximate LRD modeling with the short-range-dependent approaches.
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Li, IH. (2008). Modeling Long-Range Dependent VBR Traffic Using Synthetic Markov-Gaussian TES Models. In: Balandin, S., Moltchanov, D., Koucheryavy, Y. (eds) Next Generation Teletraffic and Wired/Wireless Advanced Networking. NEW2AN 2008. Lecture Notes in Computer Science, vol 5174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85500-2_12
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DOI: https://doi.org/10.1007/978-3-540-85500-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85499-9
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