Long-range dependence - Ten years of Internet traffic modeling

Department of Computer Science and Engineering, University of California, Riverside, Riverside, California, United States
IEEE Internet Computing (Impact Factor: 1.71). 10/2004; 8(5):57- 64. DOI: 10.1109/MIC.2004.46
Source: DBLP


Self-similarity and scaling phenomena have dominated Internet traffic analysis for the past decade. With the identification of long-range dependence (LRD) in network traffic, the research community has undergone a mental shift from Poisson and memory-less processes to LRD and bursty processes. Despite its widespread use, though, LRD analysis is hindered by the difficulty of actually identifying dependence and estimating its parameters unambiguously. The authors outline LRD findings in network traffic and explore the current lack of accuracy and robustness in LRD estimation. In addition, they present recent evidence that packet arrivals appear to be in agreement with the Poisson assumption in the Internet core.

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    • "Complex systems with inherent LRD have been described in many other fields, and its mathematical concepts are fruitfully employed. Numerous studies demonstrate LRD for example in geological and climate research (Scheffer et al., 2009; Varotsos and Kirk-Davidoff, 2006), finance market fluctuations (Matteo et al., 2005; Robinson, 2003), in Internet modeling and network traffic analysis (Abry et al., 2002; Karagiannis et al., 2004; Riedi et al., 1999), or statistic analyses of human language (Alvarez-Lacalle et al., 2006; Petersen et al., 2012). Fig. 1A shows an example of an ethernet bytes-per-time arrival, at two different aggregation levels. "
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    • "Heterogeneous M2M services form an aggregated traffic flow as traversing network serving nodes. It was confirmed that the aggregated traffic flow has the characteristics of burstiness and Long Range Dependence (LRD) [1] [2]. How to model the aggregated traffic flow has remained an open research issue for many years. "

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    • "The latest findings in the field of neurobiology concerning the functional Magnetic Resonance Imaging technique, suggest that the Hurst coefficient (see definition below) can be used as a biomarker for certain disorders including Alzheimer (Maxim et al. 2005), Parkinson (Rassouli 2006), autism (Lai 2010) and impulsive traits (Hahn et al. 2012). Finally, it is remarkable that scaling behaviour has been also identified in certain artificial processes, such as network traffic (Leeland 1994; Karagiannis et al. 2004) or econometrics (Lo 1991; Baille 1996; Serinaldi 2010). For instance, in the field of economy, the scaling behaviour has been detected in energy prices (Serletis and Rosenberg 2007), financial markets (Gama et al. 2008) and Spanish electricity spot market (Norouzzadeh et al. 2007). "
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