Computer network traffic analysis with the use of statistical self-similarity factor

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Computer network traffic analysis with the use of statistical self-similarity factor

The optimal computer network performance models require accurate traffic models, which can capture the statistical characteristic of actual traffic. If the traffic models do not represent traffic accurately, one may overestimate or underestimate the network performance. The paper presents confirmation of the self-similar nature of the selected protocols in the computer network communication lay...

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Self-similarity in network traffic

Recent studies have shown that various types of network traffic exhibit long-term dependency or self-similarity, or fractal-like behavior. Thus, to say that network traffic exhibits self-similarity means that if we look at the network traffic during a given time interval and choose a subinterval at random, the graph of the network traffic vs. time in the subinterval looks like the graph of netw...

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On the Relevance of Self-Similarity in Network Traffic Prediction

Self-similarity is an important characteristic of traffic in high speed networks which can not be captured by traditional traffic models. Traffic predictors based on non-traditional long-memory models are computationally more complex than traditional predictors based on short-memory models. Even online estimation of their parameters for actual traffic traces is not trivial work. Based on the ob...

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On Reducing the Degree of Self-Similarity in Network Traffic

While it is well known that self-similarity in network traffic leads to larger queueing delays, higher drop rates and extended periods of congestion, reducing the selfsimilarity of traffic and consequently eliminating its harmful effects has remained an open problem. In this paper we present some techniques to reduce the degree of selfsimilarity of network traffic, specifically by reducing two ...

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ژورنال

عنوان ژورنال: Annales UMCS, Informatica

سال: 2013

ISSN: 2083-3628,1732-1360

DOI: 10.2478/v10065-012-0040-0