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The influence of self-similar traffic on telecommunication network queuing

Published: 18 June 2009 Publication History

Abstract

The self-similar network traffic influence on the telecommunication queuing systems with infinite and finite buffer sizes in terms of the average queuing size and buffer overflow probability is investigated in this paper. The obtained results show that the average queue length and overflow probability of queuing systems with self-similar input traffic are much higher than the classical queuing systems. The investigated parameters are much higher, when the degrees of self-similarity (Hurst parameter) increase. The results confirm that self-similar traffic has serious implication on telecommunication network queuing.

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Paxson, V. "Fast, Approximate Synthesis of Fractional Gaussian Noise for Generating self-similar network traffic", ACM SIGCOMM, 1997.
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Rincon, D., S. Sallent. "Characterizing fractal traffic with redundant wavelet-based transform". Submitted to IEEE Globecom, 2004.
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CompSysTech '09: Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing
June 2009
653 pages
ISBN:9781605589862
DOI:10.1145/1731740
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2009

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Author Tags

  1. Hurst parameter
  2. long-range dependence
  3. queuing system
  4. self-similar traffic

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CompSysTech '09

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Overall Acceptance Rate 241 of 492 submissions, 49%

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