Scalable model for packet loss analysis of load-balancing switches with identical input processes

Yury Audzevich, Levente Bodrog, Yoram Ofek, Miklós Telek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper we present a scalable approximate model for packet loss analysis in load-balancing Birkhof-von Neumann switch with finite buffers and variable length packets assumption. We also present a numerical method to solve the model for large switches (up to the size ~30) equipped with large buffers (up to the buffer size ~1000). With regards to previously introduced models the main contribution of our model is its scalability in terms of the switch size as its computational complexity is linear with the number of ports. Contrary to previous models we assumed homogeneous input processes in this paper.

Original languageEnglish
Title of host publicationAnalytical and Stochastic Modeling Techniques and Applications - 16th International Conference, ASMTA 2009, Proceedings
Pages249-263
Number of pages15
DOIs
Publication statusPublished - Aug 20 2009
Event16th International Conference on Analytical and Stochastic Modeling Techniques and Applications, ASMTA 2009 - Madrid, Spain
Duration: Jun 9 2009Jun 12 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5513 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Analytical and Stochastic Modeling Techniques and Applications, ASMTA 2009
CountrySpain
CityMadrid
Period6/9/096/12/09

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Audzevich, Y., Bodrog, L., Ofek, Y., & Telek, M. (2009). Scalable model for packet loss analysis of load-balancing switches with identical input processes. In Analytical and Stochastic Modeling Techniques and Applications - 16th International Conference, ASMTA 2009, Proceedings (pp. 249-263). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5513 LNCS). https://doi.org/10.1007/978-3-642-02205-0_18