Stochastic bounds and histograms for network performance analysis

Farah Aït-Salaht, Hind Castel-Taleb, Jean Michel Fourneau, Nihal Pekergin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Exact analysis of queueing networks under real traffic histograms becomes quickly intractable due to the state explosion. In this paper, we propose to apply the stochastic comparison method to derive performance measure bounds under histogram-based traffics. We apply an algorithm based on dynamic programming to derive bounding traffic histograms on reduced state spaces. We indeed obtain easier bounding stochastic processes providing stochastic upper and lower bounds on buffer occupancy histograms (queue length distributions) for finite queue models. We evaluate the proposed method under real traffic traces, and we compare the results with those obtained by an approximative method. Numerical results illustrate that the proposed method provides more accurate results with a tradeoff between computation time and accuracy. Moreover, the derived performance bounds are very relevant in network dimensioning.

Original languageEnglish
Title of host publicationComputer Performance Engineering - 10th European Workshop, EPEW 2013, Proceedings
PublisherSpringer Verlag
Pages13-27
Number of pages15
ISBN (Print)9783642407246
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event10th European Workshop on Performance Engineering, EPEW 2013 - Venice, Italy
Duration: 16 Sept 201317 Sept 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8168 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th European Workshop on Performance Engineering, EPEW 2013
Country/TerritoryItaly
CityVenice
Period16/09/1317/09/13

Keywords

  • Histogram-based traffic models
  • Network QoS
  • Stochastic Comparison

Fingerprint

Dive into the research topics of 'Stochastic bounds and histograms for network performance analysis'. Together they form a unique fingerprint.

Cite this