Stochastic Bounds and Histograms for Network Performance Analysis
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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.
KeywordsNetwork QoS Histogram-based traffic models Stochastic Comparison
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