Multiscale Queuing Analysis of Long-Range-Dependent Network Traffic

Files in this item

Files Size Format View
Rib2000Mar5Multiscale.PDF 1.053Mb application/pdf Thumbnail
Rib2000Mar5Multiscale.PS 661.9Kb application/postscript View/Open

Show full item record

Item Metadata

Title: Multiscale Queuing Analysis of Long-Range-Dependent Network Traffic
Author: Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Crouse, Matthew; Baraniuk, Richard G.
Type: Conference paper
xmlui.Rice_ECE.Keywords: long-range dependence (LRD); multifractal wavelet model (MWM); multiscale queuing analysis; Gaussian
Citation: V. J. Ribeiro, R. H. Riedi, M. Crouse and R. G. Baraniuk, "Multiscale Queuing Analysis of Long-Range-Dependent Network Traffic," vol. 2, pp. 1026-1035, 2000.
Abstract: Many studies have indicated the importance of capturing scaling properties when modeling traffic loads; however, the influence of long-range dependence (LRD) and marginal statistics still remains on unsure footing. In this paper, we study these two issues by introducing a multiscale traffic model and a novel multiscale approach to queuing analysis. The multifractal wavelet model (MWM) is a multiplicative, wavelet-based model that captures the positivity, LRD, and "spikiness" of non-Gaussian traffic. Using a binary tree, the model synthesizes an N-point data set with only O(N)computations. Leveraging the tree structure of the model, we derive a multiscale queuing analysis that provides a simple closed form approximation to the tail queue probability, valid for any given buffer size. The analysis is applicable not only to the MWM but to tree-based models in general, including fractional Gaussian noise. Simulated queuing experiments demonstrate the accuracy of the MWM for matching real data traces and the precision of our theoretical queuing formula. Thus, the MWM is useful not only for fast synthesis of data for simulation purposes but also for applications requiring accurate queuing formulas such as call admission control. Our results clearly indicate that the marginal distribution of traffic at different time-resolutions affects queuing and that a Gaussian assumption can lead to over-optimistic predictions of tail queue probability even when taking LRD into account.
Date Published: 2000-03-01

This item appears in the following Collection(s)

  • ECE Publications [1053 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.