Multifractal Internet Traffic Model and Active Queue Management

Abstract

We propose a multilevel (hierarchical) ON/OFF model to simultaneously capture the mono/multifractal behavior of Internet traffic. Parameter estimation methods are developed and applied to estimate the model parameters from real traces. Wavelet analysis and simulation results show that the synthetic traffic (using this new model with estimated parameters) and real traffic share the same statistical properties and queuing behaviors. Based on this model and its statistical properties, as described by the Logscale diagram of traces, we propose an efficient method to predict the queuing behavior of FIFO and RED queues. In order to satisfy a given delay and jitter requirement for real time connections, and to provide high goodput and low packet loss for non-real time connections, we also propose a parallel virtual queue control structure to offer differential quality of services. This new queue control structure is modeled and analyzed as a regular nonlinear dynamic system. The conditions for system stability and optimization are found (under certain simplifying assumptions) and discussed. The theoretical stationary distribution of queue length is validated by simulation.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA440709

Entities

People

  • Jia-shiang Jou

Organizations

  • University of Maryland

Tags

Communities of Interest

  • C4I
  • Counter WMD
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Satellites
  • Brownian Motion
  • Channel Allocation
  • Data Science
  • Detection
  • Estimators
  • Gaussian Processes
  • Information Processing
  • Information Science
  • Internet
  • Networks
  • Normal Distribution
  • Order Statistics
  • Packet Loss
  • Power Spectra
  • Random Variables

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Computer Networking
  • Mathematical Modeling and Probability Theory.