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Self-organized criticality in a computer network model

Yuan1, Ren, Shan

  • 1Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.

Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
|October 25, 2000
PubMed
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Computer network nodes exhibit collective behavior, with constant load leading to power-law distributions in throughput and buffer content. This suggests self-organized criticality and explains network traffic self-similarity.

Area of Science:

  • Complex Systems
  • Network Science
  • Computational Physics

Background:

  • Understanding collective behavior in computer networks is crucial for optimizing performance.
  • Network traffic often exhibits complex patterns that are not fully explained by traditional models.

Purpose of the Study:

  • To investigate the collective dynamics of computer network nodes using a cellular automaton model.
  • To identify underlying statistical distributions and noise characteristics in network behavior.

Main Methods:

  • Utilized a cellular automaton model to simulate collective behavior of network nodes.
  • Analyzed spatial and temporal distributions of node throughput and buffer content.
  • Examined power spectrum analysis for system load fluctuations.

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Main Results:

  • Observed power-law distributions in both spatial and temporal aspects of node throughput and buffer content under constant network load.
  • Identified 1/f noise in the power spectrum of system load changes, indicative of self-organized criticality.
  • Demonstrated that power-law decay in buffer content suggests low probability of severe network congestion.

Conclusions:

  • The study provides evidence of self-organized criticality in computer networks.
  • Power-law distributions offer a potential explanation for the self-similar nature of computer network traffic.
  • Findings suggest that heavy congestion events are statistically rare.