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Exploring Collective Dynamics in Communication Networks.

Jian Yuan1, Kevin Mills1

  • 1National Institute of Standards and Technology, Gaithersburg, MD 20899-0001.

Journal of Research of the National Institute of Standards and Technology
|July 23, 2016
PubMed
Summary
This summary is machine-generated.

This study models network congestion using cellular automata, revealing long-range dependence (LRD) in traffic flows. Larger network sizes and collective dynamics are crucial for understanding emergent network phenomena.

Keywords:
cellular automatacollective dynamicscommunication networkscomplex systemcongestion controlemergencelong-range dependencemodeling and simulationnetwork traffic

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Area of Science:

  • Network Science
  • Complex Systems
  • Computational Science

Background:

  • Communication networks exhibit emergent phenomena from node interactions.
  • Understanding network congestion dynamics is vital for network performance.
  • Cellular automata offer a modeling approach for complex network behaviors.

Purpose of the Study:

  • To model network congestion using a 2D cellular automaton.
  • To investigate emergent phenomena and spatial-temporal evolution of network congestion.
  • To characterize correlation in congestion behavior across different system sizes and time granularities.

Main Methods:

  • Modeling communication networks as 2D cellular automata.
  • Analyzing dynamic patterns of traffic flows under various parameters and congestion-control algorithms.
  • Characterizing long-range dependence (LRD) in congestion behavior at different scales.

Main Results:

  • Long-range dependence (LRD) was observed at specific time granularities.
  • LRD decays with increasing time granularity for a fixed network size.
  • LRD extends to larger time scales as network size increases.
  • Sub-areas within larger networks exhibit stronger LRD than isolated networks of comparable size.

Conclusions:

  • Cellular automaton models can elucidate emergent network phenomena like congestion.
  • Network size significantly influences the manifestation of long-range dependence.
  • Collective dynamics are more pronounced in larger, interconnected network models.
  • Sufficiently large network models are essential for accurately studying collective dynamics.