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Published on: November 26, 2019
Congestion and decongestion in a communication network
Brajendra K Singh1, Neelima Gupte
1Department of Physics, Indian Institute of Technology Madras, Chennai 600 036, India. braj@phys.sinica.edu.tw
Summary
We identified a coefficient of betweenness centrality (CBC) to measure network traffic and pinpoint congestion-causing hubs. Enhancing high-CBC hubs with assortative connections effectively relieves network congestion.
Area of Science:
- Network science
- Communication systems engineering
- Complex systems analysis
Background:
- Network traffic congestion arises from finite node capacities in communication networks.
- Understanding network dynamics is crucial for efficient data transmission.
Purpose of the Study:
- To investigate network traffic dynamics in a 2D network.
- To identify strategies for relieving network congestion.
- To introduce a novel metric for assessing traffic-related congestion.
Main Methods:
- Simulation of network traffic in a 2D network model.
- Analysis of node and hub capacities.
- Definition and application of a coefficient of betweenness centrality (CBC) to identify critical hubs.
- Evaluation of congestion relief strategies, including hub capacity manipulation and assortative connections.
Main Results:
- A coefficient of betweenness centrality (CBC) was defined as a direct measure of network traffic.
- CBC effectively identifies hubs prone to causing congestion.
- The addition of assortative connections to high-CBC hubs significantly relieved congestion.
- Manipulating hub capacity and connections are viable strategies for congestion management.
Conclusions:
- The coefficient of betweenness centrality (CBC) is a valuable tool for network congestion analysis.
- Strategic enhancement of high-centrality hubs can efficiently mitigate network congestion.
- Findings offer insights into optimizing communication network design and performance.
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