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Rethinking max-min planning on energy-efficient software-defined networking for 5G networks.

Dingde Jiang1, Bowen Zhu2, Junyang Sun3

  • 1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China. jiangdd@uestc.edu.cn.

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|October 29, 2024
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Summary
This summary is machine-generated.

This study presents a new framework for energy-efficient 5G networks. It optimizes network-wide energy use by balancing connectivity and traffic flow, improving overall performance.

Keywords:
5G networksEnergy efficiency networksIntelligent networkingMax–min planningSoftware-defined networking

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Area of Science:

  • Computer Science
  • Telecommunications Engineering

Background:

  • Energy efficiency is crucial for 5G networks, impacting environmental, financial, and performance factors.
  • Current approaches focus on link/node-level optimization, potentially causing network-wide inefficiencies and performance degradation.

Purpose of the Study:

  • To develop a novel energy-efficient framework for 5G networks.
  • To integrate network connectivity with maximum network flow for minimum energy consumption.
  • To address dynamic traffic demands and network topology for holistic energy efficiency.

Main Methods:

  • Rethinking the max-min planning framework for energy-efficient software-defined networking.
  • Combining network connectivity (control plane) with maximum network flow (data plane).
  • Implementing a dynamic max-min planning framework based on real-time traffic demands.

Main Results:

  • The proposed framework effectively balances network connectivity and traffic flow.
  • Achieved significant improvements in network-wide energy efficiency.
  • Demonstrated enhanced performance compared to existing methods.

Conclusions:

  • The dynamic max-min planning framework offers a viable solution for network-wide energy efficiency in 5G.
  • Integrating connectivity and flow optimization is key to overcoming limitations of node/link-centric approaches.
  • The framework provides a foundation for more sustainable and performant intelligent networks.