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

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.