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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
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Matching SDN and Legacy Networking Hardware for Energy Efficiency and Bounded Delay.
Pablo Fondo-Ferreiro1, Miguel Rodríguez-Pérez2, Manuel Fernández-Veiga2
1atlanTTic Research Center, University of Vigo, 36310 Vigo, Spain. pfondo@det.uvigo.es.
Sensors (Basel, Switzerland)
|November 16, 2018
Summary
This study introduces a power-saving algorithm for networking equipment using Software-Defined Networking (SDN). It effectively reduces energy consumption in Ethernet links while maintaining quality of service for data centers and access networks.
Area of Science:
- Computer Science
- Networking
- Energy Efficiency
Background:
- Growing economic and environmental concerns necessitate energy efficiency in networking equipment.
- Existing energy-saving proposals face implementation challenges due to hardware inflexibility and vendor reluctance.
- Software-Defined Networking (SDN) offers unprecedented flexibility and control over network switching decisions.
Purpose of the Study:
- To explore the convergence of energy efficiency research and SDN.
- To present a practical power-saving algorithm leveraging SDN capabilities.
- To reduce operational costs in data centers and wired access networks.
Main Methods:
- Adapting a theoretical power-saving algorithm for implementation within an SDN framework.
- Focusing on minimizing energy consumption in bundles of energy-efficient Ethernet links.
- Evaluating algorithm performance based on energy efficiency and Quality of Service (QoS) metrics, including delay-sensitive traffic.
Main Results:
- The developed SDN-based algorithm closely approximates theoretical energy-saving results.
- The algorithm effectively minimizes energy consumption in Ethernet links.
- Performance is maintained even when considering the needs of delay-sensitive traffic.
Conclusions:
- SDN provides a viable platform for implementing advanced energy-saving solutions in networking.
- The proposed algorithm offers a practical approach to reducing operational costs and environmental impact.
- Energy efficiency can be achieved without compromising critical QoS requirements.
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