Let's adapt to network change: Towards energy saving with rate adaptation in SDN

Zemmouri, Samy; Vakilinia, Shahin; Cheriet, Mohamed

doi:10.1109/cnsm.2016.7818431

Cited by 4 publications

(1 citation statement)

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“…The idea in Reference [14] is to reroute flows on existing paths to adjust link loads in a way that LRAA-based energy saving is maximised. In the scope of this study, the idea mentioned above is defined as a Mixed Linear Integer Problem (MILP).…”

Section: Related Workmentioning

confidence: 99%

Energy aware adaptive resource management model for software‐defined networking‐based service provider networks

Çelenlioğlu

Mantar

2021

IET Networks

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Service provider networks (SPNs) seek to implement the resource management mechanisms called load balancing and energy saving to satisfy today's networking demands. Software-defined networking (SDN) allows SPNs to perform agile and efficient networking operations. It is still an open issue on how to implement these resource management mechanisms in SDN-based SPNs. An energy-aware adaptive resource management model (ERM) for SDN-based SPNs is proposed. Load balancing aims to distribute overall network traffic volume as fairly as possible among available links. Energy-saving aims to turn off as many network devices and links as possible. Unlike the works in the literature, our model utilises the trade-off between load balancing and energy efficiency. To do so, the controller establishes paths among edge pairs. The controller aggregates traffic load between edge pairs to as few links as possible based on a predefined load level. Then, unused network elements switch to sleep mode. In the case of load balancing, the controller distributes traffic load among active network resources. Experimental results show that ERM can successfully increase both energy-saving and resource utilization even under heavy traffic loads. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%