extended-abstract

Experimenting ResourceonDemand Strategies for Green WLANs

Authors:

Fikru Getachew Debele,

Nanfang Li,

Michela Meo,

Marco Ricca,

Yi ZhangAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 42, Issue 3

Pages 61 - 66

https://doi.org/10.1145/2695533.2695557

Published: 08 December 2014 Publication History

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Abstract

WLAN becomes a necessary facility for exible Internet provisioning in enterprise networks. Usually this connection provisioning is designed to handle a peak load with high density of Access Points (APs) to guarantee a desired level of quality of service (QoS). This design approach however leads to energy inefficiency due to the daily demand variability. In fact the off-peak period of the daily behavior is usually quite long and hence some resources consume power without any beneficiary activity. Resource-on-demand (RoD) provisioning is among foreseeable solutions that satisfy both energy efficiency as well as QoS constraints. That is the network capacity is dynamically dimensioned to demand while extra resource goes to low energy consumption mode to save energy. In dense WLAN scenarios, extra APs are turned off during low load until required due to increase in demand.

Some theoretical analysis of RoD has been proposed by related works, but experiments on real production networks are still needed in order to investigate the practical issues related to RoD strategies. This paper addresses some of these issues through an experimental activity performed on RoD strategies. Two strategies, namely association-based and traffic-based policies, are implemented in one part of Politecnico di Torino Campus WLAN. Results from our testbed show that RoD strategies are effective and energy saving up to 70% is possible.

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Apostolo GBernardini FMagalhães LMuchaluat-Saade D(2022)eSCIFI: An Energy Saving Mechanism for WLANs Based on Machine LearningEnergies10.3390/en1502046215:2(462)Online publication date: 10-Jan-2022
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Apostolo GBernardini FMagalhaes LMuchaluat-Saade D(2021)A Unified Methodology to Predict Wi-Fi Network Usage in Smart BuildingsIEEE Access10.1109/ACCESS.2020.30488919(11455-11469)Online publication date: 2021
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Kim SLee KKim YShin JShin SChong S(2020)Dynamic Control for On-Demand Interference-Managed WLAN InfrastructuresIEEE/ACM Transactions on Networking10.1109/TNET.2019.295359728:1(84-97)Online publication date: Feb-2020
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Experimenting ResourceonDemand Strategies for Green WLANs
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cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 42, Issue 3

December 2014

80 pages

ISSN:0163-5999

DOI:10.1145/2695533

Editor:
Giuliano Casale
Imperial College London, London

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Association for Computing Machinery

New York, NY, United States

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Published: 08 December 2014

Published in SIGMETRICS Volume 42, Issue 3

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Apostolo GBernardini FMagalhães LMuchaluat-Saade D(2022)eSCIFI: An Energy Saving Mechanism for WLANs Based on Machine LearningEnergies10.3390/en1502046215:2(462)Online publication date: 10-Jan-2022
https://doi.org/10.3390/en15020462
Apostolo GBernardini FMagalhaes LMuchaluat-Saade D(2021)A Unified Methodology to Predict Wi-Fi Network Usage in Smart BuildingsIEEE Access10.1109/ACCESS.2020.30488919(11455-11469)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2020.3048891
Kim SLee KKim YShin JShin SChong S(2020)Dynamic Control for On-Demand Interference-Managed WLAN InfrastructuresIEEE/ACM Transactions on Networking10.1109/TNET.2019.295359728:1(84-97)Online publication date: Feb-2020
https://doi.org/10.1109/TNET.2019.2953597
Bluemm CZhang YAlvarez PRuffini MDaSilva L(2017)Dynamic energy savings in Cloud-RAN: An experimental assessment and implementation2017 IEEE International Conference on Communications Workshops (ICC Workshops)10.1109/ICCW.2017.7962755(791-796)Online publication date: May-2017
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Li FWang XCao JWang RBi Y(2017)A State Transition-Aware Energy-Saving Mechanism for Dense WLANs in BuildingsIEEE Access10.1109/ACCESS.2017.27701505(25671-25681)Online publication date: 2017
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Goudos SDeruyck MPlets DMartens LJoseph W(2017)Optimization of Power Consumption in 4G LTE Networks Using a Novel Barebones Self-adaptive Differential Evolution AlgorithmTelecommunications Systems10.1007/s11235-017-0279-266:1(109-120)Online publication date: 1-Sep-2017
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Bhola AJha C(2017)Power Analysis of a Network Using DECAP Algorithm by Varying Network Sizes and UsersInformation, Communication and Computing Technology10.1007/978-981-10-6544-6_7(68-75)Online publication date: 11-Oct-2017
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