survey

Survey on Load-Balancing Methods in 802.11 Infrastructure Mode Wireless Networks for Improving Quality of Service

Authors:

Teck-Chaw Ling,

Aung Htein Maw,

Su Thawda WinAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 51, Issue 2

Article No.: 34, Pages 1 - 21

https://doi.org/10.1145/3172868

Published: 20 February 2018 Publication History

Abstract

Traffic load in any 802.11 infrastructure mode network is typically distributed unevenly between access points (APs), creating hotspots. This is due to the inherent nature of wireless area networks (WLANs), where stations are free to associate to any known AP they desire, and the lack of control by the APs themselves. This imbalance creates a condition where affected APs in the network suffer traffic congestion while others are underutilized, leading to stations experiencing lower throughput, longer latency, and operating below the network potential capacity. To alleviate this problem, some form of load balancing is required to redistribute the work load among other available APs in the wireless network. This article presents a survey of the various works done in performing load balancing in an infrastructure mode wireless network and will cover the common methods including admission control, association management, cell breathing, and association control. Updates to the IEEE standards are also presented that support load-balancing efforts. Finally, software-defined networks are investigated to determine the extent of control integration to support managing and load-balancing WLANs. Trends in load-balancing research are also uncovered that indicate how the introduction of new wireless standards influences the amount of research.

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Index Terms

Survey on Load-Balancing Methods in 802.11 Infrastructure Mode Wireless Networks for Improving Quality of Service
1. Networks

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 51, Issue 2

March 2019

748 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3186333

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Copyright © 2018 ACM.

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Publication History

Published: 20 February 2018

Accepted: 01 December 2017

Revised: 01 December 2017

Received: 01 July 2016

Published in CSUR Volume 51, Issue 2

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https://dl.acm.org/doi/10.1186/s13677-024-00651-7
Berceanu CPătrașcu M(2022)Engineering Emergence: A Survey on Control in the World of Complex NetworksAutomation10.3390/automation30100093:1(176-196)Online publication date: 10-Mar-2022
https://doi.org/10.3390/automation3010009
Wu WLiu YYao JFang XShan FYang MLing ZLuo J(2022)Learning-aided client association control for high-density WLANsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2022.109043212:COnline publication date: 20-Jul-2022
https://dl.acm.org/doi/10.1016/j.comnet.2022.109043
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Qureshi KWang LSun LZhu CShu L(2020)A Review on Design and Implementation of Software-Defined WLANsIEEE Systems Journal10.1109/JSYST.2019.296040014:2(2601-2614)Online publication date: Jun-2020
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