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ExBox: Experience Management Middlebox for Wireless Networks

Authors:

Ayon Chakraborty,

Shruti Sanadhya,

Kyu-Han KimAuthors Info & Claims

CoNEXT '16: Proceedings of the 12th International on Conference on emerging Networking EXperiments and Technologies

Pages 145 - 159

https://doi.org/10.1145/2999572.2999597

Published: 06 December 2016 Publication History

Abstract

Enterprise wireless networks face significant challenges to deliver Quality-of-Experience (QoE) with the variety of mobile applications. One of the fundamental challenges is that the traditional definition of network capacity (often defined as throughput capacity) is not sufficient to reflect applications' requirements in wireless networks. In this paper, we propose to rethink the network capacity of wireless networks to better incorporate QoE. Specifically, we first propose a novel concept of an Experiential Capacity Region (ExCR) for wireless networks. ExCR is defined as a set of simultaneous application flows whose QoE requirements can be satisfied by the network. Next, we present the infrastructure based ExBox system that measures per-application QoE metrics and determines the ExCR for wireless networks to better serve a set of mobile application flows. In its core, ExBox employs light-weight machine learning techniques that are tailored for dynamic wireless environments. Through both large-scale simulations and extensive real-life experiments on WiFi and LTE networks, we show that ExBox delivers QoE in admission control decision with a precision of ≈ 0.8 - 0.9, even when clients experience diverse channel quality. Moreover, ExBox quickly adapts to changing network environments without much overhead.

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Cited By

Szott SKosek-Szott KGawlowicz PGomez JBellalta BZubow ADressler F(2022)Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine LearningIEEE Communications Surveys & Tutorials10.1109/COMST.2022.317924224:3(1843-1893)Online publication date: Nov-2023
https://doi.org/10.1109/COMST.2022.3179242
Morshedi MNoll J(2021)Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine LearningFuture Internet10.3390/fi1303006313:3(63)Online publication date: 3-Mar-2021
https://doi.org/10.3390/fi13030063
Iwai TNakao A(2021)Bandwidth Allocation with Slice Quality Fairness in Network Slicing under Variable Link Capacity2021 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOBECOM46510.2021.9685362(1-6)Online publication date: Dec-2021
https://doi.org/10.1109/GLOBECOM46510.2021.9685362
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Index Terms

ExBox: Experience Management Middlebox for Wireless Networks
1. Networks

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Published In

cover image ACM Conferences

CoNEXT '16: Proceedings of the 12th International on Conference on emerging Networking EXperiments and Technologies

December 2016

524 pages

ISBN:9781450342926

DOI:10.1145/2999572

General Chairs:
Athina Markopoulou
University of California, Irvine, USA
,
Michalis Faloutsos
University of California, Riverside, USA
,
Program Chairs:
Vyas Sekar
Carnegie Mellon University, USA
,
Dejan Kostic
KTH Royal Institute of Technology, Sweden

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 December 2016

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CoNEXT '16

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SIGCOMM

CoNEXT '16: The 12th International Conference on emerging Networking EXperiments and Technologies

December 12 - 15, 2016

California, Irvine, USA

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CoNEXT '16 Paper Acceptance Rate 30 of 160 submissions, 19%;

Overall Acceptance Rate 198 of 789 submissions, 25%

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Cited By

Szott SKosek-Szott KGawlowicz PGomez JBellalta BZubow ADressler F(2022)Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine LearningIEEE Communications Surveys & Tutorials10.1109/COMST.2022.317924224:3(1843-1893)Online publication date: Nov-2023
https://doi.org/10.1109/COMST.2022.3179242
Morshedi MNoll J(2021)Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine LearningFuture Internet10.3390/fi1303006313:3(63)Online publication date: 3-Mar-2021
https://doi.org/10.3390/fi13030063
Iwai TNakao A(2021)Bandwidth Allocation with Slice Quality Fairness in Network Slicing under Variable Link Capacity2021 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOBECOM46510.2021.9685362(1-6)Online publication date: Dec-2021
https://doi.org/10.1109/GLOBECOM46510.2021.9685362
Iwai TNakao A(2020)Progressive Slicing for Application Identification in Application-Specific Network SlicingGLOBECOM 2020 - 2020 IEEE Global Communications Conference10.1109/GLOBECOM42002.2020.9348019(1-6)Online publication date: Dec-2020
https://doi.org/10.1109/GLOBECOM42002.2020.9348019
Morshedi MNoll J(2020)A Survey on Prediction of PQoS Using Machine Learning on Wi-Fi Networks2020 International Conference on Advanced Technologies for Communications (ATC)10.1109/ATC50776.2020.9255457(5-11)Online publication date: 8-Oct-2020
https://doi.org/10.1109/ATC50776.2020.9255457
Nikravesh AChen QHaseley SZhu XChallen GMao Z(2018)QoE Inference and Improvement Without End-Host Control2018 IEEE/ACM Symposium on Edge Computing (SEC)10.1109/SEC.2018.00011(43-57)Online publication date: Oct-2018
https://doi.org/10.1109/SEC.2018.00011
Rao ASteinert R(2018)Probabilistic multi-RAT performance abstractionsNOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium10.1109/NOMS.2018.8406277(1-6)Online publication date: Apr-2018
https://doi.org/10.1109/NOMS.2018.8406277
da Hora Dvan Doorselaer Kvan Oost KTeixeira R(2018)Predicting the effect of home Wi-Fi quality on QoEIEEE INFOCOM 2018 - IEEE Conference on Computer Communications10.1109/INFOCOM.2018.8486335(944-952)Online publication date: Apr-2018
https://doi.org/10.1109/INFOCOM.2018.8486335

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