Crowdsourcing Framework for QoE-Aware SD-WAN
Quality of experience (QoE) is an important measure of users’ satisfaction regarding their network-based services, and it is widely employed today to provide a real assessment of the service quality as perceived by the end users. QoE measures can be used to improve application performance, as well a...
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doaj-e99d5533573b4d4eb7d069dd1af65ad02021-08-26T13:46:29ZengMDPI AGFuture Internet1999-59032021-08-011320920910.3390/fi13080209Crowdsourcing Framework for QoE-Aware SD-WANIbtihal Ellawindy0Shahram Shah Heydari1Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, CanadaFaculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, CanadaQuality of experience (QoE) is an important measure of users’ satisfaction regarding their network-based services, and it is widely employed today to provide a real assessment of the service quality as perceived by the end users. QoE measures can be used to improve application performance, as well as to optimize network resources and reallocate them as needed when the service quality degrades. While quantitative QoE assessments based on network parameters may provide insights into users’ experience, subjective assessments through direct feedback from the users have also gathered interest recently due to their accuracy and interactive nature. In this paper, we propose a framework that can be used to collect real-time QoE feedback through crowdsourcing and forward it to network controllers to enhance streaming routes. We analyze how QoE can be affected by different network conditions, and how different streaming protocols compare against each other when the network parameters change dynamically. We also compare the real-time user feedback to predefined network changes to measure if participants will be able to identify all degradation events, as well as to examine which combination of degradation events are noticeable to the participants. Our aim is to demonstrate that real-time QoE feedback can enhance cloud-based services and can adjust service quality on the basis of real-time, active participants’ interactions.https://www.mdpi.com/1999-5903/13/8/209QoESDNQoScrowdsourcing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ibtihal Ellawindy Shahram Shah Heydari |
spellingShingle |
Ibtihal Ellawindy Shahram Shah Heydari Crowdsourcing Framework for QoE-Aware SD-WAN Future Internet QoE SDN QoS crowdsourcing |
author_facet |
Ibtihal Ellawindy Shahram Shah Heydari |
author_sort |
Ibtihal Ellawindy |
title |
Crowdsourcing Framework for QoE-Aware SD-WAN |
title_short |
Crowdsourcing Framework for QoE-Aware SD-WAN |
title_full |
Crowdsourcing Framework for QoE-Aware SD-WAN |
title_fullStr |
Crowdsourcing Framework for QoE-Aware SD-WAN |
title_full_unstemmed |
Crowdsourcing Framework for QoE-Aware SD-WAN |
title_sort |
crowdsourcing framework for qoe-aware sd-wan |
publisher |
MDPI AG |
series |
Future Internet |
issn |
1999-5903 |
publishDate |
2021-08-01 |
description |
Quality of experience (QoE) is an important measure of users’ satisfaction regarding their network-based services, and it is widely employed today to provide a real assessment of the service quality as perceived by the end users. QoE measures can be used to improve application performance, as well as to optimize network resources and reallocate them as needed when the service quality degrades. While quantitative QoE assessments based on network parameters may provide insights into users’ experience, subjective assessments through direct feedback from the users have also gathered interest recently due to their accuracy and interactive nature. In this paper, we propose a framework that can be used to collect real-time QoE feedback through crowdsourcing and forward it to network controllers to enhance streaming routes. We analyze how QoE can be affected by different network conditions, and how different streaming protocols compare against each other when the network parameters change dynamically. We also compare the real-time user feedback to predefined network changes to measure if participants will be able to identify all degradation events, as well as to examine which combination of degradation events are noticeable to the participants. Our aim is to demonstrate that real-time QoE feedback can enhance cloud-based services and can adjust service quality on the basis of real-time, active participants’ interactions. |
topic |
QoE SDN QoS crowdsourcing |
url |
https://www.mdpi.com/1999-5903/13/8/209 |
work_keys_str_mv |
AT ibtihalellawindy crowdsourcingframeworkforqoeawaresdwan AT shahramshahheydari crowdsourcingframeworkforqoeawaresdwan |
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1721193205770223616 |