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...

Full description

Bibliographic Details
Main Authors: Ibtihal Ellawindy, Shahram Shah Heydari
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Future Internet
Subjects:
QoE
SDN
QoS
Online Access:https://www.mdpi.com/1999-5903/13/8/209
id doaj-e99d5533573b4d4eb7d069dd1af65ad0
record_format Article
spelling 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
_version_ 1721193205770223616