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Crowdsourcing Framework for QoE-Aware SD-WAN

Author

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  • Ibtihal Ellawindy

    (Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada)

  • Shahram Shah Heydari

    (Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada)

Abstract

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.

Suggested Citation

  • Ibtihal Ellawindy & Shahram Shah Heydari, 2021. "Crowdsourcing Framework for QoE-Aware SD-WAN," Future Internet, MDPI, vol. 13(8), pages 1-20, August.
  • Handle: RePEc:gam:jftint:v:13:y:2021:i:8:p:209-:d:614847
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    Cited by:

    1. Štefica Mrvelj & Marko Matulin, 2023. "FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment," Future Internet, MDPI, vol. 15(9), pages 1-22, September.

    More about this item

    Keywords

    QoE; SDN; QoS; crowdsourcing;
    All these keywords.

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