# Crowdsourcing Framework for QoE-Aware SD-WAN +
## 7 Conclusions and Future Works  
In this paper, a real-time QoE crowdsourcing framework was proposed for SD-WAN. The proposed framework is based on a combination of QoE measurement application and QoS quality parameters that accommodate a variety of different streaming protocols. It emphasizes the dependability between QoE and QoS and how the overall user QoE perspective can be affected. We analyzed how dynamic changes of events could affect the performance of different streaming protocols and, accordingly, the perceived quality. We also compared objective and subjective results and found that both results were mostly consistent. It was demonstrated that an SD-WAN controller can receive feedback, detect problems in the service path, and take corrective action by making changes to the routing paths of the content delivery network in a timely manner. Our goal was to prove that real-time QoE feedback could enhance cloud-based services and could adjust service quality on the basis of real-time active participant interaction.  
There are several paths for future work in this area. Firstly, the scalability of the framework can be further confirmed by setting up environments with a larger number of participants in the real world. Furthermore, the use of artificial intelligence (AI) can be considered in this operation for both data summarization and network-based rerouting decisions. With AI, it is possible to learn feedback patterns received by external user participation, and an AI algorithm can decide if rerouting is required or not, where there could be a threshold for the number of clicks. This AI algorithm can determine this threshold dynamically and adjust it by learning from user feedback. AI can also be used to determine the suitable level of data abstraction/summarization for the tradeoff between prompt QoE feedback reaction and the volume of feedback traffic. Another path can focus on algorithms for optimizing SDN environments, cloud resources, and paths, taking into consideration the QoS SLA and minimum QoE requirements. Intent-based programming is currently evolving, and new research could leverage the Intent Framework of the SDN controller to optimize rerouting on the basis of QoE.  
**Author Contributions:** This research work was completed by [PERSON] as part of her thesis (supervised by [PERSON]) for the degree of Master of Computer Science at University of Ontario institute of Technology. [PERSON] and [PERSON] both contributed to the ideas and methodologies behind the work. Implementation of the test bed and data collection was performed by [PERSON] The manuscript was written by [PERSON] and [PERSON]. Revisions were implemented by [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported through funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) and Ericsson Canada.  
**Institutional Review Board Statement:** Human-based experiments in this work were approved by the Research Ethics Board of the University of Ontario Institute of Technology under application file number 14780.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: The datasets generated and/or analyzed during the current study are not publicly available due to the requirements of the project funding sources, but may be available from the corresponding author on reasonable request and subject to the approval of the funding sources.  
We thank Subjectify.us and the MSU Graphics and Media Lab for helping us to conduct subjective and objective measurements using the MSU Video Quality Measurement Tool.  
The authors declare no conflict of interest.