1. End user-perceived quality estimation in accordance with the correlation between QoS/QoE on Internet of Services dr. Rasa Bruzgiene, dr. Lina Narbutaite, dr. Tomas Adomkus Department of Telecommunications, Kaunas University of Technology, Lithuania rasa.bruzgiene@ktu.lt ICT COST Action IC1304 Autonomous Control for a Reliable Internet of Services (ACROSS)

2. Introduction Relationship between QoS/SLA and QoE The conceptual model QoS/QoE correlation on IoS The composition of QoE estimation in accordance of QoS/QoE correlation Example of use case: Experimental investigations of IPTV QoE estimation 2 Outline: End user-perceived quality estimation in accordance with the correlation between QoS/QoE on Internet of Services

3. 3 Definition: Quality of Experience (QoE) is the term which is used to describe how it is satisfied by users to the provided service quality. Problem: The poor QoE will cause dissatisfied users and fall behind in contestants due to the ultimately bad market competitive power to contestants. Dependence: QoE is composed of not only the network performance parameter but also the service quality (QoS) parameter such as cost, reliability, availability, usability, and fidelity. Importance: Although QoE is very subjective in nature, it is very important to measure it as realistically as possible. The ability to evaluate QoE will give the provider some sense of the contribution of the network's performance to the overall level of users‘ satisfaction. End user-perceived quality estimation in accordance with the correlation between QoS/QoE on Internet of Services

4. 4 Relationship between QoS/SLA and QoE Quality of Learning (QoL) represents the aspects of the user and domain model. Quality of Flow (QoF) is based on feedback, interaction, emotions.

5. 1 The conceptual model QoS/QoE correlation on Internet of Services

6. 6 The composition of QoE estimation in accordance of QoS/QoE correlation The composition of QoE estimation is based on three subjects: 1.Input data from network part 2.Input data from user perspective 3.Analytical models for QoE adaptation and optimization

7. 6 The composition of QoE estimation in accordance of QoS/QoE correlation The composition of QoE estimation is based on three subjects: 1.Input data from network part 2.Input data from user perspective 3.Analytical models for QoE adaptation and optimization

8. 1 Example of use case: Experimental investigations of IPTV QoE estimation 8/28 MOS= -0,032x 3 + +0,627x 2 - 4,020x + +9,372 MOS= -0,072x 5 + +0,273x 4 +2,014x 3 - -12,752x 2 + 21,276x – - 6,756 MOS=1

9. 1 Example of use case: Experimental investigations of IPTV QoE estimation 9/28 MOS= -0,032x 3 + +0,627x 2 - 4,020x + +9,372 MOS= -0,072x 5 + +0,273x 4 +2,014x 3 - -12,752x 2 + 21,276x – - 6,756 MOS=1

10. 9 Example of use case: Adaptation/Optimization/Feedback MOS= -0,032x 3 + +0,627x 2 - 4,020x + +9,372 MOS= -0,072x 5 + +0,273x 4 +2,014x 3 - -12,752x 2 + 21,276x – - 6,756 MOS=1

11. 1010 Example of use case: Experimental investigations of IPTV QoE estimation The average difference between the calculated objective and the subjective assessments of IPTV Quality of Experience is 0.3.

12. 1,7 4,8 3,42 MOS Tzap User's reaction Random change of IPTV channel 3,26 3,7 2,4 MOS Tzap User's reaction Sequentially change of IPTV channel 4,7 1,8 1,56 MOS Tzap User's reaction PiP EPG change of IPTV channel 1 Example of use case: Influence to Quality of Experience

13. 1212 End user-perceived quality estimation in accordance with the correlation between QoS/QoE on Internet of Services Two rivers meet in Kaunas, they join and never split up! Night view of magic Kaunas... Thank you for your attention.