end user-perceived quality estimation in accordance with the correlation between qos/qoe on internet...
TRANSCRIPT
End user-perceived quality estimation in accordance with the correlation between QoS/QoE on Internet of
Servicesdr. Rasa Bruzgiene, dr. Lina Narbutaite, dr. Tomas Adomkus
Department of Telecommunications, Kaunas University of Technology, Lithuania
ICT COST Action IC1304
Autonomous Control for a Reliable Internet of Services (ACROSS)
• 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
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
Relationship between QoS/SLA and QoE
Application/Service provider
Application Content Network
Content
Business model
Delivery
Usage
Interaction
QoE
SLA
QoS/SLA QoS/SLA
QoS/SLAQoS/SLA
QoS/SLA
User Qos/SLAQuality of experience QoE
QoSQoFQoSQoLfQoE ,
Quality of Learning (QoL) represents the aspects of the user and domain model.
Quality of Flow (QoF) is based on feedback, interaction, emotions.
QoEQoSfSLA ,
NCA QoSQoSQoSfQoS ,,
1
The conceptual model QoS/QoE correlation on Internet of Services
6
The composition of QoE estimation in accordance of QoS/QoE correlation
User cognitive model
User experence model
User interface and interaction
IaaS parameters (VM, RRT, L., etc)
PaaS+SaaS received QoS
Adaptive and estimation
model
Optimization knowledge
model
QoF
QoP
QoE
QoU
QoS
QoI
QoL
User related data
Application/cloud related data Feedback/optimization
Fro
m Q
oS
to
Qo
E
The composition of QoE estimation is based on three subjects:1.Input data from network part2.Input data from user perspective3.Analytical models for QoE adaptation and optimization
6
The composition of QoE estimation in accordance of QoS/QoE correlation
User cognitive model
User experence model
User interface and interaction
IaaS parameters (VM, RRT, L., etc)
PaaS+SaaS received QoS
Adaptive and estimation
model
Optimization knowledge
model
QoF
QoP
QoE
QoU
QoS
QoI
QoL
User related data
Application/cloud related data Feedback/optimization
Fro
m Q
oS
to
Qo
E
The composition of QoE estimation is based on three subjects:1.Input data from network part2.Input data from user perspective3.Analytical models for QoE adaptation and optimization
1
Example of use case:Experimental investigations of IPTV QoE estimation
8/28
0 1 2 3 4 5 6 7 8 90
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
T,s
MO
S
atsitiktinisaproks. "atsitiktiniam"nuoseklusaproks. "nuosekliam"
2.71.96
MOS= -0,032x3 + +0,627x2 - 4,020x + +9,372
MOS= -0,072x5 + +0,273x4 +2,014x3 - -12,752x2 + 21,276x – - 6,756
MOS=1
2
21
11
1
7,2,1
7,296,1,2
96,196,1,1
96,1,5
kaitos
kaitoskaitos
kaitoskaitos
kaitos
Tkai
TkaiTMOS
TkaiTMOS
Tkai
MOSoUser cognitive
modelUser experence
modelUser interface and interaction
IaaS parameters (VM, RRT, L., etc)
PaaS+SaaS received QoS
Adaptive and estimation
model
Optimization knowledge
model
QoF
QoP
QoE
QoU
QoS
QoI
QoL
User related data
Application/cloud related data Feedback/optimization
Fro
m Q
oS
to
Qo
E
1
Example of use case:Experimental investigations of IPTV QoE estimation
9/28
0 1 2 3 4 5 6 7 8 90
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
T,s
MO
S
atsitiktinisaproks. "atsitiktiniam"nuoseklusaproks. "nuosekliam"
2.71.96
MOS= -0,032x3 + +0,627x2 - 4,020x + +9,372
MOS= -0,072x5 + +0,273x4 +2,014x3 - -12,752x2 + 21,276x – - 6,756
MOS=1
2
21
11
1
7,2,1
7,296,1,2
96,196,1,1
96,1,5
kaitos
kaitoskaitos
kaitoskaitos
kaitos
Tkai
TkaiTMOS
TkaiTMOS
Tkai
MOSoUser cognitive
modelUser experence
modelUser interface and interaction
IaaS parameters (VM, RRT, L., etc)
PaaS+SaaS received QoS
Adaptive and estimation
model
Optimization knowledge
model
QoF
QoP
QoE
QoU
QoS
QoI
QoL
User related data
Application/cloud related data Feedback/optimization
Fro
m Q
oS
to
Qo
E
9
Example of use case:Adaptation/Optimization/Feedback
0 1 2 3 4 5 6 7 8 90
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
T,s
MO
S
atsitiktinisaproks. "atsitiktiniam"nuoseklusaproks. "nuosekliam"
2.71.96
MOS= -0,032x3 + +0,627x2 - 4,020x + +9,372
MOS= -0,072x5 + +0,273x4 +2,014x3 - -12,752x2 + 21,276x – - 6,756
MOS=1
2
21
11
1
7,2,1
7,296,1,2
96,196,1,1
96,1,5
kaitos
kaitoskaitos
kaitoskaitos
kaitos
Tkai
TkaiTMOS
TkaiTMOS
Tkai
MOSo
10
Example of use case:Experimental investigations of IPTV QoE estimation
-0,60
-0,40
-0,20
0,00
0,20
0,40
0,60
0,80
1,00
1,20MOSo - MOSs
Tzapping, s
1,3 s
2,5 s
4,0 s
The average difference between the calculated objective and the subjective assessments of IPTV Quality of Experience is 0.3.
1,7
4,83,42
MOS
TzapUser's
reaction
Random change of IPTV channel
3,26
3,72,4
MOS
TzapUser's
reaction
Sequentially change of IPTV channel
4,7
1,81,56
MOS
TzapUser's
reaction
PiP EPG change of IPTV channel
11
Example of use case:Influence to Quality of Experience
12
End user-perceived quality estimation in accordance with the correlation between QoS/QoE on Internet of Services
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