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© 2016 UZH, CSG@IFI
QoE Awareness for the Mobile Termination Rates Monopoly Liberation
Christos Tsiaras
Department of Informatics IFI, Communication Systems Group CSG, University of Zürich UZH
tsiaras@ifi.uzh.ch
PhD Thesis Defense, Zurich, May 30, 2016
Introduction AQX Decompiling QoE AbaCUS MNO Selection Mechanism Summary
© 2016 UZH, CSG@IFI
Mobile Call Services Ecosystem
MvNO
Alice Bob
I want to call
MTR Service rate
Total cost of the call = Service rate + MTR CPP principle → Total cost
Caller’s network
-1-
© 2016 UZH, CSG@IFI
Problem
MTR since the early days of mobile communication, are considered to be a “de-facto” monopoly
Could any of you connect me with Alice?
MvNO X Alice’s MvNO
I am the only network that can connect anyone with Alice!
! Only the MNO that Alice is connected with can reach her
! What about multiple SIM cards? – Not practical – Prior agreement is needed – Takes time – Is Apple SIM maybe a solution?
AT&T “bans” Apple SIM!
-2-
© 2016 UZH, CSG@IFI
Research Questions Answered
! How can QoE be estimated for multiple and diverse variables?
! How to overcome MTR monopoly? – Charging for QoE – Switch between MvNOs
! Is an automatic and on-demand MvNO selection possible? – MvNO switching time and energy cost
-3-
© 2016 UZH, CSG@IFI
AQX
! Formalizing QoE in axioms 1. Variables that affect QoE can be identified 2. Those variables can be characterized
• IVs - The more you have the better it is – E.g., quality
• AVs - The more you have the worst it is – E.g., price
3. Considering the service specifications it is possible to select the best and the worst values of each variable
4. There is an ideal/desired/expected/agreed value (x0) for each variable 5. The effect of each variable’s variation is identifiable
• Influence factors (m)
6. The importance (w) of each variable is identifiable
-4-
© 2016 UZH, CSG@IFI
IV
Value [x ]
MOS[e
i(x
)]
0 x0 1
µ
e0
M
ei
x 0 − δ ′ x 0 + δ
e0 − ϵ ′
e0 + ϵ
Value [x ]
MOS[e
i(x
)]
0 x0
1µ
e0
M
m = 1m = 10−3
m = 103
QoE
IV variable
Influence factor (m)
Expected value (x0)
-5-
© 2016 UZH, CSG@IFI
Influence Factors Calculation AV
Value [x]
MOS[e
a(x)]
0 x0
1µ
e0
M
x0 − δ′ x0 + δ
e0 − ϵ′
e0 + ϵ
ea
m- Area
m+ Area
m+ =
lnln 3− "ε
4ln 34
#
$
%%%
&
'
(((
ln x0 +δx0
m− =
lnln 3+ε
4ln 34
"
#
$$$
%
&
'''
ln x0 − (δx 0
QoE
IV variable
Influence factor (m)
Expected value (x0)
-6-
© 2016 UZH, CSG@IFI
Generic QoE Illustration of an IV & AV
Value [x]
MOS[e
i∨a(x)]
h=
M−µ
xmin
x0
xmax
µ
e0
M
ei
ea
QoE
IV variable
Importance factor (wk)
-7-
Poor quality for free
Excellent quality at a high price
This is where
your want your
customers to be
© 2016 UZH, CSG@IFI
The Design to Overcome the MTR Monopoly
! AbaCUS – Points to the MvNO to terminate a call – Triggers the MvNO switching
! Decision made by the Au2
– The auction provide incentives to MvNOs ! Auction parameters
– Expected QoE: MOS via AQX • QoS-C
– E.g., Sound quality
• TeR-C – E.g., MTR
! Automatic and on-demand MNO selection mechanism – Switches to the winning MvNO
-8-
© 2016 UZH, CSG@IFI
QoS-C & TeR-C
Variable Type Classification Unit
QoS-C Sound Quality Technical IV Standard Definition (SD)
High Definition (HD)
Guaranteed Network Access Technical IV Yes No
TeR-C
MTR Economic AV Any Currency
Call Set-up Cost Economic AV -//-
Charging rate [time] Economic AV sec
Minimum Call Duration [time] Economic AV -//-
-9-
© 2016 UZH, CSG@IFI
AbaCUS Algorithm
Caller MvNOs Preferences Announce TeR-Cs
No
Yes
Better option MvNO, QoS-C
Is there any better option?
Switch MvNO
Proceed without
any change
Start End
Input Condition
Output
Legend
Au2
-10-
No better option exists
© 2016 UZH, CSG@IFI
MNO Selection Mechanism Implementation Steps
! The internal Android API provides a method to select the MNO
! Access the internal API – Custom Android framework – Modify the IDE access rule
! Sign the implemented mechanism with the system signature key
! Run the application – System user ID – Phone process
API
-11-
© 2016 UZH, CSG@IFI
MNO Switching Mechanism Evaluation Set-up
! Count the MNO switching time and energy consumption – Between all possible MNO combinations – At different times across a day – In different locations – In different scenarios
• Stable in urban areas • Moving in a train (high handover demand)
=
-12-
© 2016 UZH, CSG@IFI
MNO Switching Time / Pair of MNOs
Sw −> Su Sw −> Or Su −> Sw Su −> Or Or −> Sw Or −> Su Mean
0.5
1.5
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
10.5
11.5
12.5
MNO switching scenarios
MN
O s
witc
hing
tim
e [s
]
MNO selection time (stable)
MNO selection time (moving)
-13-
= Or: Su: Sw:
4.3
s m
inim
um ti
me
need
ed
© 2016 UZH, CSG@IFI
MNO Switching Mechanism Power Consumption Evaluation
Process Power [W]
Talk 3G 0.7050
MNO Selection moving 0.6536
MNO Selection Stable 0.5406
Talk 2G 0.3333
Consumption 2G max talk time [h] 2G power [W] 3G max talk time [h] 3G power [W]
Voice service 18.33 0.3333 8.67 0.705
Stand-by 710.00 0.0086 610.00 0.010
Ptot =%Batteryused ⋅E(J )
texperiment (s)E = 6.11Wh ⋅3600s = 21996J
PMNO(stable) =0.14 ⋅21996J
5696s= 0.5406W
-14-
© 2016 UZH, CSG@IFI
Summary
! QoE estimation while considering technical and economic variables simultaneously is possible – With the AQX model
! MTR monopoly obstacle can be overcome – By charging for QoE
! It is technically feasible to select on-demand the MvNO to terminate a call – Because it is possible in a “reasonable” MvNO switching time
and energy cost
-15-
© 2016 UZH, CSG@IFI
Conclusions
! AQX is a generic QoE estimation model – Multidimensional – Non-service dependent – Supports diverse variables simultaneously
! Technology allows competition in the MTR market – MTR is not a “de-facto” monopoly anymore
! To enable competition in the MTR market an automatic and on-demand native implementation is needed – A prototype has been implemented and evaluated for the
Android platform
-16-
© 2016 UZH, CSG@IFI
Future Work
! Incentive market analysis to reveal QoE-related variables
! Mechanism to protect dissatisfied end-users in case of QoE is not the expected
! Explore further auction types to increase infrastructure utilization and/or social wealth-fare
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© 2016 UZH, CSG@IFI
Thank you!
Q&A
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