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Hybrid Data Pricing for Network-AssistedUser-Provided Connectivity
Lin Gao, George Iosifidis, Jianwei Huang, Leandros Tassiulas
Network Communications and Economics Lab (NCEL)The Chinese University of Hong Kong (CUHK), Hong Kong
The Centre for Research and Technology Hellas (CERTH)University of Thessaly (UTH), Greece
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User-Provided Connectivity (UPC)
B: ClientInternet
C: Client
Data (A, B, C)
(Internet connection)Data (C)(WiFi)
Data (B)
(Bluetooth)
A: Host
I UPC: Mobile users (clients) connect to the internet through thehosting of other mobile users (hosts).
F Key challenges: Security and Incentive
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Network-Assisted UPC
B: ClientInternet
C: Client
Data (A, B, C)
(Internet connection)Data (C)(WiFi)
Data (B)
(Bluetooth)
A: Host
Mobile Virtual Network Operator (MVNO)
I Network-Assisted UPC: A network operator (e.g., an MVNO) isintroduced to address the security and incentive issues in UPC.
F Real Case: Karma (https://yourkarma.com)
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Focus of This Work
Incentive Issue
How to design a proper incentive mechanism for the MVNO to ensurethe UPC between hosts and clients?
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Outline
1 Background
2 System Model
3 Game Analysis
4 Conclusion
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User-Provided Connectivity (UPC)
UPC vs OPCI Operator-Provided Connectivity (OPC): Users connect to internet through devices
of network operators (e.g., base stations);
I User-Provided Connectivity (UPC): Users connect to internet through devices of
users (e.g., mobile phones);
(Host)
WiFi Link
Operator-Provided Connectivity
User-Provided Connectivity
Network Opeator
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User-Provided Connectivity (UPC)
BenefitsI Coverage ExtensionI Service ExtensionI Resource Saving
ChallengesI SecurityI Economics (Incentive)
The challenges motivate our study of Network-Assisted UPC.I An MVNO is introduced to address the security and incentive issues.
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Mobile Virtual Network Operator (MVNO)
MVNO vs MNOI Mobile Network Operator (MNO): Spectrum License, Network Infrastructure;
I Mobile Virtual Network Operator (MVNO): No Spectrum License, No Network
Infrastructure, but Leasing Spectrum and Infrastructure from MNOs;
Mobile Network Operator
Infrastructure(Base stations, backbone
network, core network, etc.)
Spectrum License
Mobile Virtual Network Operator
Infrastructure and Spectrum of MNO1
Infrastructure and Spectrum of MNO2
MNO1 MNO2
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Mobile Virtual Network Operator (MVNO)
BenefitsI Easy to deploy (fast, low cost, etc.)I Coverage aggregation (inter-national coverage)I Proximity to end-market (flexible pricing plans)
By Oct. 2012, there are 634 active MVNOs worldwide.I Virgin Mobile (launched in 1999), operating in 6 countriesI LycaMobile (launched in 2006), operating in 16 countriesI Karma (launched in 2012), operating in USAI ...
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The Karma Model
Our work is based on the Karma Model (https://yourkarma.com).I Provide 4G services (with usage-based pricing) to its customers, using
the cellular networks with which it has a relationship.I Unique feature: User-provided connectivity (UPC)
F Karma enables its customers to operate as WiFi hotspots (hosts) androute traffic for other users (clients).
Cellular Networks
4G Connection
Mobile Virtual Network Operator(MVNO)
Mobile Hotspot(Host)
WiFi
WiFi
Mobile User(Client)
Fig. Illustration of the Karma model
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The Karma Model
Cost of hostsI Data paymentI Quality of service degradationI Energy consumption
Problem: How to incentivize users to operate as hosts?
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The Karma Model
Karma’s approachI Connectivity sharing, not data sharing
F Hosts only pay the data they actually consume, and clients pay theirown data usage.
I Free data quotaF Hosts are rewarded certain free data when routing traffic for others.
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The Karma Model
Karma’s free data reward strategyI Every host gets 100MB of free data when he shares his connectivity
with every new mobile user at the first time.
DrawbacksI Easy to employ, but fail to provide consistent incentives!
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Our Purpose
We want to design a pricing and rewarding strategy that providesconsistent incentives to hosts.
Our approachI We generalize the Karma strategy in the following aspects:
F Flexible free data quota – not fixed, but proportional to the data heroutes for other users.
Key problem: How to design the best pricing and free data quotarewarding policy to maximize the MVNO’s revenue?
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Outline
1 Background
2 System Model
3 Game Analysis
4 Conclusion
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System Model
Players: MVNO, MNO, Host, ClientI MVNO leases network resource from MNOs;I MVNO serves its subscribers (Hosts and Clients) using leased resource;
F Hosts have 4G connections, and operate as hotspots;F Clients have no 4G concoctions, and connect to internet through a host;
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System Model
We focus on the interactions of MVNO, Host, and Client.I How much the MVNO pricing the hosts and clients?I How much the MVNO rewarding the hosts?I How much data the hosts forwarding for the clients?
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System Model
One MVNOI Pay MNOs a usage-based data wholesale price w ;I Charge subscribers (hosts and clients) a usage-based data price p.I Reward hosts a free data quota ratio θ.
Hosts: I , {1, ..., I}I Transmit their own traffic;I Operate as WiFi hotspots and route traffic for clients;
Clients: N , {N1, ...,NI}I Ni : Access to internet through a host i ;
Time-slotted operation: T = {1, ...,T}
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System Model
Parameters in one period (of T slots) for each host i :I Ri , {Ri1, ...,RiT}: the 4G capacity of host i ;I Di , {Di1, ...,DiT}: the total client demand to host i ;
F Shiftable vs Non-shiftable
I ξi , {ξi1, ..., ξiT}: the unit energy cost incurred by host i fortransceiving one byte of data via the WiFi connection;
I εi , {εi1, ..., εiT}: the unit energy cost incurred by host i fortransceiving one byte of data via the 4G connection;
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System Model
MVNO ModelingI Strategy: Decide price pi and free data quota ratio θi to every host iI Objective: Maximize the total revenue (payoff)
MVNO’s Payoff
V (p,θ; (xi , yi )i∈I) =I∑
i=1
T∑t=1
(pi · (xit − θi · yit)
+ pi · yit − w · (xit + yit))
F xit : the total data that host i consumes at slot t;F yit : the total data that host i routes for other users (Ni ) at slot t;
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System ModelHost Modeling
I StrategyF αi = {αit , ..., αiT}: the percentage of host i ’s 4G capacity (at every
slot) that will be scheduled for his own data;F βi = {βit , ..., βiT}: the percentage of host i ’s 4G capacity (at every
slot) that will be scheduled for serving other clients;F xit = αit · Rit , and yit = βit · Rit .
I Objective: Maximize the total payoff, includingF Utility from consuming dataF Payment to the MVNOF Energy consumption
Host i ’s Payoff
Ji (αi ,βi ; pi , θi ) = Ui (xi )−T∑t=1
pi · (xit − θi · yit)
−T∑t=1
εitxit −T∑t=1
(εit + ξit) · yit ,
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System Model
Host Service ModelingI Elastic service: concave utility function Ui (·)
F Achieve a higher utility when consuming more data;
Ui (xi ) = log(xi1 + ...+ xiT )
I Inelastic service: step utility function Ui (·)F Achieve a certain utility when consuming a minimum amount of data;
Ui (xi ) =
vi , if
∑t∈T
xit ≥ Bi ;
0, if∑t∈T
xit < Bi .
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System Model
Problem Formulation – Hybrid Pricing GameI Game leader: the MVNO
F Deciding price and free data quota reward to every host;
I Game follower: HostsF Deciding how much data they are going to consume for themselves,
and how much they are going to route for clients.
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Outline
1 Background
2 System Model
3 Game Analysis
4 Conclusion
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Step II – Host’s Decision
Host i ’s Problem
maxαi ,βi
Ji (αi ,βi ; pi , θi )
s.t., (a) αit + βit ≤ 1, ∀t ∈ T(b) βit · Rit ≤ Dit , ∀t ∈ T (Non-shiftable demand)
or
(b)T∑t=1
βit · Rit ≤ Di (Shiftable demand)
I αi = {αit , ..., αiT}: the percentage of host i ’s 4G capacity (at everyslot) that will be scheduled for his own data;
I βi = {βit , ..., βiT}: the percentage of host i ’s 4G capacity (at everyslot) that will be scheduled for serving other clients;
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Step II – Host’s Decision
0 0.2 0.4 0.6 0.8 10
1
2
3
4
5
Reimbursement Fraction - θi
Cap
acity
Sch
eduling
Xi: To Host (Total)
Yi : To Client
Xi + Yi: Total Consumption
Observation
Xi decreases with θi (Blue curve);
Yi increases with θi (Red curve);
Xi + Yi increases with θi (Green curve).
I Xi =∑T
t=1 α∗it · Rit : the total data consumed by host i ;
I Yi =∑T
t=1 β∗it · Rit : the total data consumed by clients in Ni ;
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Step II – Host’s Decision
0.5 1 1.5 20
1
2
3
4
5
Capacity
Sch
eduling
Pricing Strategy - pi
Xi: To Host (Total)
Yi : To Client
Xi + Yi: Total Consumption
Observation
Xi decreases with pi (Blue curve);
Yi increases with pi (Red curve);
Xi + Yi first increases and then decreases with pi (Green curve).
I Xi =∑T
t=1 α∗it · Rit : the total data consumed by host i ;
I Yi =∑T
t=1 β∗it · Rit : the total data consumed by clients in Ni ;
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Step I – MVNO’s Decision
MVNO’s Problem for Host i
maxpi ,θi
Vi (pi , θi ;α∗i ,β
∗i )
, pi · (Xi − θi · Yi ) + pi · Yi − w · (Xi + Yi )
I Xi =∑T
t=1 α∗it · Rit : the total data consumed by host i ;
I Yi =∑T
t=1 β∗it · Rit : the total data consumed by clients in Ni ;
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Step II – MVNO’s Decision
pr ice - p i
reim
bursement-θ i
0.6 0.8 1 1.2 1.4 1.6 1.8 20
0.1
0.2
0.3
0.4
0.5
Lower-bound θi
Optimal p i underevery θi: p∗
i (θi)
Up per-bound θ̄i
Contour of the MVNO’s payoff
Optimal Point
Op timal θi underevery pri ce p i: θ∗i (p i)
No Reimbursing
Solution
θ∗i (pi ): the optimal θ∗i under any price pi (Blue curve);
p∗i (θi ): the optimal p∗i under any free data quota θi (Red curve);
(p∗i , θ∗i ): the intersection (Upper Green point) of θ∗i (pi ) and p∗i (θi ).
I p∗i (0): the optimal price in a pure pricing system (Lower Green point);
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Outline
1 Background
2 System Model
3 Game Analysis
4 Conclusion
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Simulation
Optimal Hybrid Pricing StrategyI q: Clients’ service request probabilityI q : 0.1→ 1: The trajectory changing from point A to point B
1 1.2 1.4 1.6 1.8 20.08
0.1
0.12
0.14
0.16
0.18
0.2
Reimbursem
ent-θ i
Price - pi
(1.843,0.10647) : 15.5433
(2,0.1) : 21.718
B
A
(1.5543,0.12144) : 6.1058
(1.4063,0.13134) : 3.5781
(1.04, 0.17): 2.4895
(1.6532,0.11576) : 8.7045
Trajectory A → B (q : 0.1 → 1)
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Simulation
MVNO’s Optimal RevenueI Increase 20% to 135% under the elastic client demand (GREEN bar)
when q increases from 0.1 to 0.9;I Increase 50% to 550% under the inelastic client demand (RED bar)
when q increases from 0.1 to 0.9;
0.01 0.1 0.2 0.4 0.6 0.90
2
4
6
8
10
12
14
16
18
Maxim
um
MVNO
Pay
off
Client Request Probability - q
Pricing-only
Hybrid Pricing (Elastic Client Demand)
Hybrid Pricing (Inelastic Client Demand)
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Conclusion
We propose a hybrid pricing scheme for the network-assisted UPCsystem;
We derive the optimal hybrid pricing policy that maximizes theMVNO’s revenue.
Future Extension — Incomplete InformationI How to derive the optimal hybrid pricing policy when only the
stochastic information is available?I How to deal with the problem even when the stochastic information is
not available?
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Thank You
[email protected] Communications and Economics Lab (NCEL)
The Chinese University of Hong Kong (CUHK)
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Smart Data Pricing (2nd May, 2014)
Multi-disciplinary program
Academic keynote: Alok Gupta (UMN)
Industry keynote: Keith Cambron (Former President AT&T Lab)
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