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WINLAB
Joint Pricing and Caching Strategies Optimization in Information Centric
Networks (ICN)
Mohammad Hajimirsadeghi, Narayan B. Mandayam, Alex Reznik (InterDigital Inc)
WINLAB, Rutgers University
WINLAB
Agenda
Introduction
System model
Static game
Dynamic game
Numerical results
Future directions
2
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Access Network
Transit Network
vast majority of internet interaction relate to content access, e.g YouTube, BitTorrent,Netflix, Hulu
A3A1 A2
C1 C2
Content distributer
(content provider)
WINLAB4
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
The users are just interested in the information, not where it’s located.
WINLAB5
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
The users are just interested in the information, not where it’s located.
I want to watch a movie but I don’t care where it is
located
WINLAB6
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
I want to watch a movie but I don’t care where it is
located
We need a new approach to support this paradigm.Here ICN comes to mind
WINLAB7
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
I want to watch a movie but I don’t care where it is
located
ICN: new communication
paradigm to increase the
efficiency of content
delivery
WINLAB8
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
I want to watch a movie but I don’t care where it is
located
The network
infrastructure actively
contributes to content
caching and distribution
WINLAB9
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
The network
infrastructure actively
contributes to content
caching and distribution
How to get content?
WINLAB10
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
The network
infrastructure actively
contributes to content
caching and distribution
How to get content?
WINLAB11
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
The network
infrastructure actively
contributes to content
caching and distribution
How to get content?
Exist?
WINLAB12
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
The network
infrastructure actively
contributes to content
caching and distribution
How to get content?
No! then fetch it from somewhere
WINLAB13
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
The network
infrastructure actively
contributes to content
caching and distribution
How to get content?
No! then fetch it from somewhere
WINLAB14
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
The network
infrastructure actively
contributes to content
caching and distribution
How to get content?
No! then fetch it from somewhere
WINLAB15
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
The network
infrastructure actively
contributes to content
caching and distribution
How to get content?
No! then fetch it from somewhere
WINLAB16
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
The network
infrastructure actively
contributes to content
caching and distribution
How to get content?
No! then fetch it from somewhere
WINLAB17
Access Network
Transit Network
A3A1 A2
C1 C2
Content distributer
(content provider)
The network
infrastructure actively
contributes to content
caching and distribution
How to get content?
Should I cache this content?
WINLAB
Introduction
Access ICN Transit ICN content provider
Price of service Cost of caching
Competitive and self interest driven
Non-cooperative game model for pricing and caching
Static case, when caching cost is fixed
Dynamic case, when caching cost is varying according to state
of the cache
18
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System Model
user’s demand:
19
( )
0
( )
0
c
A A A B B o
c
B B B A A o
a p p p
a p p p
2 Access ICNs(ISPs), one
transit ICN C and a content
provider O.
bunch of users who can switch
from one access ICN to
another.
A set of caching and pricing
strategies
try to maximize its own payoff
selfishly in a non cooperative
game.
WINLAB
System Model
user’s demand:
20
( )
0
( )
0
c
A A A B B o
c
B B B A A o
a p p p
a p p p
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Utility functions)c()p()c(U AABBCAAAA (s)
A,AOUTA,AAA, ppαpα
},{ },{ },,,{
)(
C),(CC)(K, )(pα)(pαBAK BAK KMOBAM
s
MMKKCKC pcU
(c)
o
},{
(s)
o),( p)()(p BA
BAK
oOKKO cU
(A,B) (A, ) (A, ) (A,A)
( , ) ( , ) ( , ) ( , )
α α α 1 α
α α α 1 α
1
1
C O
B A B C B O B B
s
k K
k
P P
Payoff from User’s A requests that A caches
Payoff from User’s A requests that A forwards to C
Payoff from User’s B requests that A serves
Payoff from Users’ A & B requests that C caches
Payoff from Users’ A & B requests that C forwards to other ICNs
Payoff from Users’ A & B requests that O caches
Payoff from charging Users’ A & B for the content
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Joint Pricing and Caching
Static game
Non-cooperative game
The content are different(unique) in each request.
The caching cost of the players are fixed
The game is just played once (one step game)
Optimization problem
22
,max , ,
j j j
j j jS p
U S S for all j
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Summary of our results for static game
In a network consists of the set of players as follows: access ICN A, Access ICN B, transit ICN C and content provider O
At Nash Equilibrium Caching variables take on values of 0 or 1.
If A and B are not symmetric, then 9 different case of NE could be possible, depending on choice of caching parameters.
If A and B are symmetric, then, there are only 3(based on caching parameters) NE possible.
23
,K M
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Summary of our results(symmetric case)
Degenerate case:(no need for transit network) when transit ICN
caching cost is greater than access ICNs caching cost
C-dominant case:(transit ICN cache all the demands) when
transit ICN caching cost is less than both access ICNs caching
cost and content provider caching cost
O-dominant case:(content provider cache all the demands) when
transit ICN caching cost is less than access ICNs caching cost
but greater than content provider caching cost
24
A Cc c
min ,C A Oc c c
,C A C Oc c c c
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Summary of our results(symmetric case)
Utility functions for the possible cases at the Nash points
25
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Dynamic(Repeated) Game
26
We consider a game that will be repeated over time
Cost of caching is varying according to the state of the cache with two different trends
Linear cost
Exponential cost
In each round of the game the caching cost will be updated based on the state of the cache.
The payoff of each player is the average over the all rounds.
Static game solution is applicable in repeated game
1
1 i
K K
i
U i U ii
0
0
K
K K K
cc i Z i c
0exp
K
K K
Z ic i c
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Access ICN in linear case
27
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Access ICN in exponential case
28
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Transit ICN
29
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Conclusion and Future work Conclusion
Modeling the interaction among Access ICN, transit ICN and content provider
Finding Nash Equilibrium as the game solution for static and dynamic game
The results generalize to K access ICN
Future directions Try to map the prices to dollars per megabytes
updating and improving the demand models
Add Users utility functions to the game
Content popularity
Online distributed algorithm 30
WINLAB
Thanks for your attention
Questions?
31