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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

Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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Page 1: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

Joint Pricing and Caching Strategies Optimization in Information Centric

Networks (ICN)

Mohammad Hajimirsadeghi, Narayan B. Mandayam, Alex Reznik (InterDigital Inc)

WINLAB, Rutgers University

Page 2: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

Agenda

Introduction

System model

Static game

Dynamic game

Numerical results

Future directions

2

Page 3: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB3

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)

Page 4: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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.

Page 5: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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

Page 6: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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

Page 7: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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

Page 8: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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

Page 9: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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?

Page 10: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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?

Page 11: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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?

Page 12: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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

Page 13: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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

Page 14: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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

Page 15: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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

Page 16: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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

Page 17: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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?

Page 18: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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

Page 19: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

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.

Page 20: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

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

Page 21: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

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

Page 22: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

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

Page 23: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

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

Page 24: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

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

Page 25: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

Summary of our results(symmetric case)

Utility functions for the possible cases at the Nash points

25

Page 26: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

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

Page 27: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

Access ICN in linear case

27

Page 28: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

Access ICN in exponential case

28

Page 29: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

Transit ICN

29

Page 30: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

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

Page 31: Joint Pricing and Caching Strategies Optimization in ...Non-cooperative game model for pricing and caching Static case, when caching cost is fixed Dynamic case, when caching cost is

WINLAB

Thanks for your attention

Questions?

31