Abstract

Internet traffic volume is increasing, and this causes scalability issues in content delivery. Information-centric network has been introduced to support this increase in Internet traffic through caching. While collaborative caching in information-centric network is a crucial feature to improve network performance and reduce delivery costs in content distribution, the current pricing strategies on the Internet are not incentive compatible with information-centric network interconnection. In this paper, we focus on the economic incentive interactions in caching deployment between several types of information-centric network providers (content provider and Internet service provider). In particular, we develop game-theoretic models to study the interaction between providers in an information-centric network model where the providers are motivated to cache and share content. We use a generalized Zipf distribution to model content popularity. We formulate the interactions between the Internet service providers and between the content providers as a noncooperative game. We use a Stackelberg game model to capture the interactions between the content provider and Internet service providers. Through mathematical analysis, we prove the existence and uniqueness of the Nash equilibrium under some conditions. An iterative and distributed algorithm based on best response dynamics is proposed to achieve the equilibrium point. The numerical simulations illustrate that our proposed game models result in a win-win solution.

Highlights

  • Introduction e growth ofInternet traffic data is rapidly increasing due to the explosion of sites on the Internet such as YouTube, Dailymotion, and Netflix

  • Our work belongs to the second research line but differs from these existing works in the following five aspects: (1) We developed an analytical framework for the distribution of popular content in information-centric network (ICN), which comprises multiple content providers (CPs), multiple Internet service provider (ISP), and a set of endusers

  • We developed an analytical framework for the distribution of accessible content in an ICN that comprises multiple ISPs and multiple CPs. e interaction among ISPs and among CPs is investigated by using the noncooperative game

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Summary

Game Analysis

Let G1 [F, 􏼚Psj, Pccj, Qsj􏼛, 􏼚UISPj(.)􏼛] denote the noncooperative price QoS price game (NPQPG), where. E game G1(Pcc, qs) admits at least one equilibrium, if and only if its second derivative of the utility UISPj function with respect to price Psi is nonpositive. Algorithm 1 summarizes the best response learning steps that each provider has to perform to find the Nash equilibrium Such as (i) E denotes CP or ISP (ii) O refers to N or K (iii) x refers to the vector price pc, vector price ps, vector price pcc, vector qs, vector qc or vector qss (iv) Xo refers to the policy profile price, QoS or QoC e proof of the above theorem can be found in Appendix E. E optimal QoS and QoC of the ISPs is given as follows: Stackelberg game. We solve the first stage of the Stackelberg game, where the CPs set QoS and QoC. We formulate the game between CPs as a noncooperative game. e noncooperative game is described in the above Sections 4.5 and 4.6. □

Numerical Investigation
Conclusion
Proof of Theorem 6
Full Text
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