This Article 
 Bibliographic References 
 Add to: 
Market-Based Resource Allocation for Content Delivery in the Internet
December 2003 (vol. 52 no. 12)
pp. 1573-1585

Abstract—Caches have been used extensively to store the most popular/recent requested data to improve the user latency and reduce the network load. Recently, a more systematic approach to caching has been developed within the framework of Content Delivery Networks (CDN). A CDN is the network of caches, where the caches are geographically distributed and serve user requests on behalf of the subscriber Web sites. Users receive the requested information from the caching servers, which are closer to the users and usually much less loaded than the origin server. The objective is to minimize the user latency by intelligently distributing the content and serving the user requests from the most efficient sites. We realistically model the agents in a CDN with selfish self-maximizing behaviors and define the problem as a noncooperative game. We separate the distribution and routing subproblems and use games to solve each. We show that the subproblems have equilibrium solutions and, if the equilibrium of a subproblem is unique, we achieve the global optimum for that subproblem. We also determine that a unique equilibrium is reached if the content providers are not willing to pay high amounts and the cache sizes are sufficiently small. We noticed that the global system optimum requires the content providers to pay very high amounts, which in practice may prohibit the applicability of the distributed method. Thus, we consider an investment strategy for the content providers, which maximizes the publishers' net benefits and leads to a near-optimum system solution. We also show that the joint distribution and routing game has an equilibrium and demonstrate its performance by numerical examples.

[1] Ö Erçetin, Optimal Non-Cooperative Resource Allocation for Content Distribution in the Internet PhD dissertation, Univ. of Maryland, College Park, May 2002.
[2] L. Fan et al., "Summary Cache: A Scalable Wide-Area Web Cache Sharing Protocol," Computer Communication Review, Vol. 28, No. 4, Oct. 1998, pp. 254-265.
[3] I. Cidon, S. Kutten, and R. Soffer, Optimal Allocation of Electronic Content Proc. INFOCOM, 2001.
[4] M. Karlsson and M. Mahalingam, Do We Need Replica Placement Algorithms in Content Delivery Networks Proc. Workshop Web Content Caching (WCW), 2002.
[5] L. Qiu, V. Padmanabham, and G. Voelker, "On the Placement of Web Server Replicas," Proc. 20th Joint Conf. IEEE Computer and Comm. Soc. (IEEE INFOCOM), IEEE CS Press, 2001, pp. 1587-1596.
[6] M. Rabinovich and A. Aggarwal, RaDaR: A Scalable Architecture for a Global Web Hosting Service Proc. Int'l Conf. Distributed Computing Systems (ICDCS), 1999.
[7] T.P. Kelly, S. Jarmin, and J.K. MacKie-Mason, Variable QoS from Shared Web Caches: User-Centered Design and Value Sensitive Replacement Proc. MIT Workshop Internet Service Quality Economics, Dec. 1999.
[8] F. Kelly, Charging and Rate Control for Elastic Traffic European Trans. Telecomm., vol. 8, pp 33-37, 1997.
[9] R.J. Gibbens and F.P. Kelly, Resource Pricing and the Evolution of Congestion Control Automatica, vol. 35, pp. 1969-1985, 1999.
[10] F. Kelly, A. Maulloo, and D. Tan, Rate Control in Communications Networks: Shadow Prices, Proportional Fairness and Stability J. Operational Research Soc., vol. 49, pp. 237-252, 1998.
[11] C. Courcoubetis, G.D. Stamoulis, C. Manolakis, and F.P. Kelly, An Intelligent Agent for Optimizing QoS-for-Money in Priced ABR Connections Telecomm. Systems, special issue on network economics, to appear.
[12] H. Yaiche, R. Mazumdar, and C. Rosenberg, A Game Theoretic Framework for Bandwidth Allocation and Pricing in Broadband Networks IEEE/ACM Trans. Networking, vol. 8, no. 5, pp. 667-678, 2000.
[13] L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker, Web Caching and Zipf-Like Distributions: Evidence and Implications Proc. Infocom '99, Mar. 1999.
[14] M. Day, B. Cain, G. Tomlinson, and P. Rzewski, A Model for Content Internetworking IETF Draft, Mar. 2001.
[15] M. Green, B. Cain, G. Tomlinson, S. Thomas, and P. Rzewski, Content Internetworking Architectural Overview IETF Draft, Mar. 2001.
[16] M. Nottingham, Requirements for Demand Driven Surrogate Origin Servers IETF Draft, Jan. 2001.
[17] L. Amini, S. Thomas, and O. Spatscheck, Distribution Peering Requirements for Content Distribution Internetworking IETF Draft, Feb. 2001.
[18] A. Barbir, B. Cain, M. Green, M. Hofmann, R. Nair, B. Potter, and O. Spatscheck, Known CDN Request-Routing Mechanisms IETF Draft, June 2001.
[19] A. Biliris, C. Cranor, F. Douglas, M. Rabinovich, S. Sibal, O. Spatscheck, and W. Sturm, CDN Brokering Proc. Workshop Web content Caching (WCW '01), June 2001.
[20] D. Bertsekas, Nonlinear Programming, second ed. Athena Scientific, 1999.
[21] S.H. Low and D.E. Lapsley, Optimization Flow Control I: Basic Algorithm and Convergence IEEE/ACM Trans. Networking, vol. 7, no. 6, Dec. 1999.
[22] G. Alexander Jehle, Advanced Microeconomic Theory. Prentice Hall, 1991.
[23] N. Van Long and A. Soubeyran, Existence and Uniqueness of Cournot Equilibrium: A Contraction Mapping Approach Economics Letters, vol 67, pp. 345-348, 2000.
[24] D.P. Bertsekas and J.N. Tsitsiklis, Parallel and Distributed Computation.Englewood Cliffs, N.J.: Prentice Hall International, 1989.
[25] G.K. Zipf, Human Behavior and the Principle of Least Effort. Cambridge Mass.: Addison-Wesley, 1949.

Index Terms:
Information storage, dissemination, routing, market model, resource allocation, game theory.
?zg? Er?etin, Leandros Tassiulas, "Market-Based Resource Allocation for Content Delivery in the Internet," IEEE Transactions on Computers, vol. 52, no. 12, pp. 1573-1585, Dec. 2003, doi:10.1109/TC.2003.1252853
Usage of this product signifies your acceptance of the Terms of Use.