Volume 26, Issue 2 (Summer 2021)
JPBUD 2021, 26(2): 3-41 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Ahmadi A. (2021). Optimal Pricing for Services of Iran’s Internet Exchange Points: A Fuzzy Geometric Programming Approach. JPBUD. 26(2), 3-41. doi:10.52547/jpbud.26.2.3
URL: http://jpbud.ir/article-1-1970-en.html
URL: http://jpbud.ir/article-1-1970-en.html
Researches Institute, Allameh Tabatabae’i University, Tehran, Iran. , a.ahmadi@atu.ac.ir
Abstract: (3021 Views)
The optimal pricing of goods and services is one of the most challenging issues in economics. Scarcity of resources which causes a good or a service priced more expensive, on the one hand, and its costs of production on the other, motivate researchers to find optimum price. This paper investigates an optimal pricing scheme for Iran’s IXPs services including 1Ge ports, 10GE, and 100GE ports through a Fuzzy Geometric Programming model. “Internet exchange points” (IXPs) are infrastructures through which Autonomous Systems (AS) such as Internet Service Providers (ISPs), Content Delivery Networks (CDNs), Content Providers (CPs), and other internet networks, peer with each other to exchange internet traffic more efficiently. These points provide their members with a wide variety of services. How should these services be priced so that the maximum number of potential members can be connected to IXP and also the profit of IXP is maximized? The FGP model answers these questions. According to the results of this paper, the demand for the services of IXPs is a function of the price of ports and the number of services provided in each IXP. This demand function is normal; i.e., the quantity demanded has a negative relationship with the port price and a positive relationship with the number of services. The demand function for 1GE ports is price inelastic, while the elasticities of the price for 10GE and 100GE is greater than 1. Also, it has been shown that the optimal price of the Iranian IXP services based on the FGP model proposes a higher price of 10.3% compared to its current price, and a lower price for 10GE and 100GE ports of 1.75% and 14.5% respectively compared to the current price. The sensitivity analysis shows that the changes of price elasticity up to 90%, have no impact on the profit of IXP, but it has a small effect on the optimum price.
Keywords: Internet Pricing, Internet Exchange Points, Demand for IXP, Mathematical Optimization, Fuzzy Geometric Programming
Type of Study: Applicable |
Subject:
Microeconomics
Received: Jan 26 2021 | Accepted: Aug 01 2021 | ePublished: Dec 05 2021
Received: Jan 26 2021 | Accepted: Aug 01 2021 | ePublished: Dec 05 2021
References
1. Accongiagioco, G., Altman, E., Gregori, E., & Lenzini, L. (2014). A Game Theoretical Study of Peering vs Transit in the Internet. Paper Presented at the 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). [DOI:10.1109/INFCOMW.2014.6849330]
2. Bagheri, A., & Nazeman, H. (2020). Investigating Competition in Iran's Electricity Industry. The Journal of Planning and Budgeting, 25(1), 87-108. http://jpbud.ir/article-1-46-fa.html [DOI:10.29252/jpbud.25.1.87]
3. Böttger, T., Antichi, G., Fernandes, E. L., di Lallo, R., Bruyere, M., Uhlig, S., . . . Castro, I. (2018). Shaping the Internet: 10 Years of IXP Growth. arXiv preprint arXiv:1810.10963.
4. Boyd, S., Kim, S.-J., Vandenberghe, L., & Hassibi, A. (2007). A Tutorial on Geometric Programming. Optimization and Engineering, 8(1), 67-127. [DOI:10.1007/s11081-007-9001-7]
5. Chen, C.-K. (2000). Optimal Determination of Quality Level, Selling Quantity and Purchasing Price for Intermediate Firms. Production Planning & Control, 11(7), 706-712. [DOI:10.1080/095372800432179]
6. Courcoubetis, C., & Weber, R. (2003). Pricing Communication Networks: Economics, Technology and Modelling: John Wiley & Sons. [DOI:10.1002/0470867175]
7. Duffin, R., & Peterson, E. L. (1966). Duality Theory for Geometric Programming. SIAM Journal on Applied Mathematics, 14(6), 1307-1349. [DOI:10.1137/0114105]
8. Ecker, J. G. (1980). Geometric Programming: Methods, Computations and Applications. SIAM Review, 22(3), 338-362. [DOI:10.1137/1022058]
9. Ha, S., Sen, S., Joe-Wong, C., Im, Y., & Chiang, M. (2012). TUBE: Time-Dependent Pricing for Mobile Data. Paper Presented at the Proceedings of the ACM SIGCOMM 2012 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication. [DOI:10.1145/2342356.2342402]
10. Hande, P., Chiang, M., Calderbank, R., & Zhang, J. (2010). Pricing Under Constraints in Access Networks: Revenue Maximization and Congestion Management. Paper Presented at the 2010 Proceedings IEEE INFOCOM. [DOI:10.1109/INFCOM.2010.5461954]
11. He, H., Xu, K., & Liu, Y. (2012). Internet Resource Pricing Models, Mechanisms, and Methods. Networking Science, 1(1-4), 48-66. [DOI:10.1007/s13119-011-0004-5]
12. Islam, S., & Mandal, W. A. (2019). Fuzzy Geometric Programming Techniques and Applications: Springer. [DOI:10.1007/978-981-13-5823-4]
13. Kamaei, S., Kamaei, S., & Saraj, M. (2019). Solving a Posynomial Geometric Programming Problem with Fully Fuzzy Approach. Yugoslav Journal of Operations Research, 29(2), 203-220. [DOI:10.2298/YJOR181115005K]
14. Kelly, F. P., Maulloo, A. K., & Tan, D. K. H. (1998). Rate Control for Communication Networks: Shadow Prices, Proportional Fairness and Stability. Journal of the Operational Research Society, 49(3), 237-252. [DOI:10.1057/palgrave.jors.2600523]
15. Kim, D., & Lee, W. J. (1998). Optimal Joint Pricing and Lot Sizing with Fixed and Variable Capacity. European Journal of Operational Research, 109(1), 212-227. [DOI:10.1016/S0377-2217(97)00100-8]
16. MacKie-Mason, J., & Varian, H. (1995). Public Access to the Internet: MIT Press, Chapter Pricing the Internet.
17. Mazumdar, R., Mason, L. G., & Douligeris, C. (1991). Fairness in Network Optimal Flow Control: Optimality of Product Forms. IEEE Transactions on Communications, 39(5), 775-782. [DOI:10.1109/26.87140]
18. Muttitanon, W., & Samanchuen, T. (2020). Internet Cost Reduction Using Internet Exchange Point: A Case Study of Internet Network of Thailand. Wireless Personal Communications, 115(1), 3177-3198. [DOI:10.1007/s11277-020-07198-1]
19. Ojha, A. K., & Biswal, K. (2010). Posynomial Geometric Programming Problems with Multiple Parameters. Journal of Computing, 2(1), 84-90.
20. Rahmaniani, R., Sadjadi, S. J., Shafia, M. A., & Rahmaniyan, N. (2012). The Optimal Pricing Model in an Uncertain and Competitive Environment: Using Possibilitic Geometric Programming Approach. African Journal of Business Management, 6(46), 11565-11574. [DOI:10.5897/AJBM12.704]
21. Rao, S. S. (2019). Engineering Optimization: Theory and Practice: John Wiley & Sons. [DOI:10.1002/9781119454816]
22. Sadjadi, S., Yousefli, A., & Ghezelsoflou, R. (2011). Optimal Pricing for Internet Service Providers: Fuzzy Geometric Programming Model. African Journal of Business Management, 5(17), 7291-7295. [DOI:10.5897/AJBM10.1122]
23. Sato, K., & Nakashima, K. (2020). Optimal Pricing Problem for a Pay-Per-Use System Based on the Internet of Things with Intertemporal Demand. International Journal of Production Economics, 221(1), 107477. [DOI:10.1016/j.ijpe.2019.08.012]
24. Sen, S., Joe-Wong, C., Ha, S., & Chiang, M. (2013). Smart Data Pricing (SDP): Economic Solutions to Network Congestion. Recent Advances in Networking, 1(1), 221-274.
25. Shakkottai, S., Srikant, R., Ozdaglar, A., & Acemoglu, D. (2008). The Price of Simplicity. IEEE Journal on Selected Areas in Communications, 26(7), 1269-1276. [DOI:10.1109/JSAC.2008.080923]
26. You, P.-S., Hsieh, Y.-C., & Huang, C.-M. (2009). A Particle Swarm Optimization Based Algorithm to the Internet Subscription Problem. Expert Systems with Applications, 36(3), 7093-7098. [DOI:10.1016/j.eswa.2008.08.080]
27. Zadeh, L. A. (1978). Fuzzy Sets as a Basis for a Theory of Possibility. Fuzzy Sets and Systems, 1(1), 3-28. [DOI:10.1016/0165-0114(78)90029-5]
28. Zener, C. (1961). A Mathematical Aid in Optimizing Engineering Designs. Proceedings of the National Academy of Sciences of the United States of America, 47(4), 537-539. [DOI:10.1073/pnas.47.4.537]
29. Zhang, F. (2011). Pricing in Multi-Service Communication Networks: A Game-theoretic Approach. (Doctor of Philosophy). The University of Oklahoma.
| Rights and permissions | |
.jpg) |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
