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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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A Novel Simulation Model for Pricing Different QoS Levels in IP Networks

RADONJIC DJOGATOVIC, V. See more information about RADONJIC DJOGATOVIC, V. on SCOPUS See more information about RADONJIC DJOGATOVIC, V. on IEEExplore See more information about RADONJIC DJOGATOVIC, V. on Web of Science, DJOGATOVIC, M. See more information about DJOGATOVIC, M. on SCOPUS See more information about DJOGATOVIC, M. on SCOPUS See more information about DJOGATOVIC, M. on Web of Science
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Download PDF pdficon (896 KB) | Citation | Downloads: 231 | Views: 514

Author keywords
decision making, IP networks, quality of service, programming, simulation

References keywords
simulation(11), pricing(10), networks(5), quality(4), discrete(4), communications(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-02-28
Volume 20, Issue 1, Year 2020, On page(s): 27 - 34
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.01004
Web of Science Accession Number: 000518392600004
SCOPUS ID: 85083703162

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The selection of the appropriate pricing concept is one of the most important business decisions an Internet service provider (ISP) has to make. An efficient pricing concept implies that price reflects the quality of service (QoS) obtained by a service provider. In this paper, we propose four billing scenarios, each with applied user-centric QoS-based pricing concept, which is focused on users' demands, defined through QoS and price requirements, as well as delivered QoS. The main issue of this research is to analyse how much ISP's revenue will vary depending on the applied billing scenario. For this purpose, we propose a novel discrete event simulation model. he proposed simulation model is implemented using a newly created programming library based on the event scheduling strategy. This model can be helpful to an ISP in the process of decision making which billing scenario to choose in order to maximize its revenue. Output parameters obtained through the simulation analysis are ISP's revenue and service prices for different billing scenarios.

References | Cited By  «-- Click to see who has cited this paper

[1] ITU-T Recommendation E.800, Definitions of terms related to quality of service, 2008.

[2] J. Altmann, K. Chu, "How to charge for network services: flat-rate or usage-based?" Computer Networks, vol. 36, no. 5-6, pp. 519-531, 2001.
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[3] J. MacKie-Mason, L. Murphy, J. Murphy, "The role of responsive pricing in the Internet," The Journal of Electronic Publishing, vol.1, no.1&2, 1995.

[4] J. Chod, N. Rudi, "Resource flexibility with responsive pricing," Operations Research, vol. 53, no. 3, pp. 532-548, 2005.
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[5] A. Nevo, J. L. Turner, J. W. Williams, "Usage-based pricing and demand for residential broadband," Econometrica, vol. 84, pp. 411-443, 2016.
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[6] S. Sen, C. Joe-Wong, S. Ha, M. Chiang, "Incentivizing time-shifting of data: A survey of time-dependent pricing for internet access," IEEE Communications Magazine, vol. 50, no. 11, pp. 91-99, 2012.
[CrossRef] [Web of Science Times Cited 80] [SCOPUS Times Cited 102]

[7] A. Guerrero-Ibanez, J. Contreras-Castillo, A. Barba, A. Reyes, "A QoS-based dynamic pricing approach for services provisioning in heterogeneous wireless access networks," Pervasive and Mobile Computing, vol. 7, no. 5, pp. 569-583, 2011.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 10]

[8] D. Elreedy, A. F. Atiya, H. Fayed, M. Saleh, "A framework for an agent-based dynamic pricing for broadband wireless price rate plans," Journal of Simulation, vol. 13, no. 2, pp. 96-110, 2019.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]

[9] B. Al-Manthari, N. Nasser, H. Hassanein, "Congestion pricing in wireless cellular networks," IEEE Communications Surveys & Tutorials, vol. 13, no. 3, pp. 358-371, 2011.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 23]

[10] V. Radonjic Đogatovic, M. Đogatovic, M. Stanojevic, "Simulation analysis of quality of business in IP networks," Yugoslav Journal of Operations Research, [S.l.], 2018.
[CrossRef] [SCOPUS Times Cited 1]

[11] UsQoP - User-centric QoS-based pricing simulation model, from, accessed on 11.11.2019.

[12] T. H. Rasmussen , N. Hansen, S. Lazarova-Molnar, "A discrete-event simulation tool for decision support in selecting project scheduling strategies", 7th International Conference ICMSAO, Sharjah, UAE, 2017, pp. 1-5.
[CrossRef] [SCOPUS Times Cited 1]

[13] G. E. Vieira, M. Kuck, E. Frazzon, M. Freitag, Evaluating the robustness of production schedules using discrete-event simulation, IFAC-PapersOnLine, vol. 50, no. 1, 2017, pp. 7953-7958,
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[14] F. M. Alam, S. Mohan, W. Fowler, M. Gopalakrishnan, "A discrete event simulation tool for performance management of web-based application systems," Journal of Simulation, vol. 6, no. 1, pp 21-32, 2012.
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[15] V. Đogatovic, A. Kostic-Ljubisavljevic, Telecommunications pricing fundamentals, pp. 53-58, University of Belgrade, 2015.

[16] R. Stankiewicz, P. Cholda, A. Jajszczyk, "QoX: What is It Really?," IEEE Communications Magazine, vol. 49, no. 3, pp. 148-158, 2011.
[CrossRef] [Web of Science Times Cited 73] [SCOPUS Times Cited 95]

[17] S. Lindskog, E. Jonsson, "Adding Security to Quality of Service Architectures," Perspectives on Multimedia, R. Burnett, A. Brunstrom, A.G. Nilsson, Ed., John Wiley & Sons, 2003, pp.145-158.
[CrossRef] [SCOPUS Times Cited 6]

[18] G. I. Palmer, V. A. Knight, P. R. Harper, A. L. Hawa, "Ciw: An open-source discrete event simulation library," Journal of Simulation, vol. 13, no. 1, pp. 68-82, 2019.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 4]

[19] M. A. Law, Simulation modeling and analysis, 5th Edition, pp. 93-102, McGraw-Hill Education, 2014.

[20] B. A. Downey, Think Python: How to think like a computer scientist, 2nd Edition, pp. 225, O'Reilly, 2015.

[21] D. Kashyap, J. Viradiya, "A Survey Of Various Load Balancing Algorithms In Cloud Computing," International Journal of Scientific & Technology Research, vol. 3, no. 11, pp. 115-119, 2014.

[22] G. Allen, M. Owens, The definitive guide to SQLite, 2nd Edition, pp. 8-13, Apress Berkley, 2010.

[23] PYES - PYthon Event Scheduling simulation library, from, accessed on 11.11.2019.

[24] P. Reichl, P. Zwickl, P. Maille, M. Varela, "What you pay is what you get?" Quality and User Experience, 3:5, 2018.

[25] M. Falkner, M. Devetsikiotis, I. Lambadaris, "An overview of pricing concepts for broadband IP networks," IEEE Communications Surveys & Tutorials, vol. 3, no. 2, pp. 2-13, 2000.

References Weight

Web of Science® Citations for all references: 390 TCR
SCOPUS® Citations for all references: 490 TCR

Web of Science® Average Citations per reference: 15 ACR
SCOPUS® Average Citations per reference: 19 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-05-11 20:36 in 140 seconds.

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