Advertisement

Agent-Based Simulations of the Software Market under Different Pricing Schemes for Software-as-a-Service and Perpetual Software

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6296)

Abstract

In this paper, we present agent-based simulations that model the interactions between software buyers and vendors in a software market that offers Software-as-a-Service (SaaS) and perpetual software (PS) licensing under different pricing schemes. In particular, scenarios are simulated, in which vendor agents dynamically set prices. Customer (or buyer) agents respond to these prices by selecting the software license scheme according to four fundamental criteria using Analytic Hierarchy Process (AHP) as decision support mechanism. These criteria relate to finance, software capability, organization, and vendor. Three pricing schemes are implemented for our simulations: derivative-follower (DF), demand-driven (DD), and competitor-oriented (CO). The results show that DD scheme is the most effective method but hard to implement since it requires perfect knowledge about market conditions. This result is supported through a price sensitivity analysis.

Keywords

Software-as-a-Service pricing perpetual software pricing agent-based simulation Analytic Hierarchy Process (AHP) dynamic pricing decision support 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wailgum, T.: What to Ask Before Saying Yes to SaaS, Cloud Computing. In: The New York Times, October 27 (2008)Google Scholar
  2. 2.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: Above the Clouds: A Berkeley View of Cloud Computing. Reliable Adaptive Distributed Systems Laboratory, UC Berkeley (2009)Google Scholar
  3. 3.
  4. 4.
    Colombo, E., Francalanci, C.: Selecting CRM Packages Based on Architectural, Functional, and Cost Requirements: Empirical Validation of a Hierarchical Ranking Model. Requirements Engineering 9, 186–203 (2004)CrossRefGoogle Scholar
  5. 5.
    Jadhav, A.S., Sonar, R.M.: Evaluating and Selecting Software Packages: A Review. Information and Software Technology 51, 555–563 (2009)CrossRefGoogle Scholar
  6. 6.
    Godse, M., Mulik, S.: An Approach for Selecting Software-as-a-Service (SaaS) Product. In: IEEE International Conference on Cloud Computing (2009)Google Scholar
  7. 7.
    Lai, V.S., Trueblood, R.P., Wong, B.K.: Software Selection: A Case Study of the Application of the Analytical Hierarchical Process to the Selection of a Multimedia Authoring System. Information & Management 36, 221–232 (1999)CrossRefGoogle Scholar
  8. 8.
    Mulebeke, J.A.W., Zheng, L.: Analytical Network Process for Software Selection in Product Development: A Case Study. Journal of Engineering and Technology Management 23, 337–352 (2006)CrossRefGoogle Scholar
  9. 9.
    Rohitratana, J., Altmann, J.: Software Resource Management Considering the Interrelation between Explicit Cost, Energy Consumption, and Implicit Cost: A Decision Support Model for IT Managers. In: Multikonferenz Wirtschaftsinformatik (2010)Google Scholar
  10. 10.
    Lehmann, S., Buxmann, P.: Pricing Strategies of Software Vendors. Business and Information Systems Engineering 6, 452–462 (2009)CrossRefGoogle Scholar
  11. 11.
    Marn, M.V., Roegner, E.V., Zawada, C.C.: Pricing New Products. The McKinsey Quarterly, http://www.mckinseyquarterly.com/article_print.aspx?L2=16&L3=19&ar=1329
  12. 12.
    Kephart, J.O., Hanson, J.E., Greenwald, A.R.: Dynamic Pricing by Software Agents. Computer Networks 32, 731–752 (2000)CrossRefGoogle Scholar
  13. 13.
    Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York (1980)zbMATHGoogle Scholar
  14. 14.
    Matsuda, Y., Whang, S.: Dynamic Pricing for Network Service: Equilibrium and Stability. Management Science 45, 857–869 (1999)CrossRefzbMATHGoogle Scholar
  15. 15.
    Dasgupta, P., Hashimoto, Y.: Multi-attribute Dynamic Pricing for Online Markets using Intelligent Agents. In: Third International Joint Conference on Autonomous Agents and Multiagent Systems, New York, pp. 277–284 (2004)Google Scholar
  16. 16.
    Dasgupta, P., Das, R.: Dynamic Pricing with Limited Competitor Information in a Multi-Agent Economy. In: Scheuermann, P., Etzion, O. (eds.) CoopIS 2000. LNCS, vol. 1901, pp. 299–310. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  17. 17.
    Altmann, J., Rhodes, L.: Dynamic Netvalue Analyzer - A Pricing Plan Modeling Tool for ISPs Using Actual Network Usage Data. In: IEEE WECWIS2002, International Workshop on Advance Issues of E-Commerce and Web-Based Information Systems, Newport Beach, USA (2002)Google Scholar
  18. 18.
    Altmann, J., Varaiya, P.: INDEX Project: User Support for Buying QoS with regard to User’s Preferences. In: IWQOS 1998, Sixth IEEE/IFIP International Workshop on Quality of Service, Napa, USA, pp. 101–104 (1998)Google Scholar
  19. 19.
    Altmann, J., Rupp, B., Varaiya, P.: Internet Demand under Different Pricing Schemes. In: ACM EC 1999, ACM Conference on Electronic Commerce, Denver, Colorado, USA (1999)Google Scholar
  20. 20.
    Risch, M., Altmann, J.: Enabling Open Cloud Markets Through WS-Agreement Extensions. In: Service Level Agreements in Grids Workshop, in conjunction with GRID2009, Banff, Canada. CoreGRID Springer Series (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Technology Management, Economics, and Policy Program, Department of Industrial Engineering, College of EngineeringSeoul National UniversitySeoulSouth Korea

Personalised recommendations