Abstract
Several Cloud computing business models have been developed and implemented, including dynamic pricing schemes. This paper extends the known concepts of revenue management to the specific case of Cloud computing from two perspectives. First, we propose system architecture for Cloud service providers for combining demand-based pricing and scheduling. Second, a comparison of two yield management methods for cloud computing has been compared: Limited Discount Period Algorithm and VM Reservation Level Algorithm. By taking advantage of demand estimation, the two algorithms find the optimum number of VMs that are sold at full price and the optimum time period before the allocation when the prices should change. Simulation results show that both yield management methods outperform static pricing models and the algorithms perform differently considering the deviation of demand.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Tsakalozos, K., Kllapi, H., Sitaridi, E., Roussopoulos, M., Paparas, D., Delis, A.: Flexible use of cloud resources through profit maximization and price discrimination. In: Proceedings of the 2011 IEEE 27th International Conference on Data Engineering, pp. 75–86. IEEE Computer Society (2011)
Altmann, J., Hovestadt, M., Kao, O.: Business support service platform for providers in open cloud computing markets. In: 2011 The 7th International Conference on Networked Computing (INC), pp. 149–154 (2011)
McGill, J.I., Van Ryzin, G.J.: Revenue Management: Research Overview and Prospects. Transportation Science 33, 233–256 (1999)
Hayes, B.: Cloud computing. Commun. ACM 51, 9–11 (2008)
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. University of California at Berkeley (2009)
Mell, P., Grance, T.: The NIST definition of cloud computing. National Institute of Standards and Technology 53, 50 (2009)
Team, M.S.R.: Cloud Computing takes off. BLUE PAPER (2011)
Han, L.: Market Acceptance of Cloud Computing - An Analysis of Market Structure, Price Models and Service Requirements. Information Systems Management 42 (2009)
Smith, B.C., Leimkuhler, J.F., Darrow, R.M.: Yield Management at American Airlines. Interface 22, 8–31 (1992)
Iallat, F., Ancarani, F.: Yield management, dynamic pricing and CRM in telecommunication. Journal of Services Marketing (2008)
Kimes, S.E.: The basics of yield management. The Cornell H.R.A Quarterly (1989)
Belobaba, P.P.: Application of a probabilistic decision model to airlines eat inventory control Operations Research 37,14 (1987)
Gayar, N.F.E., Saleh, M., Atiya, A., El-Shishiny, H., Zakhary, A.A.Y.F., Habib, H.A.A.M.: An integrated framework for advanced hotel revenue management Hospitality Management 23, 14 (2011)
Relihan, W.J.: The Yield-Management Approach to Hotel-Room Pricing
Netessine, S., Shumsky, R.: Yield Management (1999)
Sulistio, A., Kim, K.H., Buyya, R.: Using Revenue Management to Determine Pricing of Reservations. In: IEEE International Conference on e-Science and Grid Computing, Bangalore, pp. 396–405 (2007)
Anandasivam, A., Neumann, D.: Managing Revenue in Grids. System Sciences. In: 42nd Hawaii International Conference on HICSS 2009, Big Island, HI, pp. 1–10 (2009)
Arlitt, M.F., Williamson, C.L.: Web server workload characterization: The search for invariants. Performance Evaluation Review 24, 126–137 (1996)
Cherkasova, L., Gupta, M.: Analysis of enterprise media server workloads: Access patterns, locality, content evolution, and rates of change. IEEE/ACM Transactions on Networking 12, 781–794 (2004)
Gmach, D., Rolia, J., Cherkasova, L., Kemper, A.: Workload analysis and demand prediction of enterprise data center applications, pp. 171–180 (2007)
Van, H.N., Tran, F.D., Menaud, J.M.: SLA-aware virtual resource management for cloud infrastructures, pp. 357–362 (2009)
Chiang, W.-C., Chen, J.C.H., Xu, X.: An overview of research on revenue management: current issues and future research Int. J. Revenue Management 1 (2007)
Kashef, M.M., Altmann, J.: A cost model for hybrid clouds. In: Vanmechelen, K., Altmann, J., Rana, O.F. (eds.) GECON 2011. LNCS, vol. 7150, pp. 46–60. Springer, Heidelberg (2012)
Paleologo, G.A.: Price-at-Risk: A methodology for pricing utility computing services. Systems Journal 43 (2004)
Svrcek, T.: Modeling airline group passenger demand for revenue optimization. Massachusetts Institute of Technology, Flight Transportation Laboratory, Cambridge, Mass. (1991)
Monroe, K.B.: Pricing: Making profitable decisions, 2nd edn. McGraw-Hill Companies (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kashef, M.M., Uzbekov, A., Altmann, J., Hovestadt, M. (2013). Comparison of Two Yield Management Strategies for Cloud Service Providers. In: Park, J.J.(.H., Arabnia, H.R., Kim, C., Shi, W., Gil, JM. (eds) Grid and Pervasive Computing. GPC 2013. Lecture Notes in Computer Science, vol 7861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38027-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-38027-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38026-6
Online ISBN: 978-3-642-38027-3
eBook Packages: Computer ScienceComputer Science (R0)