Comparison of Two Yield Management Strategies for Cloud Service Providers
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.
|Date of creation:||May 2013|
|Date of revision:||May 2013|
|Publication status:||Published in Proceedings of the 8th International Conference on Grid and Pervasive Computing (GPC 2013).|
|Contact details of provider:|| Postal: 599 Gwanak-Ro, Gwanak-Gu, Seoul 151-744|
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- Jorn Altmann & Matthias Hovestadt & Odej Kao, 2011. "Business Support Service Platform for Providers in Open Cloud Computing Markets," TEMEP Discussion Papers 201179, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Sep 2011.
- Han, Lei, 2009. "Market acceptance of cloud computing: An analysis of market structure, price models and service requirements," Bayreuth Reports on Information Systems Management 42, University of Bayreuth, Chair of Information Systems Management.
- Mohammad Mahdi Kashef & Jorn Altmann, 2011. "A Cost Model for Hybrid Clouds," TEMEP Discussion Papers 201182, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Dec 2011.
- Wen-Chyuan Chiang & Jason C.H. Chen & Xiaojing Xu, 2007. "An overview of research on revenue management: current issues and future research," International Journal of Revenue Management, Inderscience Enterprises Ltd, vol. 1(1), pages 97-128.
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