2012 International Conference on Cloud and Service Computing (2012)
Shanghai, China China
Nov. 22, 2012 to Nov. 24, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSC.2012.41
Cloud computing as a new IT technology is burgeoning and an increasing number of providers are offering various web services related to cloud computing. Meanwhile, the demands of different kinds of users are also rising sharply. In order to maximize the revenue, a proper pricing model is in desperate need. Nowadays, most of the providers are using static pricing which neglects the changes of supply and demand. Since the web services are easy to access and can be used by a large number of users, a dynamic pricing model aimed at maximizing the revenue is proposed. Our dynamic pricing model can automatically adjust the prices of resources according to the demands from users and the pricing for packages is based on Apriori Algorithm. Furthermore, the dynamic pricing model also can be adjusted and optimized by Genetic Annealing Algorithm so as to well adapt to the changes of Supply and demand. Compared with the static pricing model, the dynamic pricing model can increase the revenue to a considerable extent.
data mining, dynamic pricing, IaaS, cloud computing, maximize revenue
H. Qin, X. Wu, J. Hou, H. Wang, W. Zhang and W. Dou, "Self-Adaptive Cloud Pricing Strategies with Markov Prediction and Data Mining Method," 2012 International Conference on Cloud and Service Computing(CSC), Shanghai, China China, 2012, pp. 219-226.