Self-Adaptive Cloud Pricing Strategies with Markov Prediction and Data Mining Method

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.

[1]  Marc Premm,et al.  Bid price control and dynamic pricing in clouds , 2009, ECIS.

[2]  Robert L. Grossman,et al.  The Case for Cloud Computing , 2009, IT Professional.

[3]  Bingsheng He,et al.  Distributed Systems Meet Economics: Pricing in the Cloud , 2010, HotCloud.

[4]  Dave Durkee,et al.  Why cloud computing will never be free , 2010, ACM Queue.

[5]  Henk Sol,et al.  Proceedings of the 3rd European Conference on Information Systems , 1995 .

[6]  Ben Jai.  The economy of parallel and distributed computing in the cloud , 2011, Proceedings of 2011 International Symposium on VLSI Design, Automation and Test.

[7]  Takashi Washio,et al.  An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data , 2000, PKDD.

[8]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[9]  S. M. Lim,et al.  Automated Parameter Optimization of Double Frequency Modulation Synthesis Using the Genetic Annealing Algorithm , 1996 .

[10]  Mario Macías,et al.  A genetic model for pricing in cloud computing markets , 2011, SAC.

[11]  Ahmed Patel,et al.  Review of pricing models for grid & cloud computing , 2011, 2011 IEEE Symposium on Computers & Informatics.

[12]  M. E. Johnson,et al.  Generalized simulated annealing for function optimization , 1986 .

[13]  Duc Truong Pham,et al.  Intelligent optimisation techniques , 2000 .

[14]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[15]  Yong Meng Teo,et al.  Dynamic Resource Pricing on Federated Clouds , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[16]  Baochun Li,et al.  A theory of cloud bandwidth pricing for video-on-demand providers , 2012, 2012 Proceedings IEEE INFOCOM.

[17]  Panagiotis Kalagiakos,et al.  Cloud Computing learning , 2011, 2011 5th International Conference on Application of Information and Communication Technologies (AICT).

[18]  Baochun Li,et al.  Maximizing revenue with dynamic cloud pricing: The infinite horizon case , 2012, 2012 IEEE International Conference on Communications (ICC).

[19]  J. Dale Prince,et al.  Introduction to Cloud Computing , 2011 .