Abstract
Cloud computing is the delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a metered service over a network (typically the Internet). To maximize the revenue of cloud service providers, a dynamic pricing model is proposed, which consists of two data mining methods. The first data mining method is the k-means algorithm with which historical data are classified into groups. The second one is Bayes decision that can forecast the trend of user-preferred cloud service packages. In proposed pricing model, BP-neutral network is applied to forecast the price which can maximize the revenue. Compared with the static pricing model and the models without k-means algorithm, the proposed model can meet customers’ demand better and outperform them in revenue maximization.
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Wu, X., Hou, J., Zhuo, S., Zhang, W. (2013). Dynamic Pricing Strategy for Cloud Computing with Data Mining Method. In: Zhang, Y., Li, K., Xiao, Z. (eds) High Performance Computing. HPC 2012. Communications in Computer and Information Science, vol 207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41591-3_4
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DOI: https://doi.org/10.1007/978-3-642-41591-3_4
Publisher Name: Springer, Berlin, Heidelberg
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