Authors:
Caesar Wu
;
Rajkumar Buyya
and
Kotagiri Ramamohanarao
Affiliation:
Cloud Computing and Distribution Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne and Australia
Keyword(s):
Cloud Market Segmentation, Hierarchical Clustering, Time Series Forecasting, Cloud Service Providers.
Abstract:
The topics of cloud pricing models and resources management have been receiving enormous attention recently. However, very few studies have considered the importance of cloud market segmentation. Moreover, there is no a better, practical and quantifiable solution for a cloud service providers (CSP) to segment cloud market. We propose a novel solution that combines both hierarchical clustering and time series forecasting on the basis of the classical theory of market segmentation. In comparison with some traditional approaches, such as nested, analytic, Delphi, and strategy-based approaches, our method is much more effective, flexible, measurable and practical for CSPs to implement their cloud market strategies by rolling out different pricing models. Our tested results and empirical analysis show that our solution can efficiently segment cloud markets and also predict the market demands. Our primary goal is to offer a new solution so that CSPs can tail its limited cloud resources for
its targeted market or cloud customers.
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