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Estimation of cloud node acquisition

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2 Author(s)
Ahmed, Waseem ; Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology (TNLIST), Tsinghua University, Beijing 100084, China, and Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China ; Wu, Yongwei

Over the past decade, there has been a paradigm shift leading consumers and enterprises to the adoption of cloud computing services. Even though most cases are still in the early stages of transition, there has been a steady increase in the implementation of the pay-as-you-go or pay-as-you-grow models offered by cloud providers. Whether applied as an extension of virtual infrastructure, software, or platform as a service, many users are still challenged by the estimation of adequate resource allocation and the wide variations in pricing. Customers require a simple method of predicting future demand in terms of the number of nodes to be allocated in the cloud environment. In this paper, we review and discuss existing methodologies for estimating the demand for cloud nodes and their corresponding pricing policies. Based on our review, we propose a novel approach using the Hidden Markov Model to estimate the acquisition of cloud nodes.

Published in:

Tsinghua Science and Technology  (Volume:19 ,  Issue: 1 )