Cai, Z.; Li, X.; Ruiz García, R.; Li, Q. (2018). Price forecasting for spot instances in Cloud computing. Future Generation Computer Systems. 79:38-53. https://doi.org/10.1016/j.future.2017.09.038
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/146877
Title:
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Price forecasting for spot instances in Cloud computing
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Author:
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Cai, Zhicheng
Li, Xiaoping
Ruiz García, Rubén
Li, Qianmu
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UPV Unit:
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Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
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Issued date:
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Abstract:
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[EN] Big data applications usually need to rent a large number of virtual machines from Cloud computing providers. As a result of the policies employed by Cloud providers, the prices of spot virtual machine instances ...[+]
[EN] Big data applications usually need to rent a large number of virtual machines from Cloud computing providers. As a result of the policies employed by Cloud providers, the prices of spot virtual machine instances behavior stochastically. Spot prices (prices of spot instances) fluctuate greatly or have multiple regimes. Choosing virtual machines according to trends in prices is helpful in decreasing the resource rental cost. Existing price prediction methods are unable to accurately predict prices in these environments. As a result, a dynamic-ARIMA and two markov regime-switching autoregressive model based forecasting methods have been developed in this paper. Experimental results show that the proposals are better than the existing MonthAR in most scenarios. (C) 2017 Elsevier B.V. All rights reserved.
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Subjects:
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Cloud computing
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Spot price
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Forecast
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Markov regime-switching
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Scheduling
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Copyrigths:
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Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
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Source:
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Future Generation Computer Systems. (issn:
0167-739X
)
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DOI:
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10.1016/j.future.2017.09.038
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Publisher:
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Elsevier
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Publisher version:
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https://doi.org/10.1016/j.future.2017.09.038
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Project ID:
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info:eu-repo/grantAgreement/NSFC//61572127/
info:eu-repo/grantAgreement/NSFC//61602243/
info:eu-repo/grantAgreement/Natural Science Foundation of Jiangsu Province//BK20160846/
info:eu-repo/grantAgreement/Jiangsu Key Laboratory of Image and Video Understanding for Social Safety//30916014107/
info:eu-repo/grantAgreement/MINECO//DPI2015-65895-R/ES/OPTIMIZATION OF SCHEDULING PROBLEMS IN CONTAINER YARDS/
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Thanks:
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The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No. 61602243 and No. 61572127), the Natural Science ...[+]
The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No. 61602243 and No. 61572127), the Natural Science Foundation of Jiangsu Province (Grant No. BK20160846), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Grant No. 30916014107). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD" (No. DPI2015-65895-R) financed by FEDER funds.
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Type:
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Artículo
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