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Efficient Resources Provisioning Based on Load Forecasting in Cloud

Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application's actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance....

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Detalles Bibliográficos
Autores principales: Hu, Rongdong, Jiang, Jingfei, Liu, Guangming, Wang, Lixin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951090/
https://www.ncbi.nlm.nih.gov/pubmed/24701160
http://dx.doi.org/10.1155/2014/321231
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author Hu, Rongdong
Jiang, Jingfei
Liu, Guangming
Wang, Lixin
author_facet Hu, Rongdong
Jiang, Jingfei
Liu, Guangming
Wang, Lixin
author_sort Hu, Rongdong
collection PubMed
description Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application's actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements.
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spelling pubmed-39510902014-04-03 Efficient Resources Provisioning Based on Load Forecasting in Cloud Hu, Rongdong Jiang, Jingfei Liu, Guangming Wang, Lixin ScientificWorldJournal Research Article Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application's actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements. Hindawi Publishing Corporation 2014-02-20 /pmc/articles/PMC3951090/ /pubmed/24701160 http://dx.doi.org/10.1155/2014/321231 Text en Copyright © 2014 Rongdong Hu et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hu, Rongdong
Jiang, Jingfei
Liu, Guangming
Wang, Lixin
Efficient Resources Provisioning Based on Load Forecasting in Cloud
title Efficient Resources Provisioning Based on Load Forecasting in Cloud
title_full Efficient Resources Provisioning Based on Load Forecasting in Cloud
title_fullStr Efficient Resources Provisioning Based on Load Forecasting in Cloud
title_full_unstemmed Efficient Resources Provisioning Based on Load Forecasting in Cloud
title_short Efficient Resources Provisioning Based on Load Forecasting in Cloud
title_sort efficient resources provisioning based on load forecasting in cloud
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951090/
https://www.ncbi.nlm.nih.gov/pubmed/24701160
http://dx.doi.org/10.1155/2014/321231
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