Efficient Resources Provisioning Based on Load Forecasting in Cloud
Joint Authors
Hu, Rongdong
Jiang, Jingfei
Liu, Guangming
Wang, Lixin
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-20
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Hu, Rongdong& Jiang, Jingfei& Liu, Guangming& Wang, Lixin. 2014. Efficient Resources Provisioning Based on Load Forecasting in Cloud. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1049216
Modern Language Association (MLA)
Hu, Rongdong…[et al.]. Efficient Resources Provisioning Based on Load Forecasting in Cloud. The Scientific World Journal No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1049216
American Medical Association (AMA)
Hu, Rongdong& Jiang, Jingfei& Liu, Guangming& Wang, Lixin. Efficient Resources Provisioning Based on Load Forecasting in Cloud. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1049216
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049216