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. We pr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/321231 |
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| _version_ | 1849408211777486848 |
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| author | Rongdong Hu Jingfei Jiang Guangming Liu Lixin Wang |
| author_facet | Rongdong Hu Jingfei Jiang Guangming Liu Lixin Wang |
| author_sort | Rongdong Hu |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-3a67208225a14e5bb0be9310347628bf |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-3a67208225a14e5bb0be9310347628bf2025-08-20T03:35:51ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/321231321231Efficient Resources Provisioning Based on Load Forecasting in CloudRongdong Hu0Jingfei Jiang1Guangming Liu2Lixin Wang3School of Computer, National University of Defense Technology, Changsha 410073, ChinaSchool of Computer, National University of Defense Technology, Changsha 410073, ChinaSchool of Computer, National University of Defense Technology, Changsha 410073, ChinaSchool of Computer, National University of Defense Technology, Changsha 410073, ChinaCloud 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.http://dx.doi.org/10.1155/2014/321231 |
| spellingShingle | Rongdong Hu Jingfei Jiang Guangming Liu Lixin Wang Efficient Resources Provisioning Based on Load Forecasting in Cloud The Scientific World Journal |
| 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 |
| url | http://dx.doi.org/10.1155/2014/321231 |
| work_keys_str_mv | AT rongdonghu efficientresourcesprovisioningbasedonloadforecastingincloud AT jingfeijiang efficientresourcesprovisioningbasedonloadforecastingincloud AT guangmingliu efficientresourcesprovisioningbasedonloadforecastingincloud AT lixinwang efficientresourcesprovisioningbasedonloadforecastingincloud |