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 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.
School of Computer, National University of Defense Technology, Changsha 410073, China.
Rongdong
R
Hu
School of Computer, National University of Defense Technology, Changsha 410073, China.
Jingfei
J
Jiang
School of Computer, National University of Defense Technology, Changsha 410073, China ; National Supercomputer Center, Tianjin 300457, China.
Guangming
G
Liu
School of Computer, National University of Defense Technology, Changsha 410073, China.
Lixin
L
Wang
D005544:Forecasting; D016247:Information Storage and Retrieval / Q000592:standards / Q000639:trends*; D020407:Internet / Q000592:standards / Q000639:trends*; D016571:Neural Networks, Computer