期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 4, 期 3, 页码 2401-2406出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2899224
关键词
Cloud data centers; Stochastic configuration networks (SCNs); Wavelet decomposition; Workload forecasting; Savitzky-Golay filter
类别
资金
- National Natural Science Foundation of China (NSFC) [61802015, 61703011]
- National Defense Pre-Research Foundation of China [41401020401, 41401050102]
- National Science and Technology Major Project of the Ministry of Science and Technology of China [2018ZX07111005]
Cloud services have rapidly grown in cloud data centers (CDCs). Accurate workload prediction benefits CDCs since appropriate resource provisioning can be performed for their providers to ensure the full satisfaction of service-level agreement (SLA) requirements fromusers. Yet these providers face some challenging issues in accurate workload prediction, i.e., how to achieve high accuracy and fast learning of prediction models. Consistent efforts have been made to address them. This letter proposes an innovative integrated forecasting method that combines stochastic configuration networks with Savitzky-Golay smoothing filter and wavelet decomposition to forecast workload at the succeeding time slot. We first smooth the workload via a Savitzky-Golay filter. Then, we adopt wavelet decomposition to decompose smoothed outcome into multiple components. Supported by stochastic configuration networks, an integrated model is established, which can well describe statistical features both of detail and trend components. Extensive experimental outcomes have explicated that our approach realizes better prediction results and quicker training than those of representative prediction approaches.
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