TY - JOUR
T1 - Time-Dependent Cloud Workload Forecasting via Multi-Task Learning
AU - Bi, Jing
AU - Yuan, Haitao
AU - Zhou, Meng Chu
AU - Liu, Qing
N1 - Funding Information:
Manuscript received September 10, 2018; accepted January 13, 2019. Date of publication February 13, 2019; date of current version March 28, 2019. This letter was recommended for publication by Associate Editor Y. Lu and Editor J. Li upon evaluation of the reviewers comments. This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 61802015 and 61703011, in part by the National Defense Pre-Research Foundation of China under Grants 41401020401 and 41401050102, and in part by the National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant 2018ZX07111005. (Corresponding author: Haitao Yuan.) J. Bi is with the Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China, and also with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail:,jing.bi@njit.edu).
PY - 2019/7
Y1 - 2019/7
N2 - 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 from users. 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.
AB - 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 from users. 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.
KW - Cloud data centers
KW - Savitzky-Golay filter
KW - Stochastic configuration networks (SCNs)
KW - Wavelet decomposition
KW - Workload forecasting
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U2 - 10.1109/LRA.2019.2899224
DO - 10.1109/LRA.2019.2899224
M3 - Article
AN - SCOPUS:85063998022
SN - 2377-3766
VL - 4
SP - 2401
EP - 2406
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 3
M1 - 8641310
ER -