TY - JOUR
T1 - Temporal Prediction of Multiapplication Consolidated Workloads in Distributed Clouds
AU - Bi, Jing
AU - Yuan, Haitao
AU - Zhou, Mengchu
N1 - Funding Information:
Manuscript received November 10, 2018; accepted January 11, 2019. Date of publication February 21, 2019; date of current version October 4, 2019. This paper was recommended for publication by Associate Editor S. Yin and Editor M. P. Fanti upon evaluation of the reviewers’ comments. This work was supported in part by the National Natural Science Foundation of China under Grant 61802015 and Grant 61703011, in part by the National Science and Technology Major Project under Grant 2018ZX07111005, and in part by the National Defense Foundation Research Common Project under Grant 41401020401 and Grant 41401050102. (Corresponding author: Haitao Yuan.) J. Bi is with the Faculty of Information Technology, School of Software Engineering, 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).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - With their fast development and deployment, a large number of cloud services provided by distributed cloud data centers have become the most important part of Internet services. In spite of numerous benefits, their providers face some challenging issues, e.g., dynamic resource scaling and power consumption. Workload prediction plays a crucial role in addressing them. Accuracy and fast learning are the key performances. Its consistent efforts have been made for their improvement. This paper proposes an integrated prediction method that combines the Savitzky-Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In this approach, a task time series is first smoothed by the SG filter, and the smoothed one is then decomposed into multiple components via wavelet decomposition. Based on them, an integrated model is, for the first time, established and can well characterize the statistical features of both trend and detailed components. Experimental results demonstrate that it achieves better prediction results and faster learning speed than some representative prediction methods. Note to Practitioners-Workload prediction plays an important role in constructing scalable and green distributed cloud data centers. This paper presents a novel and fundamental methodology to achieve accuracy and fast learning for workload prediction. It develops an integrated prediction approach that combines the Savitzky-Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In order to establish a fine prediction model for the obtained information while achieving better prediction results and faster learning speed, this paper proposes an integrated method, SGW-S, to build a prediction model of a task time series and determine its optimal model parameters. The experimental results in the real-world data set show that the proposed method outperforms baseline methods in predicting the large-scale task time series. The proposed approach can aid the design and optimization of industrial cloud data centers and practitioners' prediction of different types of task time series.
AB - With their fast development and deployment, a large number of cloud services provided by distributed cloud data centers have become the most important part of Internet services. In spite of numerous benefits, their providers face some challenging issues, e.g., dynamic resource scaling and power consumption. Workload prediction plays a crucial role in addressing them. Accuracy and fast learning are the key performances. Its consistent efforts have been made for their improvement. This paper proposes an integrated prediction method that combines the Savitzky-Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In this approach, a task time series is first smoothed by the SG filter, and the smoothed one is then decomposed into multiple components via wavelet decomposition. Based on them, an integrated model is, for the first time, established and can well characterize the statistical features of both trend and detailed components. Experimental results demonstrate that it achieves better prediction results and faster learning speed than some representative prediction methods. Note to Practitioners-Workload prediction plays an important role in constructing scalable and green distributed cloud data centers. This paper presents a novel and fundamental methodology to achieve accuracy and fast learning for workload prediction. It develops an integrated prediction approach that combines the Savitzky-Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In order to establish a fine prediction model for the obtained information while achieving better prediction results and faster learning speed, this paper proposes an integrated method, SGW-S, to build a prediction model of a task time series and determine its optimal model parameters. The experimental results in the real-world data set show that the proposed method outperforms baseline methods in predicting the large-scale task time series. The proposed approach can aid the design and optimization of industrial cloud data centers and practitioners' prediction of different types of task time series.
KW - Distributed cloud data centers (DCDCs)
KW - Savitzky-Golay (SG) filter
KW - stochastic configuration networks (SCNs)
KW - task time-series prediction
KW - wavelet decomposition
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U2 - 10.1109/TASE.2019.2895801
DO - 10.1109/TASE.2019.2895801
M3 - Article
AN - SCOPUS:85077492061
SN - 1545-5955
VL - 16
SP - 1763
EP - 1773
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 4
M1 - 8648212
ER -