TY - GEN
T1 - Hybrid task prediction based on wavelet decomposition and ARIMA model in cloud data center
AU - Bi, Jing
AU - Zhang, Libo
AU - Yuan, Haitao
AU - Zhou, Mengchu
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported in part by a Grant from the Special Financial Grant from the China Postdoctoral Science Foundation (No. 2017T100034), the China Postdoctoral Science Foundation (No. 2016M600912), the Fundamental Research Funds for the Central Universities (No. 2016RC030), the National Natural Science Foundation of China (No. 61703011), and the National Science and Technology Major Project (No. 2018ZX07111005).
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/5/18
Y1 - 2018/5/18
N2 - With the development of Information and Communication Technology (ICT), the service provided by cloud data centers has become a new pattern of Internet services. The prediction of the number of arriving tasks plays a crucial role in resource allocation and optimization for cloud data center providers. This work proposes a hybrid method that combines wavelet decomposition and autoregressive integrated moving average (ARIMA) to predict it at the next time interval. In this approach, the task time series is smoothed by Savitzky-Golay filtering, and then the smoothed time series is decomposed into multiple components via wavelet decomposition. An ARIMA model is established for the statistical characteristics of the trend and components, respectively. Finally, their prediction results are reconstructed via wavelet reduction and the predicted number of arriving tasks is obtained. Experimental results demonstrate that the hybrid method achieves better prediction results compared with some typical prediction methods including ARIMA and neural networks.
AB - With the development of Information and Communication Technology (ICT), the service provided by cloud data centers has become a new pattern of Internet services. The prediction of the number of arriving tasks plays a crucial role in resource allocation and optimization for cloud data center providers. This work proposes a hybrid method that combines wavelet decomposition and autoregressive integrated moving average (ARIMA) to predict it at the next time interval. In this approach, the task time series is smoothed by Savitzky-Golay filtering, and then the smoothed time series is decomposed into multiple components via wavelet decomposition. An ARIMA model is established for the statistical characteristics of the trend and components, respectively. Finally, their prediction results are reconstructed via wavelet reduction and the predicted number of arriving tasks is obtained. Experimental results demonstrate that the hybrid method achieves better prediction results compared with some typical prediction methods including ARIMA and neural networks.
KW - ARIMA
KW - Cloud data center
KW - Savitzky-Golay filtering
KW - task prediction
KW - wavelet decomposition
UR - http://www.scopus.com/inward/record.url?scp=85048237812&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048237812&partnerID=8YFLogxK
U2 - 10.1109/ICNSC.2018.8361342
DO - 10.1109/ICNSC.2018.8361342
M3 - Conference contribution
AN - SCOPUS:85048237812
T3 - ICNSC 2018 - 15th IEEE International Conference on Networking, Sensing and Control
SP - 1
EP - 6
BT - ICNSC 2018 - 15th IEEE International Conference on Networking, Sensing and Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018
Y2 - 27 March 2018 through 29 March 2018
ER -