@article{8f61a5d8907b4124bfb7c52715be4075,
title = "Integrated deep learning method for workload and resource prediction in cloud systems",
abstract = "Cloud computing providers face several challenges in precisely forecasting large-scale workload and resource time series. Such prediction can help them to achieve intelligent resource allocation for guaranteeing that users{\textquoteright} performance needs are strictly met with no waste of computing, network and storage resources. This work applies a logarithmic operation to reduce the standard deviation before smoothing workload and resource sequences. Then, noise interference and extreme points are removed via a powerful filter. A Min–Max scaler is adopted to standardize the data. An integrated method of deep learning for prediction of time series is designed. It incorporates network models including both bi-directional and grid long short-term memory network to achieve high-quality prediction of workload and resource time series. The experimental comparison demonstrates that the prediction accuracy of the proposed method is better than several widely adopted approaches by using datasets of Google cluster trace.",
keywords = "BG-LSTM, Cloud data centers, Deep learning, Hybrid prediction, Savitzky–Golay filter",
author = "Jing Bi and Shuang Li and Haitao Yuan and Zhou, {Meng Chu}",
note = "Funding Information: MengChu Zhou (S{\textquoteright}88-M{\textquoteright}90-SM{\textquoteright}93-F{\textquoteright}03) received his B.S. degree in Control Engineering from Nanjing University of Science and Technology, Nanjing, China in 1983, M.S. degree in Automatic Control from Beijing Institute of Technology, Beijing, China in 1986, and Ph. D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, NY in 1990. He joined New Jersey Institute of Technology (NJIT), Newark, NJ in 1990, and is now a Distinguished Professor of Electrical and Computer Engineering. He has over 900 publications including 12 books, 600+ journal papers (450+ in IEEE transactions), 27 patents and 29 book-chapters. His research interests are in Petri nets, intelligent automation, Internet of Things, big data, web services, and intelligent transportation. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering and Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica. He is presently Associate Editor of IEEE Transactions on Intelligent Transportation Systems, IEEE Internet of Things Journal, and IEEE Transactions on Systems, Man, and Cybernetics: Systems. He is a recipient of Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man and Cybernetics Society, Excellence in Research Prize and Medal from New Jersey Institute of Technology, and Edison Patent Award from the Research & Development Council of New Jersey. He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is a Fellow of International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS) and Chinese Association of Automation (CAA). Funding Information: This work was supported in part by the Major Science and Technology Program for Water Pollution Control and Treatment of China under Grant 2018ZX07111005, in part by the National Natural Science Foundation of China (NSFC) under Grants 62073005 and 61802015, in part by the National Defense Pre-Research Foundation of China under Grants 41401020401 and 41401050102, and in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia, under Grant No. RG-48-135-40. Publisher Copyright: {\textcopyright} 2020 Elsevier B.V.",
year = "2021",
month = feb,
day = "1",
doi = "10.1016/j.neucom.2020.11.011",
language = "English (US)",
volume = "424",
pages = "35--48",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",
}