An Online Fault Detection Model and Strategies Based on SVM-Grid in Clouds

PeiYun Zhang; Sheng Shu; MengChu Zhou

doi:10.1109/JAS.2017.7510817

Volume 5 Issue 2

Mar. 2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2018 > 5(2): 445-456

PeiYun Zhang, Sheng Shu and MengChu Zhou, "An Online Fault Detection Model and Strategies Based on SVM-Grid in Clouds," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 445-456, Feb. 2018. doi: 10.1109/JAS.2017.7510817

Citation:

PeiYun Zhang, Sheng Shu and MengChu Zhou, "An Online Fault Detection Model and Strategies Based on SVM-Grid in Clouds," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 445-456, Feb. 2018. doi: 10.1109/JAS.2017.7510817

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An Online Fault Detection Model and Strategies Based on SVM-Grid in Clouds

doi: 10.1109/JAS.2017.7510817

PeiYun Zhang^{1
,
,},
Sheng Shu^1
,,
MengChu Zhou^{2, 3
,
,}

1.
School of Mathematics and Computer Science, Anhui Normal University, Wuhu 241003, China
2.
Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
3.
Renewable Energy Research Group, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

Funds:

the National Natural Science Foundation of China 61472005

the National Natural Science Foundation of China 61201252

CERNET Innovation Project NGII20160207

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Author Bio:
Peiyun Zhang (M'16) received her B.S. degree from Anhui Normal University, China in 1998, M.S. degree from Northwest University, China in 2005, and Ph.D. degree from the School of Computer Science and Technology, Nanjing University of Science and Technology, China in 2008. She did post-doctoral research in University of Science & Technology China, from 2010 to 2013, and a Visiting Scholar with the New Jersey Institute of Technology, USA in 2016. She is currently a Professor with the School of Mathematics and Computer Science, Anhui Normal University, China. Her research interests include cloud computing, big data, trust computing, Petri nets, web service and intelligent information processing. She has published over 50 papers in the above areas. (e-mail: zpyanu@ahnu.edu.cn)

Sheng Shu received his B.S. degree from Hefei University, China in 2015. He is pursuing his master degree at Anhui Normal University, China since 2015 and is expected to graduate in July 2018. His research interests include cloud computing and intelligent information processing. (e-mail: mrshusheng@163.com)

MengChu Zhou (S'88-M'90-SM'93-F'03) received his B.S. degree in Control Engineering from Nanjing University of Science and Technology, China in 1983, M.S. degree in Automatic Control from Beijing Institute of Technology, 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. His research interests are in Petri nets, intelligent automation, Internet of Things, big data, web services, and intelligent transportation. He has over 700 publications including 12 books, 400+ journal papers (300+ in IEEE transactions), 11 patents and 28 book-chapters. 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 was General Chair of IEEE Conf. on Automation Science and Engineering, Washington D.C., August 23-26, 2008, General Co-Chair of 2003 IEEE International Conference on System, Man and Cybernetics (SMC), Washington DC, October 5-8, 2003, Founding General Co-Chair of 2004 IEEE Int. Conf. on Networking, Sensing and Control, Taipei, March 21-23, 2004, and General Chair of 2006 IEEE Int. Conf. on Networking, Sensing and Control, Ft. Lauderdale, Florida, U.S.A. April 23-25, 2006. He was Program Chair of 2010 IEEE International Conference on Mechatronics and Automation, August 4-7, 2010, Xi'an, China, 1998 and 2001 IEEE International Conference on SMC and 1997 IEEE International Conference on Emerging Technologies and Factory Automation. He organized and chaired over 100 technical sessions and served on program committees for many conferences. Dr. Zhou has led or participated in over 50 research and education projects with total budget over M, funded by National Science Foundation, Department of Defense, NIST, New Jersey Science and Technology Commission, and industry. He was the recipient of NSF's Research Initiation Award, CIM University-LEAD Award from Society of Manufacturing Engineers, Perlis Research Award and Fenster Innovation in Engineering Education Award from NJIT, Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, Leadership Award and Academic Achievement Award from Chinese Association for Science and TechnologyUSA, Asian American Achievement Award from Asian American Heritage Council of New Jersey, and Outstanding Contributions Award, Distinguished Lecturership, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE SMC Society, and Distinguished Service Award from IEEE Robotics and Automation Society. He is founding Co-chair of Enterprise Information Systems Technical Committee (TC) and Environmental Sensing, Networking, and Decision-making TC of IEEE SMC Society. He has been among most highly cited scholars for years and ranked top one in the field of engineering worldwide in 2012 by Web of Science/Thomson Reuters and now Clarivate Analytics. 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). (e-mail: zhou@njit.edu)
Corresponding author: Peiyun Zhang, e-mail: mrshusheng@163.com; MengChu Zhou, e-mail: zhou@njit.edu
Received Date: 2017-07-24
Accepted Date: 2017-11-10

Abstract

Abstract

Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potentially help cloud managers take some timely measures before fault occurrence in clouds. Because of the complex structure and dynamic change characteristics of the clouds, existing fault detection methods suffer from the problems of low efficiency and low accuracy. In order to solve them, this work proposes an online detection model based on asystematic parameter-search method called SVM-Grid, whose construction is based on a support vector machine (SVM). SVM-Grid is used to optimize parameters in SVM. Proper attributes of a cloud system's running data are selected by using Pearson correlation and principal component analysis for the model. Strategies of predicting cloud faults and updating fault sample databases are proposed to optimize the model and improve its performance. In comparison with some representative existing methods, the proposed model can achieve more efficient and accurate fault detection for cloud systems.
- Cloud computing,
- fault detection,
- support vector machine (SVM),
- grid

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References(49)

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An Online Fault Detection Model and Strategies Based on SVM-Grid in Clouds

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