A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence
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Semantic Technology Institute (STI) Innsbruck, Department of Computer Science, University of Innsbruck, 6020 Innsbruck, Austria
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Institute of Computer Science, University of Tartu, 50090 Tartu, Estonia
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School of Computer and Information Sciences, University of Hyderabad, Hyderabad 500046, India
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Wageningen Data Competence Center, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
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Consumption and Healthy Lifestyles Chair Group, Wageningen University & Research, 6706 KN Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Domenico Talia
Big Data Cogn. Comput. 2022, 6(1), 26; https://doi.org/10.3390/bdcc6010026
Received: 25 November 2021
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Revised: 14 February 2022
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Accepted: 24 February 2022
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Published: 1 March 2022
(This article belongs to the Special Issue Advanced Machine Learning and Data Mining: A New Frontier in Artificial Intelligence Research)
Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (self-monitoring, analysis, and reporting technology) hard drive metrics with other system metrics, such as central processing unit (CPU) utilisation. Therefore, we propose a combined system metrics approach for failure prediction based on artificial intelligence to improve reliability. We tested over 100 cloud servers’ data and four artificial intelligence algorithms: random forest, gradient boosting, long short-term memory, and gated recurrent unit, and also performed correlation analysis. Our correlation analysis sheds light on the relationships that exist between system metrics and failure, and the experimental results demonstrate the advantages of combining system metrics, outperforming the state-of-the-art.
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Keywords:
failure prediction; fault tolerance; cloud computing; artificial intelligence; reliability
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MDPI and ACS Style
Chhetri, T.R.; Dehury, C.K.; Lind, A.; Srirama, S.N.; Fensel, A. A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence. Big Data Cogn. Comput. 2022, 6, 26. https://doi.org/10.3390/bdcc6010026
AMA Style
Chhetri TR, Dehury CK, Lind A, Srirama SN, Fensel A. A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence. Big Data and Cognitive Computing. 2022; 6(1):26. https://doi.org/10.3390/bdcc6010026
Chicago/Turabian StyleChhetri, Tek R., Chinmaya K. Dehury, Artjom Lind, Satish N. Srirama, and Anna Fensel. 2022. "A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence" Big Data and Cognitive Computing 6, no. 1: 26. https://doi.org/10.3390/bdcc6010026
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