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.
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.
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.
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.
Abstract
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 CPU utilisation. Therefore, we propose a combined metrics approach for failure prediction based on Artificial Intelligence to improve reliability. We tested over 100 cloud servers’ data and four AI algorithms: Random Forest, Gradient Boosting, Long-Short-Term Memory, and Gated Recurrent Unit. Our experimental result shows the benefits of combining metrics, outperforming state-of-the-art.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
