A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

dc.contributor.author Chhetri, Tek Raj
dc.contributor.author Dehury, Chinmaya Kumar
dc.contributor.author Lind, Artjom
dc.contributor.author Srirama, Satish Narayana
dc.contributor.author Fensel, Anna
dc.date.accessioned 2022-03-27T06:03:09Z
dc.date.available 2022-03-27T06:03:09Z
dc.date.issued 2022-03-01
dc.description.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 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.
dc.identifier.citation Big Data and Cognitive Computing. v.6(1)
dc.identifier.uri 10.3390/bdcc6010026
dc.identifier.uri https://www.mdpi.com/2504-2289/6/1/26
dc.identifier.uri https://dspace.uohyd.ac.in/handle/1/9209
dc.subject Artificial intelligence
dc.subject Cloud computing
dc.subject Failure prediction
dc.subject Fault tolerance
dc.subject Reliability
dc.title A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence
dc.type Journal. Article
dspace.entity.type
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