Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2217
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dc.contributor.authorGhazi, Shaima’a-
dc.contributor.authorMeenakumari, J-
dc.date.accessioned2023-12-08T10:20:55Z-
dc.date.available2023-12-08T10:20:55Z-
dc.date.issued2017-
dc.identifier.citationVol. 12, No. 20; pp. 9285-9289en_US
dc.identifier.issn0973-4562-
dc.identifier.urihttps://www.ripublication.com/ijaer17/ijaerv12n20_09.pdf-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2217-
dc.description.abstractReliability of VMs has always been a challenge in a cloud environment. A fault tolerance (FT) framework that performs environmental monitoring, event logging, parallel job monitoring and resource monitoring to analyze the virtual machine reliability and to perform fault tolerance service are very much required to handle these challenges. As a part of fault tolerance mechanism there is a thorough necessity for providing preventive solutions to have continuity of services. Hence the proactive failure prediction of Virtual Machine (VMs) needs to be focused and also to be improved. It is mainly required to reduce the down time and cope up the scalability issues. This paper deals with one such predictive algorithm to enhance the efficiency.en_US
dc.language.isoenen_US
dc.publisherInternational Journal of Applied Engineering Researchen_US
dc.subjectCloud environmenten_US
dc.subjectVirtual machinesen_US
dc.subjectFault tolerance predictive algorithms and proactiveen_US
dc.titleVirtual Machine (VM) Earlier Failure Prediction Algorithmen_US
dc.typeArticleen_US
Appears in Collections:Journal Articles

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