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dc.contributor.authorMohammed, Bashir*
dc.contributor.authorModu, Babagana*
dc.contributor.authorMaiyama, Kabiru M.*
dc.contributor.authorUgail, Hassan*
dc.contributor.authorAwan, Irfan U.*
dc.contributor.authorKiran, M.*
dc.date.accessioned2019-01-10T16:36:25Z
dc.date.available2019-01-10T16:36:25Z
dc.date.issued2018
dc.identifier.citationMohammed B, Modu B, Maiyama KM, Ugail H, Awan I and Kiran, M (2018) Failure Analysis Modelling in an Infrastructure as a Service (Iaas) Environment. Electronic Notes in Theoretical Computer Science. 340(29): 41-54.en_US
dc.identifier.urihttp://hdl.handle.net/10454/16743
dc.descriptionyesen_US
dc.description.abstractFailure Prediction has long known to be a challenging problem. With the evolving trend of technology and growing complexity of high-performance cloud data centre infrastructure, focusing on failure becomes very vital particularly when designing systems for the next generation. The traditional runtime fault-tolerance (FT) techniques such as data replication and periodic check-pointing are not very effective to handle the current state of the art emerging computing systems. This has necessitated the urgent need for a robust system with an in-depth understanding of system and component failures as well as the ability to predict accurate potential future system failures. In this paper, we studied data in-production-faults recorded within a five years period from the National Energy Research Scientific computing centre (NERSC). Using the data collected from the Computer Failure Data Repository (CFDR), we developed an effective failure prediction model focusing on high-performance cloud data centre infrastructure. Using the Auto-Regressive Moving Average (ARMA), our model was able to predict potential future failures in the system. Our results also show a failure prediction accuracy of 95%, which is good.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1016/j.entcs.2018.09.004en_US
dc.rights© 2018 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.subjectFailure predictionen_US
dc.subjectInfrastructure as a Service (Iaas)en_US
dc.subjectReplicationen_US
dc.subjectHPCen_US
dc.subjectCheckpointingen_US
dc.subjectCloud data centre infrastructureen_US
dc.subjectCloud system failureen_US
dc.titleFailure Analysis Modelling in an Infrastructure as a Service (Iaas) Environmenten_US
dc.status.refereedyesen_US
dc.date.application2018-10-30
dc.typeArticleen_US
dc.type.versionpublished version paperen_US
refterms.dateFOA2019-01-10T16:36:25Z


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