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dc.contributor.authorSchneider, Christopher
dc.contributor.authorBarker, Adam David
dc.contributor.authorDobson, Simon Andrew
dc.identifier.citationSchneider , C , Barker , A D & Dobson , S A 2015 , ' A survey of self-healing systems frameworks ' , Software: Practice and Experience , vol. 45 , no. 10 , pp. 1375-1398 .
dc.identifier.otherPURE: 93684879
dc.identifier.otherPURE UUID: 2129008f-a60f-482a-b588-3a06c2b532a8
dc.identifier.otherScopus: 84941168239
dc.identifier.otherWOS: 000360814800004
dc.identifier.otherORCID: /0000-0001-9633-2103/work/70234166
dc.description.abstractRising complexity within multi-tier computing architectures remains an open problem. As complexity increases, so do the costs associated with operating and maintaining systems within these environments. One approach for addressing these problems is to build self-healing systems (i.e. frameworks) that can autonomously detect and recover from faulty states. Self-healing systems often combine machine learning techniques with closed control loops to reduce the number of situations requiring human intervention. This is particularly useful in situations where human involvement is both costly to develop, and a source of potential faults. Therefore, a survey of self-healing frameworks and methodologies in multi-tier architectures is provided to the reader. Uniquely, this study combines an overview of the state of the art with a comparative analysis of the computing environment, degree of behavioural autonomy, and organisational requirements of these approaches. Highlighting these aspects provides for an understanding of the different situational benefits of these self-healing systems. We conclude with a discussion of potential and current research directions within this field.
dc.relation.ispartofSoftware: Practice and Experienceen
dc.rights© 2014. John Wiley & Sons, Ltd.This is the accepted version of the following article: A survey of self-healing systems frameworks Schneider, C., Barker, A. D. & Dobson, S. A. 2014 In : Software: Practice and Experience. Early online, which has been published in final form at
dc.subjectAutonomic computingen
dc.subjectSelf-healing systemsen
dc.subjectArtificial intelligenceen
dc.subjectMachine learningen
dc.subjectEvolutionary programmingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleA survey of self-healing systems frameworksen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.description.statusPeer revieweden

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