Show simple item record

dc.contributor.authorBashir, Mohammed*
dc.contributor.authorAwan, Irfan U.*
dc.contributor.authorUgail, Hassan*
dc.contributor.authorMuhammad, Y.*
dc.date.accessioned2019-03-15T09:55:36Z
dc.date.available2019-03-15T09:55:36Z
dc.date.issued2019-06
dc.identifier.citationBashir M, Awan IU, Ugail H et al (2019) Failure Prediction using Machine Learning in a Virtualised HPC System and application. Cluster Computing. 22(2): 471-485.en_US
dc.identifier.urihttp://hdl.handle.net/10454/16892
dc.descriptionYesen_US
dc.description.abstractFailure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular check-pointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the Support Vector Machine (SVM), Random Forest(RF), k-Nearest Neighbors (KNN), Classi cation and Regression Trees (CART) and Linear Discriminant Analysis (LDA). Experimental results indicates that the average prediction accuracy of our model using SVM when predicting failure is 90% accurate and effective compared to other algorithms. This f inding implies that our method can effectively predict all possible future system and application failures within the system.en_US
dc.description.sponsorshipPetroleum Technology Development Fund (PTDF) funding support under the OSS scheme with grant number (PTDF/E/OSS/PHD/MB/651/14)en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1007/s10586-019-02917-1en_US
dc.rights© 2019 Springer. Reproduced in accordance with the publisher's self-archiving policy. The final publication is available at Springer via https://doi.org/10.1007/s10586-019-02917-1
dc.subjectFailureen_US
dc.subjectMachine learningen_US
dc.subjectHigh performance computingen_US
dc.subjectCloud computingen_US
dc.titleFailure Prediction using Machine Learning in a Virtualised HPC System and applicationen_US
dc.status.refereedYesen_US
dc.date.Accepted2019-02-09
dc.date.application2019-03-21
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
refterms.dateFOA2020-03-23T13:30:07Z


Item file(s)

Thumbnail
Name:
Awan_IU_Failure_prediction_(20 ...
Size:
925.8Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record