Requests Prediction in Cloud with a Cyclic Window Learning Algorithm
dc.contributor.author | Yoon, Min Sang | |
dc.contributor.author | Kamal, Ahmed | |
dc.contributor.author | Zhu, Zhengyuan | |
dc.contributor.author | Zhu, Zhengyuan | |
dc.contributor.department | Statistics | |
dc.contributor.department | Center for Survey Statistics and Methodology (CSSM) | |
dc.date | 2020-06-14T00:33:33.000 | |
dc.date.accessioned | 2020-07-02T06:55:43Z | |
dc.date.available | 2020-07-02T06:55:43Z | |
dc.date.copyright | Fri Jan 01 00:00:00 UTC 2016 | |
dc.date.embargo | 2020-06-13 | |
dc.date.issued | 2016-01-01 | |
dc.description.abstract | <p>Automatic resource scaling is one advantage of cloud systems. Cloud systems are able to scale the number of physical machines depending on user requests. Therefore, accurate request prediction brings a great improvement in cloud systems' performance. If we can make accurate requests prediction, the appropriate number of physical machines that can accommodate predicted amount of requests can be activated and cloud systems will save more energy by preventing excessive activation of physical machines. Also, cloud systems can implement advanced load distribution with accurate requests prediction. We propose a prediction model that predicts probability distribution parameters of requests for each time interval. Maximum Likelihood Estimation (MLE) and Local Linear Regression (LLR) are used to implement this algorithm. An evaluation of the proposed algorithm is performed with the Google cluster-trace data. The prediction is achieved in terms of the number of task arrivals, CPU requests, and memory resource requests. Then the accuracy of prediction is measured with Mean Absolute Percentage Error(MAPE) and Normalized Mean Squared Error (NMSE).</p> | |
dc.description.comments | <p>This is a manuscript of a proceeding published as Yoon, Min Sang, Ahmed E. Kamal, and Zhengyuan Zhu. "Requests prediction in cloud with a cyclic window learning algorithm." In <em>2016 IEEE Globecom Workshops (GC Wkshps)</em>, (2016). DOI: <a href="https://doi.org/10.1109/GLOCOMW.2016.7849022" target="_blank">10.1109/GLOCOMW.2016.7849022</a>. Posted with permission.</p> | |
dc.format.mimetype | application/pdf | |
dc.identifier | archive/lib.dr.iastate.edu/stat_las_conf/11/ | |
dc.identifier.articleid | 1011 | |
dc.identifier.contextkey | 18089965 | |
dc.identifier.s3bucket | isulib-bepress-aws-west | |
dc.identifier.submissionpath | stat_las_conf/11 | |
dc.identifier.uri | https://dr.lib.iastate.edu/handle/20.500.12876/90244 | |
dc.language.iso | en | |
dc.source.bitstream | archive/lib.dr.iastate.edu/stat_las_conf/11/2016_ZhuZhengyuan_RequestsPrediction.pdf|||Fri Jan 14 18:39:32 UTC 2022 | |
dc.source.uri | 10.1109/GLOCOMW.2016.7849022 | |
dc.subject.disciplines | Statistics and Probability | |
dc.subject.keywords | Predictive models | |
dc.subject.keywords | Probability distribution | |
dc.subject.keywords | Data models | |
dc.subject.keywords | Maximum likelihood estimation | |
dc.subject.keywords | Histograms | |
dc.subject.keywords | Prediction algorithms | |
dc.subject.keywords | Linear regression | |
dc.title | Requests Prediction in Cloud with a Cyclic Window Learning Algorithm | |
dc.type | article | |
dc.type.genre | conference | |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | 51db2a08-8f9d-4f97-bdbc-6790b3d5a608 | |
relation.isOrgUnitOfPublication | 264904d9-9e66-4169-8e11-034e537ddbca |
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