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AuthorBaig S.-U.-R.
AuthorIqbal W.
AuthorBerral J.L.
AuthorErradi A.
AuthorCarrera D.
Available date2020-04-01T06:54:49Z
Publication Date2019
Publication NameIEEE Transactions on Network and Service Management
ResourceScopus
ISSN19324537
URIhttp://dx.doi.org/10.1109/TNSM.2019.2932840
URIhttp://hdl.handle.net/10576/13649
AbstractAccurate estimation of data center resource utilization is a challenging task due to multi-tenant co-hosted applications having dynamic and time-varying workloads. Accurate estimation of future resources utilization helps in better job scheduling, workload placement, capacity planning, proactive auto-scaling, and load balancing. The inaccurate estimation leads to either under or over-provisioning of data center resources. Most existing estimation methods are based on a single model that often does not appropriately estimate different workload scenarios. To address these problems, we propose a novel method to adaptively and automatically identify the most appropriate model to accurately estimate data center resources utilization. The proposed approach trains a classifier based on statistical features of historical resources usage to decide the appropriate prediction model to use for given resource utilization observations collected during a specific time interval. We evaluated our approach on real datasets and compared the results with multiple baseline methods. The experimental evaluation shows that the proposed approach outperforms the state-of-the-art approaches and delivers 6% to 27% improved resource utilization estimation accuracy compared to baseline methods. - 2004-2012 IEEE.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectData center
data classification
dynamic prediction model
feature extraction
modeling and prediction
resource management
TitleAdaptive Prediction Models for Data Center Resources Utilization Estimation
TypeArticle
Pagination1681-1693
Issue Number4
Volume Number16
dc.accessType Abstract Only


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