Aloufi, OF, Djemame, K orcid.org/0000-0001-5811-5263, Saeed, F et al. (1 more author) (2021) A survey on predicting workloads and optimizing QoS in the cloud computing. In: Proceedings of the 2021 International Congress of Advanced Technology and Engineering, ICOTEN 2021. 2021 International Congress of Advanced Technology and Engineering, 04-05 Jul 2021, Taiz, Yemen. IEEE , pp. 1-7. ISBN 978-1-6654-2966-5
Abstract
computing delivers quality of service (QoS) to the end-user. Next, it discusses how to schedule one’s workload in the infrastructure using technologies that have recently emerged such as Machine Learning (ML). That is followed by an overview of how ML can be used for resource management. Then, this paper aims to outline the benefits of using ML to schedule upcoming demands to achieve QoS and conserve energy. In addition, we reviewed the research related to ML methods for predicting workloads in cloud computing. It also provides information on the approaches to elasticity, while another section discusses the methods of prediction used in previous studies.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Sep 2021 13:30 |
Last Modified: | 21 Sep 2021 13:30 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICOTEN52080.2021.9493436 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178186 |