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Gyeera, T.W., Simons, A.J.H. orcid.org/0000-0002-5925-7148 and Stannett, M.P. orcid.org/0000-0002-2794-8614 (2023) Kalman filter based prediction and forecasting of cloud server KPIs. IEEE Transactions on Services Computing, 16 (4). pp. 2742-2754. ISSN 1939-1374
Abstract
Cloud computing depends on the dynamic allocation and release of resources, on demand, to meet heterogeneous computing needs. This is challenging for cloud data centers, which process huge amounts of data characterised by its high volume, velocity, variety and veracity (4Vs model). Managing such a workload is increasingly difficult using state-of-the-art methods for monitoring and adaptation, which typically react to service failures after the fact. To address this, we seek to develop proactive methods for predicting future resource exhaustion and cloud service failures. Our work uses a realistic test bed in the cloud, which is instrumented to monitor and analyze resource usage. In this paper, we employed the optimal Kalman filtering technique to build a predictive and analytic framework for cloud server KPIs, based on historical data. Our k-step-ahead predictions on historical data yielded a prediction accuracy of 95.59%. The information generated from the framework can best be used for optimal resources provisioning, admission control and cloud SLA management.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Kalman filtering; machine learning; adaptive filters; virtual infrastructure network; cloud computing |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 31 Oct 2022 16:43 |
Last Modified: | 25 Sep 2024 15:29 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TSC.2022.3217148 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:192727 |
Available Versions of this Item
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Kalman filter based prediction and forecasting of cloud server KPIs. (deposited 03 Jul 2023 14:55)
- Kalman filter based prediction and forecasting of cloud server KPIs. (deposited 31 Oct 2022 16:43) [Currently Displayed]