Gyeera, T.W. orcid.org/0000-0002-2567-4197, Simons, A.J.H. orcid.org/0000-0002-5925-7148 and Stannett, M. orcid.org/0000-0002-2794-8614 (2023) Regression analysis of predictions and forecasts of cloud data center KPIs using the boosted decision tree algorithm. IEEE Transactions on Big Data, 9 (4). pp. 1071-1085. ISSN 2332-7790
Abstract
Cloud data centers seek to optimize their provision of pooled CPU, bandwidth and storage resources. While over-provision is wasteful, under-provision may lead to violating Service Level Agreements (SLAs) with their consumers; yet the relationship between low-level Key Performance Indicators (KPIs) and SLA violations is not well understood. State-of-the art monitoring systems typically react to service failures after the fact, partly due to unexpected nonlinearities in the aggregated performance data. We seek to provide better modelling of KPIs using predictive algorithms that could be used for the proactive monitoring and adaptation of cloud services. In this paper, we investigate the Boosted Decision Tree (BDT) regression algorithm. We tested the BDT algorithm in a real monitoring framework deployed on a novel Azure cloud test-bed distributed over multiple geolocations, using thousands of robot-user requests to produce huge volumes of KPI data. The BDT algorithm achieved an R-Squared score of 0.9991 at the 0.2 learning rate. This closely predicted the KPI data and outperformed other approaches, such as Ordinary Least Squares and Stochastic Gradient Descent; and is a promising candidate for making short- and long-term predictions for cloud resource allocation.
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: | Boosted decision tree; machine learning; deep learning; algorithms; computational modelling; virtual infrastructure network and 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: | 18 May 2023 11:32 |
Last Modified: | 26 Sep 2024 13:50 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/tbdata.2022.3230649 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199293 |