Djemame, K orcid.org/0000-0001-5811-5263 and Aljulayfi, A orcid.org/0000-0002-2262-2340 (2021) A Machine Learning based Context-aware Prediction Framework for Edge Computing Environments. In: Proceedings of the 12th International Conference on Cloud Computing and Services Science. 12th International Conference on Cloud Computing and Services Science, 28-30 Apr 2021, Online. , pp. 143-150. ISBN 978-989-758-510-4
Abstract
A Context-aware Prediction Framework (CAPF) can be provided through a Self-adaptive System (SAS) resource manager to support the autoscaling decision in Edge Computing (EC) environments. However, EC dynamicity and workload fluctuation represent the main challenges to design a robust prediction framework. Machine Learning (ML) algorithms show a promising accuracy in workload forecasting problems which may vary according to the workload pattern. Therefore, the accuracy of such algorithms needs to be evaluated and compared in order to select the most suitable algorithm for EC workload prediction. In this paper, a thorough comparison is conducted focusing on the most popular ML algorithms which are Linear Regression (LR), Support Vector Regression (SVR), and Neural Networks (NN) using real EC dataset. The experimental results show that a robust prediction framework can be supported by more than one algorithm considering the EC contextual behavior. The results also reveal that the NN outpe rforms LR and SVR in most cases.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of a conference paper, published in final form in the Proceedings of the 12th International Conference on Cloud Computing and Services Science under a CC-BY-NC-ND license, https://doi.org/10.5220/0010379001430150. This version is made available with the permission of the publisher. |
Keywords: | Edge Computing; Self-adaptive Systems; Machine Learning; Prediction Framework; Linear Regression; Support Vector Regression; Neural Networks; Sliding Window. |
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: | 30 Apr 2021 15:21 |
Last Modified: | 16 Feb 2024 10:14 |
Published Version: | https://www.scitepress.org/PublicationsDetail.aspx... |
Status: | Published |
Identification Number: | 10.5220/0010379001430150 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173188 |