Alrefai, T. and Indrusiak, L.S. (2020) Management of container-based genetic algorithm workloads over cloud infrastructure. In: CF '20: Proceedings of the 17th ACM International Conference on Computing Frontiers. 17th ACM International Conference on Computing Frontiers, 11-13 May 2020, Sicily, Catania, Italy. Association for Computing Machinery , pp. 229-232. ISBN 9781450379564
Abstract
This paper proposes two approaches to managing the workload of multiple instances of genetic algorithms (GAs) running as containers over a cloud environment. The aim of both approaches is to obtain, for as many instances as possible, a GA output which achieves a user-defined fitness level by a user-defined deadline. To reach such a goal, the proposed approaches allocate the GA containers to cloud nodes and carefully control the execution of every GA instance by forcing them to run in stages. The paper proposes two approaches, fitness tracking (FT) and fitness prediction (FP), with both approaches compared against state-of-the-art container-based orchestration approaches.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Copyright held by the owner/author(s). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CF '20: Proceedings of the 17th ACM International Conference on Computing Frontiers, https://doi.org/10.1145/3387902.3394031 |
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) > Distributed Systems & Services |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Jul 2024 11:12 |
Last Modified: | 21 Jan 2025 14:17 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Identification Number: | 10.1145/3387902.3394031 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214577 |