Alrefai, Thamer and Soares Indrusiak, Leandro orcid.org/0000-0002-9938-2920 (2020) Management of container-based genetic algorithm workloads over cloud infrastructure. In: CF '20: Proceedings of the 17th ACM International Conference on Computing Frontiers. ACM , pp. 229-232.
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 ACM, Inc. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 05 Jun 2020 10:20 |
Last Modified: | 06 Feb 2025 00:04 |
Published Version: | https://doi.org/10.1145/3387902.3394031 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/3387902.3394031 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161610 |