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Management of container-based genetic algorithm workloads over cloud infrastructure

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Title of host publicationCF '20: Proceedings of the 17th ACM International Conference on Computing Frontiers
DateAccepted/In press - 29 Mar 2020
DatePublished (current) - 11 May 2020
Pages229-232
Number of pages4
PublisherACM
Original languageEnglish

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.

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© 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.

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