Management of container-based genetic algorithm workloads over cloud infrastructure

Thamer Alrefai; Leandro Soares Indrusiak

doi:10.1145/3387902.3394031

Management of container-based genetic algorithm workloads over cloud infrastructure

Thamer Alrefai, Leandro Soares Indrusiak

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	CF '20: Proceedings of the 17th ACM International Conference on Computing Frontiers
Publisher	ACM
Pages	229-232
Number of pages	4
DOIs	https://doi.org/10.1145/3387902.3394031
Publication status	Published - 11 May 2020

Bibliographical note

© 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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10.1145/3387902.3394031

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Cite this

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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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