Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling

Peter Lane; Na Helian; Muhammad Haad Bodla; Minghua Zheng; Paul Moggridge

doi:10.1007/978-3-031-02462-7_20

Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling

Peter Lane, Na Helian, Muhammad Haad Bodla, Minghua Zheng, Paul Moggridge

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

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Abstract

The performance of cloud computing depends in part on job-scheduling algorithms, but also on the connection structure. Previous work on this structure has mostly looked at fixed and static connections. However, we argue that such static structures cannot be optimal in all situations. We introduce a dynamic hierarchical connection system of sub-schedulers between the scheduler and servers, and use artificial intelligence search algorithms to optimise this structure. Due to its dynamic and flexible nature, this design enables the system to adaptively accommodate heterogeneous jobs and resources to make the most use of resources. Experimental results compare genetic algorithms and simulating annealing for optimising the structure, and demonstrate that a dynamic hierarchical structure can significantly reduce the total makespan (max processing time for given jobs) of the heterogeneous tasks allocated to heterogeneous resources, compared with a one-layer structure. This reduction is particularly pronounced when resources are scarce.

Original language	English
Title of host publication	Applications of Evolutionary Computation - 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Proceedings
Editors	Juan Luis Jiménez Laredo, J. Ignacio Hidalgo, Kehinde Oluwatoyin Babaagba
Publisher	Springer Nature
Pages	301-316
Number of pages	16
ISBN (Electronic)	978-3-031-02462-7
ISBN (Print)	978-3-031-02461-0
DOIs	https://doi.org/10.1007/978-3-031-02462-7_20
Publication status	Published - 15 Apr 2022
Event	International Conference on the Applications of Evolutionary Computation: Evolutionary Computation in Edge, Fog, and Cloud Computing - , Spain Duration: 20 Apr 2022 → 22 Apr 2022 http://www.evostar.org/2022/evoapps/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13224 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on the Applications of Evolutionary Computation
Country/Territory	Spain
Period	20/04/22 → 22/04/22
Internet address	http://www.evostar.org/2022/evoapps/

Keywords

Cloud computing
Dynamic hierarchical job scheduling structure
Genetic algorithms
Optimisation

Access to Document

10.1007/978-3-031-02462-7_20Licence: Unspecified

Accepted-Version
© 2022 Springer Nature Switzerland AG. This is the accepted manuscript version of a conference paper that been published in final form at https://doi.org/10.1007/978-3-031-02462-7_20
Accepted author manuscript, 779 KBLicence: Unspecified

Cite this

Lane, P., Helian, N., Bodla, M. H., Zheng, M., & Moggridge, P. (2022). Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. In J. L. Jiménez Laredo, J. I. Hidalgo, & K. O. Babaagba (Eds.), Applications of Evolutionary Computation - 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Proceedings (pp. 301-316). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13224 LNCS). Springer Nature . https://doi.org/10.1007/978-3-031-02462-7_20

Lane, Peter ; Helian, Na ; Bodla, Muhammad Haad et al. / Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. Applications of Evolutionary Computation - 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Proceedings. editor / Juan Luis Jiménez Laredo ; J. Ignacio Hidalgo ; Kehinde Oluwatoyin Babaagba. Springer Nature , 2022. pp. 301-316 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The performance of cloud computing depends in part on job-scheduling algorithms, but also on the connection structure. Previous work on this structure has mostly looked at fixed and static connections. However, we argue that such static structures cannot be optimal in all situations. We introduce a dynamic hierarchical connection system of sub-schedulers between the scheduler and servers, and use artificial intelligence search algorithms to optimise this structure. Due to its dynamic and flexible nature, this design enables the system to adaptively accommodate heterogeneous jobs and resources to make the most use of resources. Experimental results compare genetic algorithms and simulating annealing for optimising the structure, and demonstrate that a dynamic hierarchical structure can significantly reduce the total makespan (max processing time for given jobs) of the heterogeneous tasks allocated to heterogeneous resources, compared with a one-layer structure. This reduction is particularly pronounced when resources are scarce. ",

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Lane, P , Helian, N, Bodla, MH, Zheng, M & Moggridge, P 2022, Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. in JL Jiménez Laredo, JI Hidalgo & KO Babaagba (eds), Applications of Evolutionary Computation - 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13224 LNCS, Springer Nature , pp. 301-316, International Conference on the Applications of Evolutionary Computation, Spain, 20/04/22. https://doi.org/10.1007/978-3-031-02462-7_20

Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. / Lane, Peter ; Helian, Na; Bodla, Muhammad Haad et al.
Applications of Evolutionary Computation - 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Proceedings. ed. / Juan Luis Jiménez Laredo; J. Ignacio Hidalgo; Kehinde Oluwatoyin Babaagba. Springer Nature , 2022. p. 301-316 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13224 LNCS).

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

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AU - Helian, Na

AU - Bodla, Muhammad Haad

AU - Zheng, Minghua

AU - Moggridge, Paul

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N2 - The performance of cloud computing depends in part on job-scheduling algorithms, but also on the connection structure. Previous work on this structure has mostly looked at fixed and static connections. However, we argue that such static structures cannot be optimal in all situations. We introduce a dynamic hierarchical connection system of sub-schedulers between the scheduler and servers, and use artificial intelligence search algorithms to optimise this structure. Due to its dynamic and flexible nature, this design enables the system to adaptively accommodate heterogeneous jobs and resources to make the most use of resources. Experimental results compare genetic algorithms and simulating annealing for optimising the structure, and demonstrate that a dynamic hierarchical structure can significantly reduce the total makespan (max processing time for given jobs) of the heterogeneous tasks allocated to heterogeneous resources, compared with a one-layer structure. This reduction is particularly pronounced when resources are scarce.

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Lane P , Helian N, Bodla MH, Zheng M, Moggridge P. Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. In Jiménez Laredo JL, Hidalgo JI, Babaagba KO, editors, Applications of Evolutionary Computation - 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Proceedings. Springer Nature . 2022. p. 301-316. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-02462-7_20

Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling

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