University of Hertfordshire

By the same authors

Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. / Lane, Peter; Helian, Na; Bodla, Muhammad Haad; Zheng, Minghua; Moggridge, Paul.

Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. Springer, 2022. p. 301-316 (Part of the Lecture Notes in Computer Science book series; Vol. 13224).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Lane, P, Helian, N, Bodla, MH, Zheng, M & Moggridge, P 2022, Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. in Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. Part of the Lecture Notes in Computer Science book series, vol. 13224, Springer, 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

APA

Lane, P., Helian, N., Bodla, M. H., Zheng, M., & Moggridge, P. (2022). Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. In Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling (pp. 301-316). (Part of the Lecture Notes in Computer Science book series; Vol. 13224). Springer. https://doi.org/10.1007/978-3-031-02462-7_20

Vancouver

Lane P, Helian N, Bodla MH, Zheng M, Moggridge P. Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. In Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. Springer. 2022. p. 301-316. (Part of the Lecture Notes in Computer Science book series). https://doi.org/10.1007/978-3-031-02462-7_20

Author

Lane, Peter ; Helian, Na ; Bodla, Muhammad Haad ; Zheng, Minghua ; Moggridge, Paul. / Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling. Springer, 2022. pp. 301-316 (Part of the Lecture Notes in Computer Science book series).

Bibtex

@inproceedings{9b3d306d6aa5490f92c9130ef675e064,
title = "Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling",
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. ",
author = "Peter Lane and Na Helian and Bodla, {Muhammad Haad} and Minghua Zheng and Paul Moggridge",
note = "{\textcopyright} 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; International Conference on the Applications of Evolutionary Computation : Evolutionary Computation in Edge, Fog, and Cloud Computing ; Conference date: 20-04-2022 Through 22-04-2022",
year = "2022",
month = apr,
day = "15",
doi = "10.1007/978-3-031-02462-7_20",
language = "English",
isbn = "978-3-031-02461-0",
series = "Part of the Lecture Notes in Computer Science book series",
publisher = "Springer",
pages = "301--316",
booktitle = "Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling",
url = "http://www.evostar.org/2022/evoapps/",

}

RIS

TY - GEN

T1 - Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling

AU - Lane, Peter

AU - Helian, Na

AU - Bodla, Muhammad Haad

AU - Zheng, Minghua

AU - Moggridge, Paul

N1 - © 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

PY - 2022/4/15

Y1 - 2022/4/15

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.

AB - 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.

U2 - 10.1007/978-3-031-02462-7_20

DO - 10.1007/978-3-031-02462-7_20

M3 - Conference contribution

SN - 978-3-031-02461-0

T3 - Part of the Lecture Notes in Computer Science book series

SP - 301

EP - 316

BT - Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling

PB - Springer

T2 - International Conference on the Applications of Evolutionary Computation

Y2 - 20 April 2022 through 22 April 2022

ER -