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        Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling

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        scheduling_evoapp22_final.pdf (PDF, 779Kb)(embargoed until 15/04/2024)
        Author
        Lane, Peter
        Helian, Na
        Bodla, Muhammad Haad
        Zheng, Minghua
        Moggridge, Paul
        Attention
        2299/25501
        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.
        Publication date
        2022-04-15
        Published in
        Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling
        Published version
        https://doi.org/10.1007/978-3-031-02462-7_20
        Other links
        http://hdl.handle.net/2299/25501
        Relations
        School of Physics, Engineering & Computer Science
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