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

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Original languageEnglish
Title of host publicationDynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling
PublisherSpringer
Pages301-316
Number of pages16
ISBN (Electronic)978-3-031-02462-7
ISBN (Print)978-3-031-02461-0
DOIs
Publication statusPublished - 15 Apr 2022
EventInternational Conference on the Applications of Evolutionary Computation: Evolutionary Computation in Edge, Fog, and Cloud Computing - , Spain
Duration: 20 Apr 202222 Apr 2022
http://www.evostar.org/2022/evoapps/

Publication series

NamePart of the Lecture Notes in Computer Science book series
Volume13224

Conference

ConferenceInternational Conference on the Applications of Evolutionary Computation
Country/TerritorySpain
Period20/04/2222/04/22
Internet address

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.

Notes

© 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

ID: 27243358