Decision variable contribution based adaptive mechanism for evolutionary multi-objective cloud workflow scheduling

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dc.citation.issue6
dc.citation.volume9
dc.contributor.authorLi J
dc.contributor.authorXing L
dc.contributor.authorZhong W
dc.contributor.authorCai Z
dc.contributor.authorHou F
dc.date.accessioned2025-03-02T22:56:27Z
dc.date.available2025-03-02T22:56:27Z
dc.date.issued2023-06-29
dc.description.abstractWorkflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems.
dc.description.confidentialfalse
dc.edition.editionDecember 2023
dc.format.pagination7337-7348
dc.identifier.citationLi J, Xing L, Zhong W, Cai Z, Hou F. (2023). Decision variable contribution based adaptive mechanism for evolutionary multi-objective cloud workflow scheduling. Complex and Intelligent Systems. 9. 6. (pp. 7337-7348).
dc.identifier.doi10.1007/s40747-023-01137-w
dc.identifier.eissn2198-6053
dc.identifier.elements-typejournal-article
dc.identifier.issn2199-4536
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72551
dc.languageEnglish
dc.publisherSpringer Nature
dc.publisher.urihttp://link.springer.com/article/10.1007/s40747-023-01137-w
dc.relation.isPartOfComplex and Intelligent Systems
dc.rights(c) 2023 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCloud computing
dc.subjectWorkflow scheduling
dc.subjectMulti-objective
dc.subjectEvolutionary optimization
dc.subjectLarge-scale
dc.titleDecision variable contribution based adaptive mechanism for evolutionary multi-objective cloud workflow scheduling
dc.typeJournal article
pubs.elements-id478950
pubs.organisational-groupOther
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