Renumber coevolutionary multiswarm particle swarm optimization for multi-objective workflow scheduling on cloud computing environment
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Li, Hai-Hao | - |
dc.contributor.author | Chen, Zong-Gan | - |
dc.contributor.author | Zhan, Zhi-Hui | - |
dc.contributor.author | Du, Ke-Jing | - |
dc.contributor.author | Zhang, Jun | - |
dc.date.accessioned | 2023-11-24T02:33:34Z | - |
dc.date.available | 2023-11-24T02:33:34Z | - |
dc.date.issued | 2015-07 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115704 | - |
dc.description.abstract | Resources scheduling is a significant research topic in cloud computing, which is often modeled as a cost-minimization and deadline-constrained workflow scheduling model. This is a constrained single objective problem that to minimize the overall workflow execution cost while meeting deadline constraints. In this paper, we offer a new horizon to convert this single-objective problem to a multi-objective problem and present coevolutionary multiswarm particle swarm optimization (CMPSO) to find the non-dominated solutions with different execute cost and time. Meanwhile, the renumber strategy is adopted in CMPSO to make the learning efficient. CMPSO is compared with a renumber PSO (RNPSO) by setting the execute time in the CMPSO's nondominated solutions as the deadline constraint of RNPSO. Results show that CMPSO not only offers many non-dominated solutions with different prices and execute time, but also obtains better solution than RNPSO under a same deadline. Copyright is held by the owner/author(s). | - |
dc.format.extent | 2 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | Renumber coevolutionary multiswarm particle swarm optimization for multi-objective workflow scheduling on cloud computing environment | - |
dc.type | Article | - |
dc.publisher.location | 네델란드 | - |
dc.identifier.doi | 10.1145/2739482.2764632 | - |
dc.identifier.scopusid | 2-s2.0-84959323159 | - |
dc.identifier.bibliographicCitation | GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp 1419 - 1420 | - |
dc.citation.title | GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation | - |
dc.citation.startPage | 1419 | - |
dc.citation.endPage | 1420 | - |
dc.type.docType | Conference paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Cloud computing | - |
dc.subject.keywordAuthor | Guiding point | - |
dc.subject.keywordAuthor | Particle swarm optimization | - |
dc.subject.keywordAuthor | Renumber | - |
dc.subject.keywordAuthor | Scheduling | - |
dc.identifier.url | https://dl.acm.org/doi/abs/10.1145/2739482.2764632? | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.