A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/105903 |
Resumo: | Abdelaziz, A., Anastasiadou, M., & Castelli, M. (2020). A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment. Applied Sciences (Switzerland), 10(18), 1-25. [2806]. https://doi.org/10.3390/APP10186538 |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environmentCloud computingGenetic algorithmHealthcare servicesParallel particle swarm optimisationMaterials Science(all)InstrumentationEngineering(all)Process Chemistry and TechnologyComputer Science ApplicationsFluid Flow and Transfer ProcessesAbdelaziz, A., Anastasiadou, M., & Castelli, M. (2020). A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment. Applied Sciences (Switzerland), 10(18), 1-25. [2806]. https://doi.org/10.3390/APP10186538Cloud computing has a significant role in healthcare services, especially in medical applications. In cloud computing, the best choice of virtual machines (Virtual_Ms) has an essential role in the quality improvement of cloud computing by minimising the execution time of medical queries from stakeholders and maximising utilisation of medicinal resources. Besides, the best choice of Virtual_Ms assists the stakeholders to reduce the total execution time of medical requests through turnaround time and maximise CPU utilisation and waiting time. For that, this paper introduces an optimisation model for medical applications using two distinct intelligent algorithms: genetic algorithm (GA) and parallel particle swarm optimisation (PPSO). In addition, a set of experiments was conducted to provide a competitive study between those two algorithms regarding the execution time, the data processing speed, and the system efficiency. The PPSO algorithm was implemented using the MATLAB tool. The results showed that the PPSO algorithm gives accurate outcomes better than the GA in terms of the execution time of medical queries and efficiency by 3.02% and 37.7%, respectively. Also, the PPSO algorithm has been implemented on the CloudSim package. The results displayed that the PPSO algorithm gives accurate outcomes better than default CloudSim in terms of final implementation time of medicinal queries by 33.3%. Finally, the proposed model outperformed the state-of-the-art methods in the literature review by a range from 13% to 67%.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAbdelaziz, AhmedAnastasiadou, MariaCastelli, Mauro2020-10-19T23:26:33Z2020-09-182020-09-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article25application/pdfhttp://hdl.handle.net/10362/105903eng2076-3417PURE: 25993389https://doi.org/10.3390/APP10186538info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:51:05Zoai:run.unl.pt:10362/105903Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:40:37.011815Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment |
title |
A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment |
spellingShingle |
A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment Abdelaziz, Ahmed Cloud computing Genetic algorithm Healthcare services Parallel particle swarm optimisation Materials Science(all) Instrumentation Engineering(all) Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
title_short |
A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment |
title_full |
A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment |
title_fullStr |
A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment |
title_full_unstemmed |
A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment |
title_sort |
A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment |
author |
Abdelaziz, Ahmed |
author_facet |
Abdelaziz, Ahmed Anastasiadou, Maria Castelli, Mauro |
author_role |
author |
author2 |
Anastasiadou, Maria Castelli, Mauro |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School RUN |
dc.contributor.author.fl_str_mv |
Abdelaziz, Ahmed Anastasiadou, Maria Castelli, Mauro |
dc.subject.por.fl_str_mv |
Cloud computing Genetic algorithm Healthcare services Parallel particle swarm optimisation Materials Science(all) Instrumentation Engineering(all) Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
topic |
Cloud computing Genetic algorithm Healthcare services Parallel particle swarm optimisation Materials Science(all) Instrumentation Engineering(all) Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
description |
Abdelaziz, A., Anastasiadou, M., & Castelli, M. (2020). A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment. Applied Sciences (Switzerland), 10(18), 1-25. [2806]. https://doi.org/10.3390/APP10186538 |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-10-19T23:26:33Z 2020-09-18 2020-09-18T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/105903 |
url |
http://hdl.handle.net/10362/105903 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2076-3417 PURE: 25993389 https://doi.org/10.3390/APP10186538 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
25 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138020509614080 |