A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment

Detalhes bibliográficos
Autor(a) principal: Abdelaziz, Ahmed
Data de Publicação: 2020
Outros Autores: Anastasiadou, Maria, Castelli, Mauro
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
id RCAP_42503e95d61c9982179dc8388ca38099
oai_identifier_str oai:run.unl.pt:10362/105903
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling 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
repository.mail.fl_str_mv
_version_ 1799138020509614080

Registros relacionados