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G2LC: Resources Autoscaling for Real Time Bioinformatics Applications in IaaS

Cloud computing has started to change the way how bioinformatics research is being carried out. Researchers who have taken advantage of this technology can process larger amounts of data and speed up scientific discovery. The variability in data volume results in variable computing requirements. The...

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Detalles Bibliográficos
Autores principales: Hu, Rongdong, Liu, Guangming, Jiang, Jingfei, Wang, Lixin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4609414/
https://www.ncbi.nlm.nih.gov/pubmed/26504488
http://dx.doi.org/10.1155/2015/549026
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author Hu, Rongdong
Liu, Guangming
Jiang, Jingfei
Wang, Lixin
author_facet Hu, Rongdong
Liu, Guangming
Jiang, Jingfei
Wang, Lixin
author_sort Hu, Rongdong
collection PubMed
description Cloud computing has started to change the way how bioinformatics research is being carried out. Researchers who have taken advantage of this technology can process larger amounts of data and speed up scientific discovery. The variability in data volume results in variable computing requirements. Therefore, bioinformatics researchers are pursuing more reliable and efficient methods for conducting sequencing analyses. This paper proposes an automated resource provisioning method, G2LC, for bioinformatics applications in IaaS. It enables application to output the results in a real time manner. Its main purpose is to guarantee applications performance, while improving resource utilization. Real sequence searching data of BLAST is used to evaluate the effectiveness of G2LC. Experimental results show that G2LC guarantees the application performance, while resource is saved up to 20.14%.
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spelling pubmed-46094142015-10-26 G2LC: Resources Autoscaling for Real Time Bioinformatics Applications in IaaS Hu, Rongdong Liu, Guangming Jiang, Jingfei Wang, Lixin Comput Math Methods Med Research Article Cloud computing has started to change the way how bioinformatics research is being carried out. Researchers who have taken advantage of this technology can process larger amounts of data and speed up scientific discovery. The variability in data volume results in variable computing requirements. Therefore, bioinformatics researchers are pursuing more reliable and efficient methods for conducting sequencing analyses. This paper proposes an automated resource provisioning method, G2LC, for bioinformatics applications in IaaS. It enables application to output the results in a real time manner. Its main purpose is to guarantee applications performance, while improving resource utilization. Real sequence searching data of BLAST is used to evaluate the effectiveness of G2LC. Experimental results show that G2LC guarantees the application performance, while resource is saved up to 20.14%. Hindawi Publishing Corporation 2015 2015-10-04 /pmc/articles/PMC4609414/ /pubmed/26504488 http://dx.doi.org/10.1155/2015/549026 Text en Copyright © 2015 Rongdong Hu et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hu, Rongdong
Liu, Guangming
Jiang, Jingfei
Wang, Lixin
G2LC: Resources Autoscaling for Real Time Bioinformatics Applications in IaaS
title G2LC: Resources Autoscaling for Real Time Bioinformatics Applications in IaaS
title_full G2LC: Resources Autoscaling for Real Time Bioinformatics Applications in IaaS
title_fullStr G2LC: Resources Autoscaling for Real Time Bioinformatics Applications in IaaS
title_full_unstemmed G2LC: Resources Autoscaling for Real Time Bioinformatics Applications in IaaS
title_short G2LC: Resources Autoscaling for Real Time Bioinformatics Applications in IaaS
title_sort g2lc: resources autoscaling for real time bioinformatics applications in iaas
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4609414/
https://www.ncbi.nlm.nih.gov/pubmed/26504488
http://dx.doi.org/10.1155/2015/549026
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