Towards operational cost minimization for cloud bursting with deadline constraints in hybrid clouds
Abstract In hybrid clouds, there is a technique named cloud bursting which can allow companies to expand their capacity to meet the demands of peak workloads in a low-priced manner. In this work, a cost-aware job scheduling approach based on queueing theory in hybrid clouds is proposed. The job sche...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Chunlin [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Cluster computing - Springer US, 1998, 21(2018), 4 vom: 25. Aug., Seite 2013-2029 |
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| Übergeordnetes Werk: |
volume:21 ; year:2018 ; number:4 ; day:25 ; month:08 ; pages:2013-2029 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10586-018-2841-4 |
|---|
| Katalog-ID: |
OLC2066391026 |
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| 520 | |a Abstract In hybrid clouds, there is a technique named cloud bursting which can allow companies to expand their capacity to meet the demands of peak workloads in a low-priced manner. In this work, a cost-aware job scheduling approach based on queueing theory in hybrid clouds is proposed. The job scheduling problem in the private cloud is modeled as a queueing model. A genetic algorithm is applied to achieve optimal queues for jobs to improve the utilization rate of the private cloud. Then, the task execution time is predicted by back propagation neural network. The max–min strategy is applied to schedule tasks according to the prediction results in hybrid clouds. Experiments show that our cost-aware job scheduling algorithm can reduce the average job waiting time and average job response time in the private cloud. In additional, our proposed job scheduling algorithm can improve the system throughput of the private cloud. It also can reduce the average task waiting time, average task response time and total costs in hybrid clouds. | ||
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| 700 | 1 | |a Luo, Youlong |4 aut | |
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10.1007/s10586-018-2841-4 doi (DE-627)OLC2066391026 (DE-He213)s10586-018-2841-4-p DE-627 ger DE-627 rakwb eng 004 VZ 54.50$jProgrammierung: Allgemeines bkl 54.32$jRechnerkommunikation bkl 54.25$jParallele Datenverarbeitung bkl Li, Chunlin verfasserin aut Towards operational cost minimization for cloud bursting with deadline constraints in hybrid clouds 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In hybrid clouds, there is a technique named cloud bursting which can allow companies to expand their capacity to meet the demands of peak workloads in a low-priced manner. In this work, a cost-aware job scheduling approach based on queueing theory in hybrid clouds is proposed. The job scheduling problem in the private cloud is modeled as a queueing model. A genetic algorithm is applied to achieve optimal queues for jobs to improve the utilization rate of the private cloud. Then, the task execution time is predicted by back propagation neural network. The max–min strategy is applied to schedule tasks according to the prediction results in hybrid clouds. Experiments show that our cost-aware job scheduling algorithm can reduce the average job waiting time and average job response time in the private cloud. In additional, our proposed job scheduling algorithm can improve the system throughput of the private cloud. It also can reduce the average task waiting time, average task response time and total costs in hybrid clouds. Hybrid clouds Cloud bursting Load balancing Genetic algorithm BP neural network Tang, Jianhang aut Luo, Youlong aut Enthalten in Cluster computing Springer US, 1998 21(2018), 4 vom: 25. Aug., Seite 2013-2029 (DE-627)265187907 (DE-600)1465290-0 (DE-576)9265187905 1386-7857 nnns volume:21 year:2018 number:4 day:25 month:08 pages:2013-2029 https://doi.org/10.1007/s10586-018-2841-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 54.50$jProgrammierung: Allgemeines VZ 181569876 (DE-625)181569876 54.32$jRechnerkommunikation VZ 10640623X (DE-625)10640623X 54.25$jParallele Datenverarbeitung VZ 181569892 (DE-625)181569892 AR 21 2018 4 25 08 2013-2029 |
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10.1007/s10586-018-2841-4 doi (DE-627)OLC2066391026 (DE-He213)s10586-018-2841-4-p DE-627 ger DE-627 rakwb eng 004 VZ 54.50$jProgrammierung: Allgemeines bkl 54.32$jRechnerkommunikation bkl 54.25$jParallele Datenverarbeitung bkl Li, Chunlin verfasserin aut Towards operational cost minimization for cloud bursting with deadline constraints in hybrid clouds 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In hybrid clouds, there is a technique named cloud bursting which can allow companies to expand their capacity to meet the demands of peak workloads in a low-priced manner. In this work, a cost-aware job scheduling approach based on queueing theory in hybrid clouds is proposed. The job scheduling problem in the private cloud is modeled as a queueing model. A genetic algorithm is applied to achieve optimal queues for jobs to improve the utilization rate of the private cloud. Then, the task execution time is predicted by back propagation neural network. The max–min strategy is applied to schedule tasks according to the prediction results in hybrid clouds. Experiments show that our cost-aware job scheduling algorithm can reduce the average job waiting time and average job response time in the private cloud. In additional, our proposed job scheduling algorithm can improve the system throughput of the private cloud. It also can reduce the average task waiting time, average task response time and total costs in hybrid clouds. Hybrid clouds Cloud bursting Load balancing Genetic algorithm BP neural network Tang, Jianhang aut Luo, Youlong aut Enthalten in Cluster computing Springer US, 1998 21(2018), 4 vom: 25. Aug., Seite 2013-2029 (DE-627)265187907 (DE-600)1465290-0 (DE-576)9265187905 1386-7857 nnns volume:21 year:2018 number:4 day:25 month:08 pages:2013-2029 https://doi.org/10.1007/s10586-018-2841-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 54.50$jProgrammierung: Allgemeines VZ 181569876 (DE-625)181569876 54.32$jRechnerkommunikation VZ 10640623X (DE-625)10640623X 54.25$jParallele Datenverarbeitung VZ 181569892 (DE-625)181569892 AR 21 2018 4 25 08 2013-2029 |
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10.1007/s10586-018-2841-4 doi (DE-627)OLC2066391026 (DE-He213)s10586-018-2841-4-p DE-627 ger DE-627 rakwb eng 004 VZ 54.50$jProgrammierung: Allgemeines bkl 54.32$jRechnerkommunikation bkl 54.25$jParallele Datenverarbeitung bkl Li, Chunlin verfasserin aut Towards operational cost minimization for cloud bursting with deadline constraints in hybrid clouds 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In hybrid clouds, there is a technique named cloud bursting which can allow companies to expand their capacity to meet the demands of peak workloads in a low-priced manner. In this work, a cost-aware job scheduling approach based on queueing theory in hybrid clouds is proposed. The job scheduling problem in the private cloud is modeled as a queueing model. A genetic algorithm is applied to achieve optimal queues for jobs to improve the utilization rate of the private cloud. Then, the task execution time is predicted by back propagation neural network. The max–min strategy is applied to schedule tasks according to the prediction results in hybrid clouds. Experiments show that our cost-aware job scheduling algorithm can reduce the average job waiting time and average job response time in the private cloud. In additional, our proposed job scheduling algorithm can improve the system throughput of the private cloud. It also can reduce the average task waiting time, average task response time and total costs in hybrid clouds. Hybrid clouds Cloud bursting Load balancing Genetic algorithm BP neural network Tang, Jianhang aut Luo, Youlong aut Enthalten in Cluster computing Springer US, 1998 21(2018), 4 vom: 25. Aug., Seite 2013-2029 (DE-627)265187907 (DE-600)1465290-0 (DE-576)9265187905 1386-7857 nnns volume:21 year:2018 number:4 day:25 month:08 pages:2013-2029 https://doi.org/10.1007/s10586-018-2841-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 54.50$jProgrammierung: Allgemeines VZ 181569876 (DE-625)181569876 54.32$jRechnerkommunikation VZ 10640623X (DE-625)10640623X 54.25$jParallele Datenverarbeitung VZ 181569892 (DE-625)181569892 AR 21 2018 4 25 08 2013-2029 |
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Towards operational cost minimization for cloud bursting with deadline constraints in hybrid clouds |
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towards operational cost minimization for cloud bursting with deadline constraints in hybrid clouds |
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Towards operational cost minimization for cloud bursting with deadline constraints in hybrid clouds |
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Abstract In hybrid clouds, there is a technique named cloud bursting which can allow companies to expand their capacity to meet the demands of peak workloads in a low-priced manner. In this work, a cost-aware job scheduling approach based on queueing theory in hybrid clouds is proposed. The job scheduling problem in the private cloud is modeled as a queueing model. A genetic algorithm is applied to achieve optimal queues for jobs to improve the utilization rate of the private cloud. Then, the task execution time is predicted by back propagation neural network. The max–min strategy is applied to schedule tasks according to the prediction results in hybrid clouds. Experiments show that our cost-aware job scheduling algorithm can reduce the average job waiting time and average job response time in the private cloud. In additional, our proposed job scheduling algorithm can improve the system throughput of the private cloud. It also can reduce the average task waiting time, average task response time and total costs in hybrid clouds. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract In hybrid clouds, there is a technique named cloud bursting which can allow companies to expand their capacity to meet the demands of peak workloads in a low-priced manner. In this work, a cost-aware job scheduling approach based on queueing theory in hybrid clouds is proposed. The job scheduling problem in the private cloud is modeled as a queueing model. A genetic algorithm is applied to achieve optimal queues for jobs to improve the utilization rate of the private cloud. Then, the task execution time is predicted by back propagation neural network. The max–min strategy is applied to schedule tasks according to the prediction results in hybrid clouds. Experiments show that our cost-aware job scheduling algorithm can reduce the average job waiting time and average job response time in the private cloud. In additional, our proposed job scheduling algorithm can improve the system throughput of the private cloud. It also can reduce the average task waiting time, average task response time and total costs in hybrid clouds. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract In hybrid clouds, there is a technique named cloud bursting which can allow companies to expand their capacity to meet the demands of peak workloads in a low-priced manner. In this work, a cost-aware job scheduling approach based on queueing theory in hybrid clouds is proposed. The job scheduling problem in the private cloud is modeled as a queueing model. A genetic algorithm is applied to achieve optimal queues for jobs to improve the utilization rate of the private cloud. Then, the task execution time is predicted by back propagation neural network. The max–min strategy is applied to schedule tasks according to the prediction results in hybrid clouds. Experiments show that our cost-aware job scheduling algorithm can reduce the average job waiting time and average job response time in the private cloud. In additional, our proposed job scheduling algorithm can improve the system throughput of the private cloud. It also can reduce the average task waiting time, average task response time and total costs in hybrid clouds. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Towards operational cost minimization for cloud bursting with deadline constraints in hybrid clouds |
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