Cost-efficient reactive scheduling for real-time workflows in clouds
Abstract Workflow comprising of many tasks and data dependencies among tasks is an attractive programming paradigm for processing big data in clouds, and workflow scheduling plays essential roles in improving the cost and resource efficiency for cloud platforms. Up to now, large numbers of schedulin...
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| Autor*in: |
Chen, Huangke [verfasserIn] |
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| 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: The journal of supercomputing - Springer US, 1987, 74(2018), 11 vom: 03. Sept., Seite 6291-6309 |
|---|---|
| Übergeordnetes Werk: |
volume:74 ; year:2018 ; number:11 ; day:03 ; month:09 ; pages:6291-6309 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11227-018-2561-9 |
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OLC203395644X |
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| 520 | |a Abstract Workflow comprising of many tasks and data dependencies among tasks is an attractive programming paradigm for processing big data in clouds, and workflow scheduling plays essential roles in improving the cost and resource efficiency for cloud platforms. Up to now, large numbers of scheduling approaches have been proposed and improved. However, the majority of them focused on scheduling a single workflow and have not adequately exploited the idle time slots on resources to reduce the cost for executing workflow applications. To cover the above issue, we suggest to schedule tasks from different workflows in a hybrid way to take full advantage of idle time slots to improve the cost and resource efficiency, while guaranteeing the deadlines of workflows. To achieve the above idea, we first introduce a reactive scheduling architecture for real-time workflows. Then, a novel cost-efficient reactive scheduling algorithm (CERSA) is proposed to deploy multiple workflows with deadlines to cloud platforms. Finally, on the basis of real-world workflow traces, extensive experiments are conducted to compare CERSA with five existing algorithms. The experimental results demonstrate that CERSA is better than those algorithms with respect to monetary cost and resource efficiency. | ||
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10.1007/s11227-018-2561-9 doi (DE-627)OLC203395644X (DE-He213)s11227-018-2561-9-p DE-627 ger DE-627 rakwb eng 004 620 VZ Chen, Huangke verfasserin (orcid)0000-0003-2463-5580 aut Cost-efficient reactive scheduling for real-time workflows in 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 Workflow comprising of many tasks and data dependencies among tasks is an attractive programming paradigm for processing big data in clouds, and workflow scheduling plays essential roles in improving the cost and resource efficiency for cloud platforms. Up to now, large numbers of scheduling approaches have been proposed and improved. However, the majority of them focused on scheduling a single workflow and have not adequately exploited the idle time slots on resources to reduce the cost for executing workflow applications. To cover the above issue, we suggest to schedule tasks from different workflows in a hybrid way to take full advantage of idle time slots to improve the cost and resource efficiency, while guaranteeing the deadlines of workflows. To achieve the above idea, we first introduce a reactive scheduling architecture for real-time workflows. Then, a novel cost-efficient reactive scheduling algorithm (CERSA) is proposed to deploy multiple workflows with deadlines to cloud platforms. Finally, on the basis of real-world workflow traces, extensive experiments are conducted to compare CERSA with five existing algorithms. The experimental results demonstrate that CERSA is better than those algorithms with respect to monetary cost and resource efficiency. Scheduling Optimization Workflow Cloud computing Zhu, Jianghan aut Wu, Guohua (orcid)0000-0003-1552-9620 aut Huo, Lisu aut Enthalten in The journal of supercomputing Springer US, 1987 74(2018), 11 vom: 03. Sept., Seite 6291-6309 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:74 year:2018 number:11 day:03 month:09 pages:6291-6309 https://doi.org/10.1007/s11227-018-2561-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 74 2018 11 03 09 6291-6309 |
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10.1007/s11227-018-2561-9 doi (DE-627)OLC203395644X (DE-He213)s11227-018-2561-9-p DE-627 ger DE-627 rakwb eng 004 620 VZ Chen, Huangke verfasserin (orcid)0000-0003-2463-5580 aut Cost-efficient reactive scheduling for real-time workflows in 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 Workflow comprising of many tasks and data dependencies among tasks is an attractive programming paradigm for processing big data in clouds, and workflow scheduling plays essential roles in improving the cost and resource efficiency for cloud platforms. Up to now, large numbers of scheduling approaches have been proposed and improved. However, the majority of them focused on scheduling a single workflow and have not adequately exploited the idle time slots on resources to reduce the cost for executing workflow applications. To cover the above issue, we suggest to schedule tasks from different workflows in a hybrid way to take full advantage of idle time slots to improve the cost and resource efficiency, while guaranteeing the deadlines of workflows. To achieve the above idea, we first introduce a reactive scheduling architecture for real-time workflows. Then, a novel cost-efficient reactive scheduling algorithm (CERSA) is proposed to deploy multiple workflows with deadlines to cloud platforms. Finally, on the basis of real-world workflow traces, extensive experiments are conducted to compare CERSA with five existing algorithms. The experimental results demonstrate that CERSA is better than those algorithms with respect to monetary cost and resource efficiency. Scheduling Optimization Workflow Cloud computing Zhu, Jianghan aut Wu, Guohua (orcid)0000-0003-1552-9620 aut Huo, Lisu aut Enthalten in The journal of supercomputing Springer US, 1987 74(2018), 11 vom: 03. Sept., Seite 6291-6309 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:74 year:2018 number:11 day:03 month:09 pages:6291-6309 https://doi.org/10.1007/s11227-018-2561-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 74 2018 11 03 09 6291-6309 |
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10.1007/s11227-018-2561-9 doi (DE-627)OLC203395644X (DE-He213)s11227-018-2561-9-p DE-627 ger DE-627 rakwb eng 004 620 VZ Chen, Huangke verfasserin (orcid)0000-0003-2463-5580 aut Cost-efficient reactive scheduling for real-time workflows in 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 Workflow comprising of many tasks and data dependencies among tasks is an attractive programming paradigm for processing big data in clouds, and workflow scheduling plays essential roles in improving the cost and resource efficiency for cloud platforms. Up to now, large numbers of scheduling approaches have been proposed and improved. However, the majority of them focused on scheduling a single workflow and have not adequately exploited the idle time slots on resources to reduce the cost for executing workflow applications. To cover the above issue, we suggest to schedule tasks from different workflows in a hybrid way to take full advantage of idle time slots to improve the cost and resource efficiency, while guaranteeing the deadlines of workflows. To achieve the above idea, we first introduce a reactive scheduling architecture for real-time workflows. Then, a novel cost-efficient reactive scheduling algorithm (CERSA) is proposed to deploy multiple workflows with deadlines to cloud platforms. Finally, on the basis of real-world workflow traces, extensive experiments are conducted to compare CERSA with five existing algorithms. The experimental results demonstrate that CERSA is better than those algorithms with respect to monetary cost and resource efficiency. Scheduling Optimization Workflow Cloud computing Zhu, Jianghan aut Wu, Guohua (orcid)0000-0003-1552-9620 aut Huo, Lisu aut Enthalten in The journal of supercomputing Springer US, 1987 74(2018), 11 vom: 03. Sept., Seite 6291-6309 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:74 year:2018 number:11 day:03 month:09 pages:6291-6309 https://doi.org/10.1007/s11227-018-2561-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 74 2018 11 03 09 6291-6309 |
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10.1007/s11227-018-2561-9 doi (DE-627)OLC203395644X (DE-He213)s11227-018-2561-9-p DE-627 ger DE-627 rakwb eng 004 620 VZ Chen, Huangke verfasserin (orcid)0000-0003-2463-5580 aut Cost-efficient reactive scheduling for real-time workflows in 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 Workflow comprising of many tasks and data dependencies among tasks is an attractive programming paradigm for processing big data in clouds, and workflow scheduling plays essential roles in improving the cost and resource efficiency for cloud platforms. Up to now, large numbers of scheduling approaches have been proposed and improved. However, the majority of them focused on scheduling a single workflow and have not adequately exploited the idle time slots on resources to reduce the cost for executing workflow applications. To cover the above issue, we suggest to schedule tasks from different workflows in a hybrid way to take full advantage of idle time slots to improve the cost and resource efficiency, while guaranteeing the deadlines of workflows. To achieve the above idea, we first introduce a reactive scheduling architecture for real-time workflows. Then, a novel cost-efficient reactive scheduling algorithm (CERSA) is proposed to deploy multiple workflows with deadlines to cloud platforms. Finally, on the basis of real-world workflow traces, extensive experiments are conducted to compare CERSA with five existing algorithms. The experimental results demonstrate that CERSA is better than those algorithms with respect to monetary cost and resource efficiency. Scheduling Optimization Workflow Cloud computing Zhu, Jianghan aut Wu, Guohua (orcid)0000-0003-1552-9620 aut Huo, Lisu aut Enthalten in The journal of supercomputing Springer US, 1987 74(2018), 11 vom: 03. Sept., Seite 6291-6309 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:74 year:2018 number:11 day:03 month:09 pages:6291-6309 https://doi.org/10.1007/s11227-018-2561-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 74 2018 11 03 09 6291-6309 |
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Abstract Workflow comprising of many tasks and data dependencies among tasks is an attractive programming paradigm for processing big data in clouds, and workflow scheduling plays essential roles in improving the cost and resource efficiency for cloud platforms. Up to now, large numbers of scheduling approaches have been proposed and improved. However, the majority of them focused on scheduling a single workflow and have not adequately exploited the idle time slots on resources to reduce the cost for executing workflow applications. To cover the above issue, we suggest to schedule tasks from different workflows in a hybrid way to take full advantage of idle time slots to improve the cost and resource efficiency, while guaranteeing the deadlines of workflows. To achieve the above idea, we first introduce a reactive scheduling architecture for real-time workflows. Then, a novel cost-efficient reactive scheduling algorithm (CERSA) is proposed to deploy multiple workflows with deadlines to cloud platforms. Finally, on the basis of real-world workflow traces, extensive experiments are conducted to compare CERSA with five existing algorithms. The experimental results demonstrate that CERSA is better than those algorithms with respect to monetary cost and resource efficiency. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract Workflow comprising of many tasks and data dependencies among tasks is an attractive programming paradigm for processing big data in clouds, and workflow scheduling plays essential roles in improving the cost and resource efficiency for cloud platforms. Up to now, large numbers of scheduling approaches have been proposed and improved. However, the majority of them focused on scheduling a single workflow and have not adequately exploited the idle time slots on resources to reduce the cost for executing workflow applications. To cover the above issue, we suggest to schedule tasks from different workflows in a hybrid way to take full advantage of idle time slots to improve the cost and resource efficiency, while guaranteeing the deadlines of workflows. To achieve the above idea, we first introduce a reactive scheduling architecture for real-time workflows. Then, a novel cost-efficient reactive scheduling algorithm (CERSA) is proposed to deploy multiple workflows with deadlines to cloud platforms. Finally, on the basis of real-world workflow traces, extensive experiments are conducted to compare CERSA with five existing algorithms. The experimental results demonstrate that CERSA is better than those algorithms with respect to monetary cost and resource efficiency. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Workflow comprising of many tasks and data dependencies among tasks is an attractive programming paradigm for processing big data in clouds, and workflow scheduling plays essential roles in improving the cost and resource efficiency for cloud platforms. Up to now, large numbers of scheduling approaches have been proposed and improved. However, the majority of them focused on scheduling a single workflow and have not adequately exploited the idle time slots on resources to reduce the cost for executing workflow applications. To cover the above issue, we suggest to schedule tasks from different workflows in a hybrid way to take full advantage of idle time slots to improve the cost and resource efficiency, while guaranteeing the deadlines of workflows. To achieve the above idea, we first introduce a reactive scheduling architecture for real-time workflows. Then, a novel cost-efficient reactive scheduling algorithm (CERSA) is proposed to deploy multiple workflows with deadlines to cloud platforms. Finally, on the basis of real-world workflow traces, extensive experiments are conducted to compare CERSA with five existing algorithms. The experimental results demonstrate that CERSA is better than those algorithms with respect to monetary cost and resource efficiency. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Cost-efficient reactive scheduling for real-time workflows in clouds |
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https://doi.org/10.1007/s11227-018-2561-9 |
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Zhu, Jianghan Wu, Guohua Huo, Lisu |
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Zhu, Jianghan Wu, Guohua Huo, Lisu |
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