Look-ahead workflow scheduling with width changing trend in clouds
With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yang, Liwen [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023transfer abstract |
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| Schlagwörter: |
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| Umfang: |
12 |
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| Übergeordnetes Werk: |
Enthalten in: Surgeon-patient matching based on pairwise comparisons information for elective surgery - Jiang, Yan-Ping ELSEVIER, 2020, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:139 ; year:2023 ; pages:139-150 ; extent:12 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.future.2022.09.013 |
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ELV059367075 |
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| 520 | |a With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. | ||
| 520 | |a With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. | ||
| 650 | 7 | |a Cloud computing |2 Elsevier | |
| 650 | 7 | |a Deadline distribution |2 Elsevier | |
| 650 | 7 | |a Width changing trend |2 Elsevier | |
| 650 | 7 | |a Look-ahead |2 Elsevier | |
| 650 | 7 | |a Workflow scheduling |2 Elsevier | |
| 650 | 7 | |a Constrained optimization |2 Elsevier | |
| 700 | 1 | |a Ye, Lingjuan |4 oth | |
| 700 | 1 | |a Xia, Yuanqing |4 oth | |
| 700 | 1 | |a Zhan, Yufeng |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Jiang, Yan-Ping ELSEVIER |t Surgeon-patient matching based on pairwise comparisons information for elective surgery |d 2020 |g Amsterdam [u.a.] |w (DE-627)ELV004280385 |
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10.1016/j.future.2022.09.013 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001949.pica (DE-627)ELV059367075 (ELSEVIER)S0167-739X(22)00296-5 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Yang, Liwen verfasserin aut Look-ahead workflow scheduling with width changing trend in clouds 2023transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. Cloud computing Elsevier Deadline distribution Elsevier Width changing trend Elsevier Look-ahead Elsevier Workflow scheduling Elsevier Constrained optimization Elsevier Ye, Lingjuan oth Xia, Yuanqing oth Zhan, Yufeng oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:139 year:2023 pages:139-150 extent:12 https://doi.org/10.1016/j.future.2022.09.013 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 139 2023 139-150 12 |
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10.1016/j.future.2022.09.013 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001949.pica (DE-627)ELV059367075 (ELSEVIER)S0167-739X(22)00296-5 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Yang, Liwen verfasserin aut Look-ahead workflow scheduling with width changing trend in clouds 2023transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. Cloud computing Elsevier Deadline distribution Elsevier Width changing trend Elsevier Look-ahead Elsevier Workflow scheduling Elsevier Constrained optimization Elsevier Ye, Lingjuan oth Xia, Yuanqing oth Zhan, Yufeng oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:139 year:2023 pages:139-150 extent:12 https://doi.org/10.1016/j.future.2022.09.013 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 139 2023 139-150 12 |
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10.1016/j.future.2022.09.013 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001949.pica (DE-627)ELV059367075 (ELSEVIER)S0167-739X(22)00296-5 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Yang, Liwen verfasserin aut Look-ahead workflow scheduling with width changing trend in clouds 2023transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. Cloud computing Elsevier Deadline distribution Elsevier Width changing trend Elsevier Look-ahead Elsevier Workflow scheduling Elsevier Constrained optimization Elsevier Ye, Lingjuan oth Xia, Yuanqing oth Zhan, Yufeng oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:139 year:2023 pages:139-150 extent:12 https://doi.org/10.1016/j.future.2022.09.013 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 139 2023 139-150 12 |
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10.1016/j.future.2022.09.013 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001949.pica (DE-627)ELV059367075 (ELSEVIER)S0167-739X(22)00296-5 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Yang, Liwen verfasserin aut Look-ahead workflow scheduling with width changing trend in clouds 2023transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. Cloud computing Elsevier Deadline distribution Elsevier Width changing trend Elsevier Look-ahead Elsevier Workflow scheduling Elsevier Constrained optimization Elsevier Ye, Lingjuan oth Xia, Yuanqing oth Zhan, Yufeng oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:139 year:2023 pages:139-150 extent:12 https://doi.org/10.1016/j.future.2022.09.013 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 139 2023 139-150 12 |
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10.1016/j.future.2022.09.013 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001949.pica (DE-627)ELV059367075 (ELSEVIER)S0167-739X(22)00296-5 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Yang, Liwen verfasserin aut Look-ahead workflow scheduling with width changing trend in clouds 2023transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. Cloud computing Elsevier Deadline distribution Elsevier Width changing trend Elsevier Look-ahead Elsevier Workflow scheduling Elsevier Constrained optimization Elsevier Ye, Lingjuan oth Xia, Yuanqing oth Zhan, Yufeng oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:139 year:2023 pages:139-150 extent:12 https://doi.org/10.1016/j.future.2022.09.013 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 139 2023 139-150 12 |
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With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. |
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With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. |
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With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC. |
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