An enhanced list scheduling algorithm for heterogeneous computing using an optimized Predictive Cost Matrix
Effective task scheduling is essential for optimizing resource utilization and improving system performance in heterogeneous computing environments. Current algorithms face challenges, particularly their need for more focus on the computational demands of intensive tasks and their inadequate attenti...
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| Published in: | Future generation computer systems Vol. 166; p. 107733 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.05.2025
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| ISSN: | 0167-739X |
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| Abstract | Effective task scheduling is essential for optimizing resource utilization and improving system performance in heterogeneous computing environments. Current algorithms face challenges, particularly their need for more focus on the computational demands of intensive tasks and their inadequate attention to load balancing during processor allocation. To solve these problems, this study introduces the Balanced Prediction Priority Task Scheduling (BPPTS) algorithm, a novel list scheduling approach to improve the scheduling efficiency of compute-heavy tasks in heterogeneous systems. The BPPTS algorithm proposes the Balanced Prediction Cost Matrix (BPCM), which comprehensively evaluates the importance of tasks by considering their average computation cost. At the same time, a computation enhancement factor is introduced in the priority sorting to optimize the scheduling of computation-intensive tasks. The goal is to improve the scheduling efficiency of computation-intensive tasks and achieve load balancing. The BPPTS algorithm has a complexity of O(v2p), where v represents the number of tasks, and p denotes the number of processors. Experiments demonstrate that BPPTS outperforms other algorithms in terms of maximum completion time and speedup.
•A novel list-based scheduling algorithm, the Balanced Prediction Priority Task Scheduling (BPPTS) algorithm, is proposed to minimize task flow scheduling time. Additionally, it maintains a complexity of O(v2p).•A new computational matrix, the Balanced Prediction Cost Matrix (BPCM), is proposed. Based on processor costs and task dependencies, it predicts task costs across processors, aiming to optimize scheduling and improve resource allocation efficiency.•At the task prioritization phase, a computation enhancement factor is introduced to optimize task priority assignment. This approach increases the priority weight of compute-intensive tasks, improving scheduling efficiency and resource utilization, thereby achieving more effective load balancing. |
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| AbstractList | Effective task scheduling is essential for optimizing resource utilization and improving system performance in heterogeneous computing environments. Current algorithms face challenges, particularly their need for more focus on the computational demands of intensive tasks and their inadequate attention to load balancing during processor allocation. To solve these problems, this study introduces the Balanced Prediction Priority Task Scheduling (BPPTS) algorithm, a novel list scheduling approach to improve the scheduling efficiency of compute-heavy tasks in heterogeneous systems. The BPPTS algorithm proposes the Balanced Prediction Cost Matrix (BPCM), which comprehensively evaluates the importance of tasks by considering their average computation cost. At the same time, a computation enhancement factor is introduced in the priority sorting to optimize the scheduling of computation-intensive tasks. The goal is to improve the scheduling efficiency of computation-intensive tasks and achieve load balancing. The BPPTS algorithm has a complexity of O(v2p), where v represents the number of tasks, and p denotes the number of processors. Experiments demonstrate that BPPTS outperforms other algorithms in terms of maximum completion time and speedup.
•A novel list-based scheduling algorithm, the Balanced Prediction Priority Task Scheduling (BPPTS) algorithm, is proposed to minimize task flow scheduling time. Additionally, it maintains a complexity of O(v2p).•A new computational matrix, the Balanced Prediction Cost Matrix (BPCM), is proposed. Based on processor costs and task dependencies, it predicts task costs across processors, aiming to optimize scheduling and improve resource allocation efficiency.•At the task prioritization phase, a computation enhancement factor is introduced to optimize task priority assignment. This approach increases the priority weight of compute-intensive tasks, improving scheduling efficiency and resource utilization, thereby achieving more effective load balancing. |
| ArticleNumber | 107733 |
| Author | Chen, Jiawang Wang, Min Gao, Ziyi Wang, Haoyuan Bian, Weihao Qiao, Sibo |
| Author_xml | – sequence: 1 givenname: Min orcidid: 0000-0002-1852-9610 surname: Wang fullname: Wang, Min email: wangmin@tiangong.edu.cn organization: School of Life Sciences, Tiangong University, Tianjin 300387, China – sequence: 2 givenname: Jiawang orcidid: 0009-0006-4194-0682 surname: Chen fullname: Chen, Jiawang email: 2331091154@tiangong.edu.cn organization: School of Control Sciences and Engineering, Tiangong University, Tianjin 300387, China – sequence: 3 givenname: Haoyuan surname: Wang fullname: Wang, Haoyuan email: wanghaoyuan@tiangong.edu.cn organization: School of Control Sciences and Engineering, Tiangong University, Tianjin 300387, China – sequence: 4 givenname: Ziyi surname: Gao fullname: Gao, Ziyi email: 2230070937@tiangong.edu.cn organization: School of Control Sciences and Engineering, Tiangong University, Tianjin 300387, China – sequence: 5 givenname: Weihao surname: Bian fullname: Bian, Weihao email: 2331091152@tiangong.edu.cn organization: School of Control Sciences and Engineering, Tiangong University, Tianjin 300387, China – sequence: 6 givenname: Sibo surname: Qiao fullname: Qiao, Sibo email: siboqiao@tiangong.edu.cn organization: School of Software, Tiangong University, Tianjin 300387, China |
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| Cites_doi | 10.1016/j.future.2012.08.015 10.1002/cpe.3944 10.1109/5.533958 10.1109/JPROC.2022.3218057 10.1109/TPDS.2005.64 10.1109/TPDS.2017.2678507 10.1007/s12652-020-01994-0 10.1109/TPDS.2018.2808959 10.1109/TPDS.2016.2533615 10.1007/s11227-022-04687-x 10.1109/TPDS.2020.3041829 10.1109/TPDS.2013.57 10.1016/j.future.2024.107576 10.1109/71.993206 10.1016/j.jpdc.2007.05.015 10.1109/71.503776 10.1126/science.256.5055.325 10.1145/2822332.2822336 10.1109/TC.2013.170 10.1007/s11227-018-2355-0 10.1109/TPDS.2018.2851221 10.1016/j.future.2015.12.014 10.1145/3339186.3339206 10.1109/TWC.2020.3030889 |
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| Snippet | Effective task scheduling is essential for optimizing resource utilization and improving system performance in heterogeneous computing environments. Current... |
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| SubjectTerms | Computational complexity Heterogeneous computing List-based scheduling Task scheduling algorithms |
| Title | An enhanced list scheduling algorithm for heterogeneous computing using an optimized Predictive Cost Matrix |
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