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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| References | Ekmecic, Tartalja, Milutinovic (b5) 1996; 84 Ryan Mork, Paul Martin, Zhiming Zhao, Contemporary challenges for data-intensive scientific workflow management systems, in: Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science, 2015. Djigal (b16) 2020; 32 Li (b3) 2016; 65 Kwok, Ahmad (b8) 1996; 7 Tang, Tan (b27) 2016; 2016 Naithani, Eyerman, Eeckhout (b26) 2017 Sirisha (b7) 2023; 79 Hamza Djigal, Jun Feng, Jiamin Lu, Task scheduling for heterogeneous computing using a predict cost matrix, in: Workshop Proceedings of the 48th International Conference on Parallel Processing, 2019. Arabnejad, Barbosa (b13) 2013; 25 Wang, Sinnen (b19) 2018; 29 Chai (b20) 2023; 14 Sun, Zhou, Niu (b21) 2020; 20 Bittencourt, Sakellariou, Madeira (b4) 2010 Wang (b2) 2016; 2016 Hu, Veeravalli (b25) 2013; 63 Maurya, Tripathi (b10) 2018; 74 Topcuoglu, Hariri, Wu (b12) 2002; 13 Juve (b30) 2013; 29 Akarvardar, Wong (b1) 2023; 111 Lee (b6) 2016; 28 Wang, Wang, Qiao, Chen, Xie, Guo (b17) 2025; 164 He (b23) 2018; 30 Suter, Hunold (b29) 2013 Chen (b22) 2017; 28 Daoud, Kharma (b24) 2008; 68 Zhou (b14) 2017; 29 Sinnen, Sousa (b28) 2005; 16 Sirisha, Vijaya Kumari (b11) 2015 Ahmad (b9) 2024 Etminani, Naghibzadeh (b18) 2007 Abramovici (b31) 1992; 256 He (10.1016/j.future.2025.107733_b23) 2018; 30 10.1016/j.future.2025.107733_b32 Akarvardar (10.1016/j.future.2025.107733_b1) 2023; 111 Ahmad (10.1016/j.future.2025.107733_b9) 2024 Wang (10.1016/j.future.2025.107733_b19) 2018; 29 Ekmecic (10.1016/j.future.2025.107733_b5) 1996; 84 Sun (10.1016/j.future.2025.107733_b21) 2020; 20 Naithani (10.1016/j.future.2025.107733_b26) 2017 Wang (10.1016/j.future.2025.107733_b2) 2016; 2016 10.1016/j.future.2025.107733_b15 Abramovici (10.1016/j.future.2025.107733_b31) 1992; 256 Li (10.1016/j.future.2025.107733_b3) 2016; 65 Sirisha (10.1016/j.future.2025.107733_b11) 2015 Maurya (10.1016/j.future.2025.107733_b10) 2018; 74 Chai (10.1016/j.future.2025.107733_b20) 2023; 14 Wang (10.1016/j.future.2025.107733_b17) 2025; 164 Daoud (10.1016/j.future.2025.107733_b24) 2008; 68 Tang (10.1016/j.future.2025.107733_b27) 2016; 2016 Kwok (10.1016/j.future.2025.107733_b8) 1996; 7 Sirisha (10.1016/j.future.2025.107733_b7) 2023; 79 Topcuoglu (10.1016/j.future.2025.107733_b12) 2002; 13 Zhou (10.1016/j.future.2025.107733_b14) 2017; 29 Sinnen (10.1016/j.future.2025.107733_b28) 2005; 16 Lee (10.1016/j.future.2025.107733_b6) 2016; 28 Hu (10.1016/j.future.2025.107733_b25) 2013; 63 Juve (10.1016/j.future.2025.107733_b30) 2013; 29 Bittencourt (10.1016/j.future.2025.107733_b4) 2010 Chen (10.1016/j.future.2025.107733_b22) 2017; 28 Arabnejad (10.1016/j.future.2025.107733_b13) 2013; 25 Suter (10.1016/j.future.2025.107733_b29) 2013 Etminani (10.1016/j.future.2025.107733_b18) 2007 Djigal (10.1016/j.future.2025.107733_b16) 2020; 32 |
| References_xml | – volume: 2016 year: 2016 ident: b2 article-title: HSIP: A novel task scheduling algorithm for heterogeneous computing publication-title: Sci. Program. – volume: 25 start-page: 682 year: 2013 end-page: 694 ident: b13 article-title: List scheduling algorithm for heterogeneous systems by an optimistic cost table publication-title: IEEE Trans. Parallel Distrib. Syst. – volume: 256 start-page: 325 year: 1992 end-page: 333 ident: b31 article-title: LIGO: The laser interferometer gravitational-wave observatory publication-title: Science – volume: 2016 year: 2016 ident: b27 article-title: Energy-efficient reliability-aware scheduling algorithm on heterogeneous systems publication-title: Sci. Program. – volume: 30 start-page: 2 year: 2018 end-page: 14 ident: b23 article-title: A novel task-duplication based clustering algorithm for heterogeneous computing environments publication-title: IEEE Trans. Parallel Distrib. Syst. – year: 2013 ident: b29 article-title: Daggen: A synthetic task graph generator – volume: 28 start-page: 230 year: 2016 end-page: 243 ident: b6 article-title: Time-reversibility for real-time scheduling on multiprocessor systems publication-title: IEEE Trans. Parallel Distrib. Syst. – volume: 74 start-page: 3039 year: 2018 end-page: 3070 ident: b10 article-title: On benchmarking task scheduling algorithms for heterogeneous computing systems publication-title: J. Supercomput. – volume: 28 start-page: 2674 year: 2017 end-page: 2688 ident: b22 article-title: Scheduling for workflows with security-sensitive intermediate data by selective task duplication in clouds publication-title: IEEE Trans. Parallel Distrib. Syst. – volume: 111 start-page: 92 year: 2023 end-page: 112 ident: b1 article-title: Technology prospects for data-intensive computing publication-title: Proc. IEEE – volume: 14 start-page: 14807 year: 2023 end-page: 14815 ident: b20 article-title: Task scheduling based on swarm intelligence algorithms in high performance computing environment publication-title: J. Ambient Intell. Humaniz. Comput. – volume: 79 start-page: 924 year: 2023 end-page: 946 ident: b7 article-title: Complexity versus quality: a trade-off for scheduling workflows in heterogeneous computing environments publication-title: J. Supercomput. – volume: 7 start-page: 506 year: 1996 end-page: 521 ident: b8 article-title: Dynamic critical-path scheduling: An effective technique for allocating task graphs to multiprocessors publication-title: IEEE Trans. Parallel Distrib. Syst. – volume: 84 start-page: 1127 year: 1996 end-page: 1144 ident: b5 article-title: A survey of heterogeneous computing: concepts and systems publication-title: Proc. IEEE – volume: 32 start-page: 1057 year: 2020 end-page: 1071 ident: b16 article-title: IPPTS: An efficient algorithm for scientific workflow scheduling in heterogeneous computing systems publication-title: IEEE Trans. Parallel Distrib. Syst. – start-page: 397 year: 2017 end-page: 408 ident: b26 article-title: Reliability-aware scheduling on heterogeneous multicore processors publication-title: 2017 IEEE International Symposium on High Performance Computer Architecture – volume: 29 start-page: 682 year: 2013 end-page: 692 ident: b30 article-title: Characterizing and profiling scientific workflows publication-title: Future Gener. Comput. Syst. – start-page: 1 year: 2007 end-page: 6 ident: b18 article-title: A min-min max-min selective algorithm for grid task scheduling publication-title: 2007 3rd IEEE/IFIP International Conference in Central Asia on Internet – reference: Ryan Mork, Paul Martin, Zhiming Zhao, Contemporary challenges for data-intensive scientific workflow management systems, in: Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science, 2015. – reference: Hamza Djigal, Jun Feng, Jiamin Lu, Task scheduling for heterogeneous computing using a predict cost matrix, in: Workshop Proceedings of the 48th International Conference on Parallel Processing, 2019. – start-page: 1 year: 2015 end-page: 6 ident: b11 article-title: A new heuristic for minimizing schedule length in heterogeneous computing systems publication-title: 2015 IEEE International Conference on Electrical, Computer and Communication Technologies – volume: 65 start-page: 140 year: 2016 end-page: 152 ident: b3 article-title: A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds publication-title: Future Gener. Comput. Syst. – volume: 16 start-page: 503 year: 2005 end-page: 515 ident: b28 article-title: Communication contention in task scheduling publication-title: IEEE Trans. Parallel Distrib. Syst. – volume: 29 year: 2017 ident: b14 article-title: A list scheduling algorithm for heterogeneous systems based on a critical node cost table and pessimistic cost table publication-title: Concurr. Comput.: Pract. Exper. – start-page: 1 year: 2024 end-page: 23 ident: b9 article-title: An analytical review and performance measures of state-of-art scheduling algorithms in heterogenous computing environment publication-title: Arch. Comput. Methods Eng. – volume: 13 start-page: 260 year: 2002 end-page: 274 ident: b12 article-title: Performance-effective and low-complexity task scheduling for heterogeneous computing publication-title: IEEE Trans. Parallel Distrib. Syst. – start-page: 357 year: 2010 end-page: 364 ident: b4 article-title: Dag scheduling using a lookahead variant of the heterogeneous earliest finish time algorithm publication-title: 2010 18th Euromicro Conference on Parallel, Distributed and Network-Based Processing – volume: 68 start-page: 399 year: 2008 end-page: 409 ident: b24 article-title: A high performance algorithm for static task scheduling in heterogeneous distributed computing systems publication-title: J. Parallel Distrib. Comput. – volume: 29 start-page: 1736 year: 2018 end-page: 1749 ident: b19 article-title: List-scheduling versus cluster-scheduling publication-title: IEEE Trans. Parallel Distrib. Syst. – volume: 63 start-page: 2988 year: 2013 end-page: 2997 ident: b25 article-title: Dynamic scheduling of hybrid real-time tasks on clusters publication-title: IEEE Trans. Comput. – volume: 164 year: 2025 ident: b17 article-title: Heterogeneous system list scheduling algorithm based on improved optimistic cost matrix publication-title: Future Gener. Comput. Syst. – volume: 20 start-page: 1138 year: 2020 end-page: 1151 ident: b21 article-title: Distributed task replication for vehicular edge computing: Performance analysis and learning-based algorithm publication-title: IEEE Trans. Wireless Commun. – start-page: 1 year: 2007 ident: 10.1016/j.future.2025.107733_b18 article-title: A min-min max-min selective algorithm for grid task scheduling – volume: 29 start-page: 682 issue: 3 year: 2013 ident: 10.1016/j.future.2025.107733_b30 article-title: Characterizing and profiling scientific workflows publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2012.08.015 – volume: 29 issue: 5 year: 2017 ident: 10.1016/j.future.2025.107733_b14 article-title: A list scheduling algorithm for heterogeneous systems based on a critical node cost table and pessimistic cost table publication-title: Concurr. Comput.: Pract. Exper. doi: 10.1002/cpe.3944 – volume: 84 start-page: 1127 issue: 8 year: 1996 ident: 10.1016/j.future.2025.107733_b5 article-title: A survey of heterogeneous computing: concepts and systems publication-title: Proc. IEEE doi: 10.1109/5.533958 – volume: 111 start-page: 92 issue: 1 year: 2023 ident: 10.1016/j.future.2025.107733_b1 article-title: Technology prospects for data-intensive computing publication-title: Proc. IEEE doi: 10.1109/JPROC.2022.3218057 – start-page: 1 year: 2024 ident: 10.1016/j.future.2025.107733_b9 article-title: An analytical review and performance measures of state-of-art scheduling algorithms in heterogenous computing environment publication-title: Arch. Comput. Methods Eng. – start-page: 357 year: 2010 ident: 10.1016/j.future.2025.107733_b4 article-title: Dag scheduling using a lookahead variant of the heterogeneous earliest finish time algorithm – volume: 16 start-page: 503 issue: 6 year: 2005 ident: 10.1016/j.future.2025.107733_b28 article-title: Communication contention in task scheduling publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2005.64 – volume: 28 start-page: 2674 issue: 9 year: 2017 ident: 10.1016/j.future.2025.107733_b22 article-title: Scheduling for workflows with security-sensitive intermediate data by selective task duplication in clouds publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2017.2678507 – volume: 14 start-page: 14807 issue: 11 year: 2023 ident: 10.1016/j.future.2025.107733_b20 article-title: Task scheduling based on swarm intelligence algorithms in high performance computing environment publication-title: J. Ambient Intell. Humaniz. Comput. doi: 10.1007/s12652-020-01994-0 – volume: 29 start-page: 1736 issue: 8 year: 2018 ident: 10.1016/j.future.2025.107733_b19 article-title: List-scheduling versus cluster-scheduling publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2018.2808959 – start-page: 397 year: 2017 ident: 10.1016/j.future.2025.107733_b26 article-title: Reliability-aware scheduling on heterogeneous multicore processors – volume: 2016 issue: 1 year: 2016 ident: 10.1016/j.future.2025.107733_b27 article-title: Energy-efficient reliability-aware scheduling algorithm on heterogeneous systems publication-title: Sci. Program. – volume: 28 start-page: 230 issue: 1 year: 2016 ident: 10.1016/j.future.2025.107733_b6 article-title: Time-reversibility for real-time scheduling on multiprocessor systems publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2016.2533615 – volume: 79 start-page: 924 issue: 1 year: 2023 ident: 10.1016/j.future.2025.107733_b7 article-title: Complexity versus quality: a trade-off for scheduling workflows in heterogeneous computing environments publication-title: J. Supercomput. doi: 10.1007/s11227-022-04687-x – volume: 2016 issue: 1 year: 2016 ident: 10.1016/j.future.2025.107733_b2 article-title: HSIP: A novel task scheduling algorithm for heterogeneous computing publication-title: Sci. Program. – volume: 32 start-page: 1057 issue: 5 year: 2020 ident: 10.1016/j.future.2025.107733_b16 article-title: IPPTS: An efficient algorithm for scientific workflow scheduling in heterogeneous computing systems publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2020.3041829 – volume: 25 start-page: 682 issue: 3 year: 2013 ident: 10.1016/j.future.2025.107733_b13 article-title: List scheduling algorithm for heterogeneous systems by an optimistic cost table publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2013.57 – volume: 164 year: 2025 ident: 10.1016/j.future.2025.107733_b17 article-title: Heterogeneous system list scheduling algorithm based on improved optimistic cost matrix publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2024.107576 – start-page: 1 year: 2015 ident: 10.1016/j.future.2025.107733_b11 article-title: A new heuristic for minimizing schedule length in heterogeneous computing systems – volume: 13 start-page: 260 issue: 3 year: 2002 ident: 10.1016/j.future.2025.107733_b12 article-title: Performance-effective and low-complexity task scheduling for heterogeneous computing publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/71.993206 – volume: 68 start-page: 399 issue: 4 year: 2008 ident: 10.1016/j.future.2025.107733_b24 article-title: A high performance algorithm for static task scheduling in heterogeneous distributed computing systems publication-title: J. Parallel Distrib. Comput. doi: 10.1016/j.jpdc.2007.05.015 – volume: 7 start-page: 506 issue: 5 year: 1996 ident: 10.1016/j.future.2025.107733_b8 article-title: Dynamic critical-path scheduling: An effective technique for allocating task graphs to multiprocessors publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/71.503776 – year: 2013 ident: 10.1016/j.future.2025.107733_b29 – volume: 256 start-page: 325 issue: 5055 year: 1992 ident: 10.1016/j.future.2025.107733_b31 article-title: LIGO: The laser interferometer gravitational-wave observatory publication-title: Science doi: 10.1126/science.256.5055.325 – ident: 10.1016/j.future.2025.107733_b32 doi: 10.1145/2822332.2822336 – volume: 63 start-page: 2988 issue: 12 year: 2013 ident: 10.1016/j.future.2025.107733_b25 article-title: Dynamic scheduling of hybrid real-time tasks on clusters publication-title: IEEE Trans. Comput. doi: 10.1109/TC.2013.170 – volume: 74 start-page: 3039 issue: 7 year: 2018 ident: 10.1016/j.future.2025.107733_b10 article-title: On benchmarking task scheduling algorithms for heterogeneous computing systems publication-title: J. Supercomput. doi: 10.1007/s11227-018-2355-0 – volume: 30 start-page: 2 issue: 1 year: 2018 ident: 10.1016/j.future.2025.107733_b23 article-title: A novel task-duplication based clustering algorithm for heterogeneous computing environments publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2018.2851221 – volume: 65 start-page: 140 year: 2016 ident: 10.1016/j.future.2025.107733_b3 article-title: A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2015.12.014 – ident: 10.1016/j.future.2025.107733_b15 doi: 10.1145/3339186.3339206 – volume: 20 start-page: 1138 issue: 2 year: 2020 ident: 10.1016/j.future.2025.107733_b21 article-title: Distributed task replication for vehicular edge computing: Performance analysis and learning-based algorithm publication-title: IEEE Trans. Wireless Commun. doi: 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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