Research on computing task scheduling method for distributed heterogeneous parallel systems
Abstract With the explosive growth of terminal devices, scheduling massive parallel task streams has become a core challenge for distributed platforms. For computing resource providers, enhancing reliability, shortening response times, and reducing costs are significant challenges, particularly in a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-94068-0 |
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| _version_ | 1850039656874246144 |
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| author | Xianzhi Cao Chong Chen Shiwei Li Chang Lv Jiali Li Jian Wang |
| author_facet | Xianzhi Cao Chong Chen Shiwei Li Chang Lv Jiali Li Jian Wang |
| author_sort | Xianzhi Cao |
| collection | DOAJ |
| description | Abstract With the explosive growth of terminal devices, scheduling massive parallel task streams has become a core challenge for distributed platforms. For computing resource providers, enhancing reliability, shortening response times, and reducing costs are significant challenges, particularly in achieving energy efficiency through scheduling to realize green computing. This paper investigates the heterogeneous parallel task flow scheduling problem to minimize system energy consumption under response time constraints. First, for a set of independent tasks capable of parallel computation on heterogeneous terminals, the task scheduling is performed according to the computational resource capabilities of each terminal. The problem is modeled as a mixed-integer nonlinear programming problem using a Directed Acyclic Graph as the input model. Then, a dynamic scheduling method based on heuristic and reinforcement learning algorithms is proposed to schedule the task flows. Furthermore, dynamic redundancy is applied to certain tasks based on reliability analysis to enhance system fault tolerance and improve service quality. Experimental results show that our method can achieve significant improvements, reducing energy consumption by 14.3% compared to existing approaches on two practical workflow instances. |
| format | Article |
| id | doaj-art-aff0feed67454e90aa2854d8a0c871e1 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-aff0feed67454e90aa2854d8a0c871e12025-08-20T02:56:16ZengNature PortfolioScientific Reports2045-23222025-03-0115111810.1038/s41598-025-94068-0Research on computing task scheduling method for distributed heterogeneous parallel systemsXianzhi Cao0Chong Chen1Shiwei Li2Chang Lv3Jiali Li4Jian Wang5College of Computer and Control Engineering, Northeast Forestry UniversityCollege of Computer and Control Engineering, Northeast Forestry UniversityCollege of Computer and Control Engineering, Northeast Forestry UniversityCollege of Computer and Control Engineering, Northeast Forestry UniversityCollege of Computer and Control Engineering, Northeast Forestry UniversityCollege of Computer and Control Engineering, Northeast Forestry UniversityAbstract With the explosive growth of terminal devices, scheduling massive parallel task streams has become a core challenge for distributed platforms. For computing resource providers, enhancing reliability, shortening response times, and reducing costs are significant challenges, particularly in achieving energy efficiency through scheduling to realize green computing. This paper investigates the heterogeneous parallel task flow scheduling problem to minimize system energy consumption under response time constraints. First, for a set of independent tasks capable of parallel computation on heterogeneous terminals, the task scheduling is performed according to the computational resource capabilities of each terminal. The problem is modeled as a mixed-integer nonlinear programming problem using a Directed Acyclic Graph as the input model. Then, a dynamic scheduling method based on heuristic and reinforcement learning algorithms is proposed to schedule the task flows. Furthermore, dynamic redundancy is applied to certain tasks based on reliability analysis to enhance system fault tolerance and improve service quality. Experimental results show that our method can achieve significant improvements, reducing energy consumption by 14.3% compared to existing approaches on two practical workflow instances.https://doi.org/10.1038/s41598-025-94068-0Heterogeneous parallelDynamic schedulingDirected acyclic graphDynamic redundancy |
| spellingShingle | Xianzhi Cao Chong Chen Shiwei Li Chang Lv Jiali Li Jian Wang Research on computing task scheduling method for distributed heterogeneous parallel systems Scientific Reports Heterogeneous parallel Dynamic scheduling Directed acyclic graph Dynamic redundancy |
| title | Research on computing task scheduling method for distributed heterogeneous parallel systems |
| title_full | Research on computing task scheduling method for distributed heterogeneous parallel systems |
| title_fullStr | Research on computing task scheduling method for distributed heterogeneous parallel systems |
| title_full_unstemmed | Research on computing task scheduling method for distributed heterogeneous parallel systems |
| title_short | Research on computing task scheduling method for distributed heterogeneous parallel systems |
| title_sort | research on computing task scheduling method for distributed heterogeneous parallel systems |
| topic | Heterogeneous parallel Dynamic scheduling Directed acyclic graph Dynamic redundancy |
| url | https://doi.org/10.1038/s41598-025-94068-0 |
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