Research on computing task scheduling method for distributed heterogeneous parallel systems

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...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 8937 - 18
Hauptverfasser: Cao, Xianzhi, Chen, Chong, Li, Shiwei, Lv, Chang, Li, Jiali, Wang, Jian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Nature Publishing Group 15.03.2025
Nature Publishing Group UK
Nature Portfolio
Schlagworte:
Algorithms
Directed acyclic graph
Dynamic redundancy
Dynamic scheduling
Energy consumption
Energy efficiency
Heterogeneous parallel
Nonlinear programming
Scheduling
Streams
Heterogeneous parallel
Directed acyclic graph
Dynamic redundancy
Dynamic scheduling
ISSN:2045-2322, 2045-2322
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Zusammenfassung: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-94068-0