• Research on computing task sch...

Treffer: Research on computing task scheduling method for distributed heterogeneous parallel systems.

Title:
Research on computing task scheduling method for distributed heterogeneous parallel systems.
Authors:
Cao X; College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China., Chen C; College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China., Li S; College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China., Lv C; College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China., Li J; College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China., Wang J; College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China. wangj.icec@nefu.edu.cn.
Source:
Scientific reports [Sci Rep] 2025 Mar 15; Vol. 15 (1), pp. 8937. Date of Electronic Publication: 2025 Mar 15.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
References:
Sensors (Basel). 2024 Jun 27;24(13):. (PMID: 39000955)
Contributed Indexing:
Keywords: Directed acyclic graph; Dynamic redundancy; Dynamic scheduling; Heterogeneous parallel
Entry Date(s):
Date Created: 20250316 Latest Revision: 20250318
Update Code:
20250318
PubMed Central ID:
PMC11910613
DOI:
10.1038/s41598-025-94068-0
PMID:
40089577
Database:
MEDLINE

Weitere Informationen

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.
(© 2025. The Author(s).)

Declarations. Competing interests: The authors declare no Competing interests. All authors have read and agreed to the published version of the manuscript.