 A balanced scheduling method for multi-threaded tasks based on two-level parallelism between clusters and big data clusteringXian Yang, Jue Huang, Yun Zhao, Hui Tong, Shibing Chen, Yuxin Lu and Wei Cao International Journal of Information Technology and Management, 2026, vol. 25, issue 1, 30-45 Abstract: To improve the efficiency of task scheduling and enhance the negative load-balancing effect of tasks, this paper proposes a multi-threaded task-balancing scheduling method based on two-level parallelism between clusters and big data clustering. Firstly, use fuzzy C-means clustering to group task data into multiple clusters based on feature similarity. Then, build a multi-threaded task model that allows tasks to be executed in parallel on multiple threads, achieving two-level parallel processing between and within clusters. Finally, by determining task priority and hierarchical sorting, a task scheduling manager is designed to achieve balanced task scheduling. The experiment shows that the maximum standard deviation of the load in this method is 0.05, and the maximum over-time task ratio is 0.015, indicating that this method has strong load-balancing ability and can achieve real-time processing of tasks. Keywords: multi-threaded model; task scheduling; balanced scheduling; fuzzy C-means clustering; task scheduling manager. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijitma:v:25:y:2026:i:1:p:30-45 Access Statistics for this article More articles in International Journal of Information Technology and Management from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.