Article: A balanced scheduling method for multi-threaded tasks based on two-level parallelism between clusters and big data clustering Journal: International Journal of Information Technology and Management (IJITM) 2026 Vol.25 No.1 pp.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. Inderscience Publishers - linking academia, business and industry through research

Title: A balanced scheduling method for multi-threaded tasks based on two-level parallelism between clusters and big data clustering

Authors: Xian Yang; Jue Huang; Yun Zhao; Hui Tong; Shibing Chen; Yuxin Lu; Wei Cao

Addresses: Hainan Power Grid Co., Ltd., Haikou, 570311, Hainan, China ' Hainan Power Grid Co., Ltd., Haikou, 570311, Hainan, China ' China Southern Power Grid Research Institute Co., Ltd., Guangzhou, 510663, Guangdong, China ' Hainan Power Grid Co., Ltd., Haikou, 570311, Hainan, China ' Hainan Power Grid Co., Ltd., Haikou, 570311, Hainan, China ' China Southern Power Grid Research Institute Co., Ltd., Guangzhou, 510663, Guangdong, China ' China Southern Power Grid Research Institute Co., Ltd., Guangzhou, 510663, Guangdong, China

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.

DOI: 10.1504/IJITM.2026.152448

International Journal of Information Technology and Management, 2026 Vol.25 No.1, pp.30 - 45

Received: 21 Oct 2024
Accepted: 07 Feb 2025
Published online: 20 Mar 2026 *

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Title: A balanced scheduling method for multi-threaded tasks based on two-level parallelism between clusters and big data clustering

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