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

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Listed:
  • Xian Yang
  • Jue Huang
  • Yun Zhao
  • Hui Tong
  • Shibing Chen
  • Yuxin Lu
  • Wei Cao

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.

Suggested Citation

  • Xian Yang & Jue Huang & Yun Zhao & Hui Tong & Shibing Chen & Yuxin Lu & Wei Cao, 2026. "A balanced scheduling method for multi-threaded tasks based on two-level parallelism between clusters and big data clustering," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 25(1), pages 30-45.
    • Handle: RePEc:ids:ijitma:v:25:y:2026:i:1:p:30-45
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