带截止期约束的复杂依赖任务的智能调度优化

王佳; 潘仟喜; 杨文忠

doi:10.13568/j.cnki.651094.651316.2024.12.20.0006

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带截止期约束的复杂依赖任务的智能调度优化

空-天-地一体化智能数字基座关键技术研究与行业应用专题 | 更新时间：2026-01-22

- 带截止期约束的复杂依赖任务的智能调度优化
- 带截止期约束的复杂依赖任务的智能调度优化
- 新疆大学学报（自然科学版中英文） 2025年42卷第4期页码：416-424
- 作者机构：
  
  1. 新疆大学计算机科学与技术学院
  2. 丝路多语言认知计算国际合作联合实验室
  3. 新疆多语种信息技术重点实验室
- 作者简介：
- 基金信息：
  
  新疆维吾尔自治区自然科学基金“带截止期约束的流作业动态智能调度优化”（2023D01C20）;国家自然科学基金“面向新疆农业多模态气象大数据的云模式灾害精准预测”（62363032）;新疆维吾尔自治区教育厅培育类项目“基于多模态多因素数据的交通流量预测及调度研究”（XJEDU2022P011）;新疆维吾尔自治区天山英才科技创新团队“面向农业的天地协同水资源时空精准调度研究及应用创新团队”（2023TSYCTD0012）
- DOI：10.13568/j.cnki.651094.651316.2024.12.20.0006
  中图分类号： S126;TP18
- 纸质出版：2025
- 稿件说明：
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[1]王佳,潘仟喜,杨文忠.带截止期约束的复杂依赖任务的智能调度优化[J].新疆大学学报(自然科学版中英文),2025,42(04):416-424.
[1]王佳,潘仟喜,杨文忠.带截止期约束的复杂依赖任务的智能调度优化[J].新疆大学学报(自然科学版中英文),2025,42(04):416-424. DOI： 10.13568/j.cnki.651094.651316.2024.12.20.0006.

DOI：10.13568/j.cnki.651094.651316.2024.12.20.0006.

摘要

农作物分类、种植面积估计、需水量/蓄水量的预测等是农业领域水资源高效利用的重要任务．当前天-地协同水资源管理中，不同设备（天基卫星、空基遥感、地面观测）所产生的异构数据间紧密关联．用户提交的不同任务通常由一系列相互依赖的子任务构成，且要求在给定的截止期前完成．故提出一种基于图注意力网络和元学习的调度优化策略，通过均衡子任务间的传输时间和计算时间以最小化所有作业的完工时间．为更好提取异构数据间的紧密关联，采用改进多头注意力机制的图注意力网络有效提取子任务间的依赖和关联关系．同时，利用指数平滑改进的元学习方法进行网络参数优化以提高模型的适应性．与现有深度学习调度算法相比，所提调度策略在截止期前完成的作业数量比率平均提升7.52%.

Abstract

Crop classification

planting areas estimation and water demand/storage prediction are critical for optimizing water resource management in agriculture. In the water management based on sky-ground collaboration

heterogeneous data from various devices such as satellite

airborne remote sensing and ground observations

are closely depended. In general

tasks required by users always contain several dependent subtasks

and tasks are required to be finished before deadline. In this paper

we introduce a smart scheduling based on graph attention network and meta-learning for tasks with deadlines

which minimizes makespan by the balance between the data transmission time and computation time of subtasks. An enhanced multi-head attention mechanism in graph attention networks is designed to extract associations among heterogeneous data. Additionally

an exponentially smoothed meta-learning approach is designed to optimize parameters of strategies. Compared to existing deep learning-based scheduling algorithms

the proposed strategy improves the average proportion of tasks completed before deadlines by 7.52%.

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references

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