用于云计算的层次深度强化学习的新型云任务调度框架
Delong Cui1, Zhiping Peng2, Kaibin Li1
1College of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming, China.
PloS one
|August 21, 2025
概括
这项研究介绍了一个层次深度强化学习 (DRL) 框架,用于高效的云任务调度. 通过DRL调度器优化成本和性能,改善负载平衡,并减少10%的逾期任务.
科学领域:
- 计算机科学
- 人工智能
- 云计算
背景情况:
- 由于大量的动态负载,云计算任务调度是NP完整的.
- 现有的方法在动态云环境中难以实现效率和适应性.
研究的目的:
- 为大规模的云任务安排提出一个新的层次深度强化学习 (DRL) 框架.
- 在动态云环境中增强适应性,成本效益和性能.
主要方法:
- 一种分层调度方法,首先将任务分配给虚拟机集群,然后分配给单个虚拟机.
- 一个基于DRL的调度器,不断学习和调整网络参数.
主要成果:
- 该DRL框架有效地平衡成本和性能,优化负载平衡,成本和延迟时间.
- 与经典启发式算法相比,实现了10%的整体改进.
- 在低负载场景中可证明成本降低,在高负载场景中可证明资源利用率提高.
结论:
- 提出的等级DRL框架为复杂的云任务调度挑战提供了有希望的解决方案.
- 已知限制包括计算开销,潜在延迟和数据依赖性.
- 需要进一步的研究来解决复杂性和提高实时效率.
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