在多云环境中的高效的基于深度强化学习的任务调度器
Sudheer Mangalampalli1, Ganesh Reddy Karri2, M V Ratnamani3
1Department of CSE, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India. ms.sudheer@manipal.edu.
Scientific reports
|September 19, 2024
概括
适应性任务调度器 (ATSIA3C) 通过对任务进行细分和使用改进的异步优势演员关键算法来提高云计算效率. 这种方法可以显著降低产品种类,资源成本和能源消耗.
科学领域:
- 云计算 云计算 云计算
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 云计算中的任务调度问题 (TSP) 由于动态,可变的任务负载和异质资源而带来了挑战,影响了性能指标,如产量,能源消耗和成本.
- 传统的调度算法与动态云工作负载的复杂性和NP-hard性质作斗争.
- 现有的元启发式和混合式方法提供了近乎最佳的解决方案,但无法完全解决云环境中的动态TSP.
研究的目的:
- 为云计算环境开发有效的任务调度机制,以解决动态任务调度问题.
- 通过降低产量,能源消耗和资源成本来提高云模式的性能.
- 提出一种新的适应性任务调度器,利用先进的AI技术.
主要方法:
- 制定了一个自适应任务调度器 (ATSIA3C),将传入任务分成子任务.
- 模拟了使用改进异步优势演员关键 (IA3C) 算法的调度器,以实现有效的任务分割和调度.
- 实施了两阶段的调度过程:任务细分和子任务分组,然后根据约束来分配VM.
主要成果:
- 拟议的ATSIA3C与基线算法 (RATS-HM,AINN-BPSO,MOABCQ) 相比显示出显著的性能改进.
- 在 makespan 中取得了 70.49% 的改进,这表明任务完成速度更快.
- 在多云环境中,确保了资源成本提高77.42%,能源消耗减少74.24%.
结论:
- ATSIA3C有效地解决了云计算中的动态任务调度问题.
- 拟议的基于IA3C的自适应调度器为优化云资源利用和性能提供了卓越的解决方案.
- ATSIA3C在产量,成本效益和节能方面提供了实质性的改进,优于现有方法.
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