云计算中的动态多目标任务调度,使用强化学习来优化能源和成本
Xiaomo Yu1,2, Jie Mi2, Ling Tang3
1Guangxi Colleges and Universities Key Laboratory of Intelligent Logistics Technology, Nanning Normal University, Nanning, 530001, Guangxi, China.
Scientific reports
|November 26, 2025
概括
本研究介绍了一种强化学习驱动的多目标任务调度 (RL-MOTS) 框架,使用深度Q网络 (DQN) 进行高效的云任务分配. 在确保服务质量 (QoS) 的同时,RL-MOTS显著降低了能源消耗和成本.
科学领域:
- 云计算 云计算 云计算
- 人工智能的人工智能
- 优化优化 优化优化
背景情况:
- 有效的任务调度对于云计算性能,能源效率和成本管理至关重要.
- 云环境中的动态工作负载需要适应性调度解决方案.
- 在云资源管理中,平衡性能,能源和成本仍然是关键的挑战.
研究的目的:
- 引入一个新的强化学习驱动的多目标任务安排 (RL-MOTS) 框架.
- 通过使用深度Q网络 (DQN) 在云环境中实现动态任务分配.
- 同时最大限度地降低能源消耗,降低运营成本,并确保服务质量 (QoS).
主要方法:
- 加强学习驱动的多目标任务安排 (RL-MOTS) 框架的开发.
- 使用深度Q网络 (DQN) 进行动态的任务对虚拟机的分配.
- 实施适应性奖励函数,考虑实时资源利用,截止日期和能源指标.
主要成果:
- RL-MOTS实现了高达27%的能源消耗降低.
- 与现有方法相比,成本效益提高了18%.
- 在不同的工作负载条件下,成功满足了严格的任务截止日期限制.
结论:
- RL-MOTS框架为云计算中的多目标任务调度提供了有效的解决方案.
- 该框架在异构的云环境中展示了强大的性能和适应性.
- RL-MOTS为下一代分布式计算提供了一个前性的解决方案,包括混合云端架构.
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