一个基于 DRL 的在线虚拟机调度器,用于优化云中介机构的成本
Xingjia Li1, Li Pan1, Shijun Liu1
1School of Software, Shandong University, Jinan, China.
概括
本研究介绍了DeepBS,这是一个新的深度强化学习 (DRL) 调度器,用于在云爆发中管理虚拟机 (VM). 对于不确定的基础设施即服务 (IaaS) 需求,DeepBS优化了成本,同时确保了服务质量 (QoS).
科学领域:
- 计算机科学 计算机科学
- 云计算 云计算 云计算
- 人工智能的人工智能
背景情况:
- 云爆发依赖于基础设施即服务 (IaaS) 虚拟机 (VM),由于不可预测的请求到达和生命周期,导致调度的不确定性.
- 现有的VM调度深度强化学习 (DRL) 方法往往忽视了服务质量 (QoS) 的保证.
研究的目的:
- 为解决支持云爆发的云经纪人在线虚拟机调度中的成本优化挑战.
- 开发一个基于DRL的调度器,尽量减少公共云费用,同时遵守QoS约束.
主要方法:
- 建议DeepBS,一个基于DRL的在线VM调度器,专为云中介设计.
- 实施适应性学习策略,以管理非平滑和不确定的用户请求.
- 评估DeepBS使用来自谷歌和阿里巴巴的集群痕迹来查看请求到达模式.
主要成果:
- 在DeepBS的研究中,比起基准算法,DeepBS展示了显著的成本优化优势.
- 调度器有效地适应不确定的和动态的VM请求环境.
- 在尽量减少运营成本的同时,满足 QoS 限制.
结论:
- 在云爆破环境中,DeepBS提供了一个强大的解决方案,用于成本效益高的虚拟机调度.
- DRL 方法提供了不确定的 IaaS 需求的适应性和高效管理.
- 这种方法提高了云爆破策略的经济可行性.
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