汉霍德尔:在云环境中节能资源分配,使用混合优化深度增强模型与HunterPlus调度器
Senthilkumar Chellamuthu1, Kalaivani Ramanathan2, Rajesh Arivanandhan1
1Department of Computer Science and Engineering, Erode Sengunthar Engineering College, Perundurai, India.
Network (Bristol, England)
|March 24, 2025
概括
本研究介绍了HUNHODRL,这是一个新的深度强化学习 (DRL) 框架,用于优化云计算资源配置. HUNHODRL增强了容器编排和工作负载平衡,显著提高了工作完成率和能源效率.
科学领域:
- 云计算 云计算 云计算 云计算
- 人工智能的人工智能
- 资源管理 资源管理
背景情况:
- 云计算资源优化对于最大限度地减少能源浪费和服务水平协议 (SLA) 违规至关重要.
- 现有的调度技术与动态资源分配作斗争,导致工作完成和容器利用效率低下.
研究的目的:
- 提出HUNHODRL,一个基于深度强化学习 (DRL) 的框架,用于高级容器编排和工作负载分配.
- 在动态云环境中提高资源管理效率.
主要方法:
- 开发了HUNHODRL,这是一个DRL框架,通过目的地主机容量向量和主动工作利用矩阵优化调度.
- 评估HUNHODRL与HUNDRL,Bi-GGCN和CNN使用CPU,内存和磁盘I / O利用指标在各种工作负载中.
主要成果:
- 与现有模型相比,HUNHODRL在容器创建率,工作完成率和SLA违规减少方面表现出卓越的表现.
- 在不增加虚拟机 (VM) 部署成本的情况下,实现了能源效率的显著提高.
- 展示了对不同工作负载的动态适应能力,证实了可扩展性和稳定性.
结论:
- 对于下一代云基础设施,HUNHODRL提供了一个强大的,可扩展的解决方案,其性能优于传统方法.
- 基于DRL的方法显著提高了云资源利用率和节能执行任务.
- 这一框架为云计算中的智能资源配置提供了一个有希望的方向.
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