使用优化集群基于联合学习的云计算负载平衡
Krishna Keerthi Chennam1, Uma Maheswari V2, Rajanikanth Aluvalu3
1Department of CSE, Vasavi College of Engineering, Hyderabad, India.
Scientific reports
|November 21, 2025
概括
本研究介绍了一种新的基于集群的联合学习 (FL) 框架,用于高效的云任务调度和负载平衡. 新模型优化了资源利用,减少了执行时间和能源消耗.
科学领域:
- 云计算 云计算 云计算
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 云计算在任务调度和负载平衡方面面临着NP-hard的优化挑战.
- 不高效的资源利用,高能耗和长时间的执行时间是常见的问题.
研究的目的:
- 开发一种新的基于集群的联合学习 (FL) 框架,用于高效的云任务调度和负载平衡.
- 通过对具有相似特征的虚拟机 (VM) 进行集群来解决系统异质性.
主要方法:
- 实现基于特征的VM集群的无监督学习.
- 利用VM功能和基于衍生品的目标函数来优化调度.
- 与鱼优化算法 (WOA),蝶优化 (BFO),五月优化 (MFO) 和火优化 (FHO) 相比.
主要成果:
- 基于集群的FL模型与COA算法展示了卓越的性能.
- 实现了高达10%的制造量减少和15%的置时间减少.
- 在虚拟机之间显示了负载平衡的显著改进.
结论:
- 在联合学习中集群的集成为云资源管理提供了一个可扩展和适应的解决方案.
- 拟议的框架为优化云环境提供了一种弹性方法.
- 这种方法有效地提高了效率,并减少了在云任务调度中的资源浪费.
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