云计算中的智能负载均衡:将功能选择与先进的深度学习模型集成在一起
Yousef Sanjalawe1, Salam Fraihat2, Salam Al-E'mari3
1Information Technology Department, King Abdullah II School for Information Technology, The University of Jordan (JU), Amman, Jordan.
PloS one
|September 9, 2025
概括
一个新的智能负载平衡策略,SLADRO,改善了云资源管理. 它使用深度学习和优化来更好地分配工作负载,在效率和利用方面表现优于传统方法.
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:
- 云计算的增长带来了资源管理的挑战.
- 传统的负载平衡对于动态云环境来说是不够的.
- 低于最佳的利用率和高成本是由于负载平衡不足造成的.
研究的目的:
- 为云环境引入一种新的智能负载平衡策略.
- 解决处理动态工作负载的传统方法的局限性.
- 提高资源利用率,降低云基础设施的运营成本.
主要方法:
- 提出了智能负载适应分布与强化和优化 (SLADRO) 方法.
- 集成卷积神经网络 (CNN) 和长短期记忆 (LSTM) 用于负载预测.
- 使用直角数组和粒子群优化 (OOA-PSO) 进行特征选择和深度强化学习 (DRL) 进行任务调度.
主要成果:
- 斯拉德罗显著优于传统的负载平衡技术.
- 在吞吐量和容量方面表现出显著的改进.
- 实现了资源利用和能源效率的提高.
结论:
- 斯拉德罗为云负载平衡提供了一个可扩展和适应的解决方案.
- 混合方法有效地优化了资源分配.
- 先进的技术为高效的云资源管理提供了一个全面的框架.
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