一个可扩展的机器学习策略,用于数据库中的资源配置
Fady Nashat Manhary1, Marghny H Mohamed2, Mamdouh Farouk1
1Department of Computer Science, Faculty of Computers and Information, Assiut University, Assiut, Egypt.
Scientific reports
|August 20, 2025
概括
本研究介绍了LSTM-MARL-Ape-X,这是一种用于智能云资源配置的新框架. 它实现了主动,可持续和可扩展的云编排,提高了效率和准确性.
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:
- 现代云系统需要高效的资源配置,平衡服务质量 (QoS),成本和能源.
- 现有的深度Q学习 (DQN) 方法面临样本效率低下和集中化问题.
- 像临时融合变压器 (TFT) 这样的变压器模型是准确的,但对于实时使用而言计算密集.
研究的目的:
- 为云计算开发一个主动,分散和可扩展的资源管理框架.
- 在效率和实时部署方面克服现有的DQN和TFT模型的局限性.
- 为了实现端到端优化,实现强大和可持续的云编排.
主要方法:
- 整合双向长短期记忆 (BiLSTM) 用于工作负载预测与多代理强化学习 (MARL).
- 使用分布式Ape-X架构进行可扩展的学习.
- 创新包括BiLSTM与功能明智的注意力,差异规范的信用分配,和适应优先重播.
主要成果:
- 实现了94.6%的服务水平协议 (SLA) 合规性,并减少了22%的能源消耗.
- 证明了线性可扩展性超过5000个节点,决策延迟小于100毫秒.
- 框架融合速度比基线快3.2倍,在准确性和速度方面超过了变压器模型.
结论:
- LSTM-MARL-Ape-X为智能云资源配置提供了强大的解决方案,提高了效率和可持续性.
- 该框架提供端到端优化,在性能和可扩展性方面超过现有方法.
- 允许主动和分散的云编排,这对于现代大规模系统至关重要.
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