在云数据中心使用价值和趋势方法进行基于VTGAN的主动虚拟机整合
Aya I Maiyza1, Hanan A Hassan2, Walaa M Sheta2
1Informatics Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria, Egypt. amaiyza@srtacity.sci.eg.
Scientific reports
|June 20, 2025
概括
预测模型可以提高云数据中心的效率. 新型生成对抗网络预测工作负载和趋势,显著减少服务违规和虚拟机迁移,以更好地管理资源.
科学领域:
- 云计算 云计算 云计算 云计算
- 资源管理 资源管理
- 机器学习 机器学习
背景情况:
- 云数据中心在能源消耗和资源优化方面面临着挑战.
- 目前的虚拟机整合策略依赖于即时的工作负载波动,导致效率低下和服务级别协议 (SLA) 违反.
- 现有的时间序列和机器学习模型难以准确预测动态云工作负载.
研究的目的:
- 通过使用预测模型,为云数据中心引入一种新的整合策略.
- 通过准确预测未来的工作负载及其趋势,增强资源管理.
- 为了减少能源消耗,性能下降和SLA违规.
主要方法:
- 开发混合价值趋势生成对抗网络 (VTGAN) 模型,以预测资源利用和工作负载趋势.
- 拟议的VTGAN方法的模拟使用真实的PlanetLab工作负载在Cloudsim.
- 与基线算法 (THR-MMT-PBFD) 相比,VTGAN策略 (基于值和趋势) 的比较.
主要成果:
- 与THR-MMT-PBFD相比,VTGAN (更新当前和预测趋势) 方法将SLA违规减少了79%,虚拟机迁移减少了56%.
- 将VTGAN集成到VM放置算法中,以避免预测过载的主机进一步提高性能.
- 除了预测过载的服务器,与THR-MMT-PBFD相比,SLA违规减少了84%,VM迁移减少了76%.
结论:
- 使用VTGAN模型进行预测工作量和趋势分析,为云资源管理提供了一种优越的方法.
- 拟议的战略通过最大限度地减少不必要的迁移和SLA违规,显著提高了数据中心的效率.
- VTGAN模型为优化云资源利用和能源消耗提供了强大的解决方案.
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