通过基于信任的访问控制实现云资源优化:一种新的ML策略,以提高性能
Bala Subramanian C1, Bharathi St1, Shanmugapriya S1
1Computer Science and Engineering, Kalasalingam Academy of Research and Education, Srivilliputhur, India.
本研究介绍了AdaPCA,这是云资源管理的新方法. AdaPCA增强了基于信任的访问控制和资源分配,实现了99.8%的准确性,降低了延迟.
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
- 网络安全 网络安全
背景情况:
- 云计算的增长需要智能,快速和安全的资源管理.
- 高维度的信任数据给传统系统带来了挑战.
- 像决策树,随机森林和梯度提升这样的现有方法都有局限性.
研究的目的:
- 引入AdaPCA,这是一个结合AdaBoost和PCA的新型混合方法.
- 加强基于信任的访问控制和云资源分配决策.
- 为了保持最小的计算负担,同时提高系统性能.
主要方法:
- 开发了AdaPCA,将AdaBoost的自适应能力与PCA的维度减少相结合.
- 进行模拟,将AdaPCA与决策树,随机森林和梯度提升进行比较.
- 基于执行时间,资源利用,延迟和信任准确度评估性能.
主要成果:
- AdaPCA实现了99.8%的信任评分预测准确度.
- 证明了95%的资源利用效率.
- 将云资源分配时间缩短到140毫秒.
- 在所有评估参数中表现优于基准模型.
结论:
- 在云资源管理方面,AdaPCA提供了卓越的性能,包括加速决策和优化利用.
- 代表了智能,安全和适应性云系统的重大进步.
- 为高效和安全的云资源管理提供可扩展的架构.
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