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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.

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概括

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.

关键词:
在 AdaBoost 中使用 AdaBoost.使用主要组件分析的AdaBoost.云计算是一种云计算.缩小尺寸的缩小方式机器学习是机器学习.主要组件分析主要组件分析资源优化 资源优化信任评价信任评价信任评价信任评价信任评价信任评价基于信任的访问控制

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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.