计算机科学 ›› 2012, Vol. 39 ›› Issue (11): 79-82.

• 计算机网络与信息安全 • 上一篇    下一篇

一种基于自适应高斯过程的基线计算算法

杜占玮 杨永健 肖敏 白媛   

  1. (吉林大学计算机科学与技术学院 长春 130012)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Baseline Algorithm Based on Adaptive Gaussian Process Machine Learning

  • Online:2018-11-16 Published:2018-11-16

摘要: 基于自适应高斯过程技术,提出了一种计算网络主动监控中上下基线的新方法,即在满足大型服务器集群对 负载性能告警的设置与屏蔽需求下,利用样本噪音的统计特征,结合样本的数据分布,解决了样本数据的回归预测。 算法首先分析样本历史数据的噪音,通过结合蚁群算法,提出高斯过程的参数自适应机制,最后实现上下基线的计算。 实验结果表明,与其它基线计算算法相比,此算法可以在保证相同准确性的基础上,较大幅度地提高计算效率,保障网 络安全,提升网络性能和用户满意度。

关键词: 基线计算,高斯过程,机器学习,蚁群算法

Abstract: The baseline calculation is an important issue in the field of network monitoring. As to deal with the data, most researches just ignore the probability characteristics of the data,which fails to combine data distribution to predict the data and make the related processing. Therefore, this article analysed the historical data's noise first, then made the prediction with the Gaussian process machine learning, and combined with ant colony algorithm, achieved the adaptive mechanism of the parameters,and then calculated the baselines. The experiment shows that compared with other algo- rithms, our algorithm improves efficiency and accuracy.

Key words: Bascline algorithm, Gaussian process, Machine learning, Ant colony algorithm

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