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A Dynamic Anomaly Detection Approach Based on Permutation Entropy for Predicting Aging-Related Failures

机译:一种基于置换熵的动态异常检测方法以预测衰老相关的故障

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摘要

Software aging is a phenomenon referring to the performance degradation of a long-running software system. This phenomenon is an accumulative process during execution, which will gradually lead the system from a normal state to a failure-prone state. It is a crucial challenge for system reliability to predict the Aging-Related Failures (ARFs) accurately. In this paper, permutation entropy (PE) is modified to Multidimensional Multi-scale Permutation Entropy (MMPE) as a novel aging indicator to detect performance anomalies, since MMPE is sensitive to dynamic state changes. An experiment is set on the distributed database system Voldemort, and MMPE is calculated based on the collected performance metrics during execution. Finally, based on MMPE, a failure prediction model using the machine learning method to reveal the anomalies is presented, which can predict failures with high accuracy.
机译:软件衰老是一种现象,提到了长期运行的软件系统的性能下降。这种现象是在执行期间的累积过程,这将逐渐导致系统从正常状态到失败的状态。对于系统可靠性来说是一种至关重要的挑战,以准确地预测与老化相关的失败(ARFS)。在本文中,置换熵(PE)被修改为多维多尺度置换熵(MMPE)作为新颖的老化指示器以检测性能异常,因为MMPE对动态状态变化敏感。在分布式数据库系统伏卑下的实验中设置了一个实验,并且MMPE在执行期间基于收集的性能指标计算。最后,基于MMPE,介绍了使用机器学习方法来揭示异常的故障预测模型,其可以高精度地预测失败。

著录项

  • 期刊名称 Entropy
  • 作者单位
  • 年(卷),期 2020(22),11
  • 年度 2020
  • 页码 1225
  • 总页数 18
  • 原文格式 PDF
  • 正文语种
  • 中图分类
  • 关键词

    机译:软件老化;故障预测;异常检测;机器学习;

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