论文标题
恢复命令生成SEQ2SEQ学习ICT系统中自动恢复的生成
Recovery command generation towards automatic recovery in ICT systems by Seq2Seq learning
论文作者
论文摘要
随着ICT系统的规模和复杂性的增加，它们的操作越来越多地需要自动从故障中恢复。尽管已经有可能自动检测异常情况并通过当前方法分析失败的根本原因，但要决定要执行哪些命令以从失败中恢复的命令仍然取决于手动操作，这非常耗时。为了自动恢复，我们提出了一种使用神经网络模型Seq2Seq估算恢复命令的方法。该模型学习了从过去执行的设备和恢复命令获得的日志之间的复杂关系。当发生新的故障时，我们的方法估计了根据收集的日志从故障中恢复的合理命令。我们使用合成数据集和现实的OpenStack数据集进行了实验，表明我们的方法可以高精度估算恢复命令。
With the increase in scale and complexity of ICT systems, their operation increasingly requires automatic recovery from failures. Although it has become possible to automatically detect anomalies and analyze root causes of failures with current methods, making decisions on what commands should be executed to recover from failures still depends on manual operation, which is quite time-consuming. Toward automatic recovery, we propose a method of estimating recovery commands by using Seq2Seq, a neural network model. This model learns complex relationships between logs obtained from equipment and recovery commands that operators executed in the past. When a new failure occurs, our method estimates plausible commands that recover from the failure on the basis of collected logs. We conducted experiments using a synthetic dataset and realistic OpenStack dataset, demonstrating that our method can estimate recovery commands with high accuracy.
