论文标题
部分可观测时空混沌系统的无模型预测
AFETM: Adaptive function execution trace monitoring for fault diagnosis
论文作者
论文摘要
高跟踪开销，选择痕量点所需的前前努力以及缺乏有效的数据分析模型是当今采用内部组件跟踪以进行故障诊断的重要障碍。本文通过结合自适应功能水平的动态跟踪，目标故障注入和图形卷积网络来介绍一种新颖的故障诊断方法。为了实现此方法，我们介绍了（i）选择功能级别跟踪点，（ii）使用自适应跟踪时构建程序的近似函数呼叫树，以及（iii）使用故障注入活动构建图形卷积网络。我们使用由REDIS，NGINX，HTTPD和SQLITE组成的Web服务基准评估我们的方法。实验结果表明，此方法在故障诊断，开销和性能对诊断目标的准确性方面优于基于日志的方法，完整的跟踪方法以及高斯影响方法。
The high tracking overhead, the amount of up-front effort required to selecting the trace points, and the lack of effective data analysis model are the significant barriers to the adoption of intra-component tracking for fault diagnosis today. This paper introduces a novel method for fault diagnosis by combining adaptive function level dynamic tracking, target fault injection, and graph convolutional network. In order to implement this method, we introduce techniques for (i) selecting function level trace points, (ii) constructing approximate function call tree of program when using adaptive tracking, and (iii) constructing graph convolutional network with fault injection campaign. We evaluate our method using a web service benchmark composed of Redis, Nginx, Httpd, and SQlite. The experimental results show that this method outperforms log based method, full tracking method, and Gaussian influence method in the accuracy of fault diagnosis, overhead, and performance impact on the diagnosis target.
