AFETM：用于故障诊断的自适应函数执行跟踪监控
AFETM: Adaptive function execution trace monitoring for fault diagnosis
摘要：
高跟踪开销、选择跟踪点所需的前期工作量以及缺乏有效的数据分析模型是当今采用组件内跟踪进行故障诊断的重大障碍。提出了一种自适应功能级动态跟踪、目标故障注入和图卷积网络相结合的故障诊断新方法。为了实现这种方法，我们介绍了技术（i）选择功能级跟踪点，（ii）构建近似的函数调用树时，使用自适应跟踪，和（iii）构建图卷积网络与故障注入活动。我们使用由Redis，Nginx，Httpd和SQlite组成的Web服务基准测试来评估我们的方法。实验结果表明，该方法在故障诊断的准确性、系统开销以及对诊断目标的性能影响等方面均优于基于日志的方法、全跟踪方法和高斯影响方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
