Performance Diagnosis in Cloud Microservices using Deep Learning

MicroRCA Root Cause Localization of Performance Issues in Microservices

基于MicroRCA，进一步定位S上异常的指标具体是哪个（还是只用了latency来做故障定位）

核心做法是训练一个AE，学习指标的正常模式。认为对reconstrtuction error贡献越大的指标越异常

同时这个AE还被用来辅助上一步的故障系统定位，但是没有看明白具体怎么做

We observe that applying autoencoder on the service relevant metrics can significantly improve the accuracy of culprit service localization by ranking the faulty service within the top two. Table 2 shows the overall performance of the above two methods for all anomaly cases. It shows that complementing culprit service localization with autoencoder can achieve a precision of 92%, which outperforms 61.4% than the results of CSL only.

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