Generic and Robust Performance Diagnosis via Causal Inference for OLTP Database Systems

Lu, Xianglin; Xie, Zhe; Li, Zeyan; Li, Mingjie; Nie, Xin; Zhao, Ning; Yu, Qingyang; Zhang, Shenglin; Sui, Kaixin; Zhu, Lin; Peng, Dan

doi:10.1109/ccgrid54584.2022.00075

Cited by 7 publications

(2 citation statements)

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“…Research [18] has shown that for a particular bank in China, 80% of 82 failures over a year were detected within nine minutes, with the longest taking 19 minutes. Some methods [9], [11], [12], [15], [16], [18]- [20] require normal time series data outside the failure time window and set a lookback window that includes this normal time window.…”

Section: B Automated Fault Localizationmentioning

confidence: 99%

“…The K-S test [9], a two-sample Kolmogorov-Smirnov test (K-S test) [43], is used to detect differences between the probability distributions of these two subseries. NSigma uses the n-sigma rule of z score normalization [12]. The z score transforms the data into a standard normal distribution by using the mean and the standard deviation.…”

Section: ) Normality Reductionmentioning

confidence: 99%

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MetricSifter: Feature Reduction of Multivariate Time Series Data for Efficient Fault Localization in Cloud Applications

Tsubouchi,

Tsuruta

2024

IEEE Access

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Automated fault localization in large-scale cloud-based applications is challenging because it involves mining multivariate time series data from large volumes of operational monitoring metrics. To improve localization accuracy, automated fault localization methods incorporate feature reduction to reduce the number of monitoring metrics unrelated to a failure. However, these methods have problems with inaccuracy, either from removing too many failure-related metrics or from retaining too few failureunrelated metrics. In this paper, we present MetricSifter, a feature reduction framework designed to accurately identify anomalous metrics caused by faults. Our framework locates a failure time window with the highest density of fault-induced change point times across monitoring metrics with a focus on their temporal proximity. Experimental results indicate that MetricSifter achieves an accuracy of 0.981, which is significantly better than the selected baseline methods. Furthermore, experiments combining various reduction methods with various localization methods demonstrate that MetricSifter improves the recall and time efficiency over the baseline methods.

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