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• W4285280658 abstract "Continuity and steadiness are vital for services with massive users, which requires the anomalies of services should be detected and resolved in a timely manner. Our previous work proposed a tool, namely <i>ImpAPTr (Impact Analysis based on Pruning Tree)</i> , to identify the combination of multiple dimensional attributes as the clues leading to the root cause of service anomalies. However, <i>ImpAPTr</i> applies a threshold driven strategy, i.e., it needs to be triggered by a <inline-formula><tex-math notation=LaTeX>$geq 0.05%$</tex-math></inline-formula> drop of the success rate of the service calls (abbr. <i>SRSC</i> ), which may face problems in an atypical yet pervasive situation in field application. For example, the combination of trivial anomalies (i.e., each causes a drop less than 0.05% to <i>SRSC</i> ) can lead to a far more than 0.05% drop on <i>SRSC</i> . Besides, a suitable threshold is usually hard to be determined, etc. To address these problems, we propose a new method, namely <i>ImpAPTr+</i> in this paper to free the constraint of the 0.05% threshold. The basic idea is to involve time dimension and identify clues across multiple time intervals of data. We performed evaluation on three typical methods (i.e., <i>ImpAPTr+</i> , <i>R-Adtributor</i> and <i>Squeeze</i> ) with both production environment dataset and simulation dataset. The former dataset is directly retrieved from the service monitoring data in <i>Meituan</i> , one of the largest on-line service providers worldwide. The latter dataset is fabricated also using the monitoring data from the same company. The results indicate: (1) <i>ImpAPTr+</i> outperforms previous approaches to a large degree in terms of accuracy. (2) Both <i>ImpAPTr+</i> and <i>R-Adtributor</i> are able to find proper clues within seconds. (3) <i>ImpAPTr+</i> tends to find proper clues with shorter time intervals (i.e., less data), which implies that the method is more suitable for near real-time monitoring scenarios." @default.
• W4285280658 created "2022-07-14" @default.
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• W4285280658 date "2023-07-01" @default.
• W4285280658 modified "2023-09-28" @default.
• W4285280658 title "Locating Anomaly Clues for Atypical Anomalous Services: An Industrial Exploration" @default.
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• W4285280658 doi "https://doi.org/10.1109/tdsc.2022.3181143" @default.
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