Moving Metric Detection and Alerting System at eBay

Authors: Zezhong Zhang, Keyu Nie, Ted Tao Yuan

The work is oral presented on the AAAI-20 Workshop on Cloud Intelligence, 2020

Abstract: At eBay, there are thousands of product health metrics for different domain teams to monitor. We built a two-phase alerting system to notify users with actionable alerts based on anomaly detection and alert retrieval. In the first phase, we developed an efficient anomaly detection algorithm, called Moving Metric Detector (MMD), to identify potential alerts among metrics with distribution agnostic criteria. In the second alert retrieval phase, we built additional logic with feedbacks to select valid actionable alerts with point-wise ranking model and business rules. Compared with other trend and seasonality decomposition methods, our decomposer is faster and better to detect anomalies in unsupervised cases. Our two-phase approach dramatically improves alert precision and avoids alert spamming in eBay production.

Submitted to arXiv on 06 Apr. 2020

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