RAPMiner: A Generic Anomaly Localization Mechanism for CDN System with Multi-dimensional KPIs

As essential work in IT operations, anomaly localization, aiming to identify the affected scope of Internet infrastructure once an anomaly alarm occurs, is challenging due to the huge search space. The existing solutions usually show limited performances in the CDN scenario since they take the desirable assumptions that do not match with the practical anomaly pattern features. To address this issue, in this paper, we propose RAPMiner, which first uses a classification power-based redundant attribute deletion to prune the non-root cause attribute combinations, and then adopts an anomaly confidence-guided layer-by-layer top-down search to avoid searching for anomaly but non-root patterns. Both of them are effective in narrowing the search space. Experimental results show that RAPMiner can achieve comparable performance with the SOTA approach on the published Squeeze dataset according to F1-score and efficiency, as well as the best RC@k with stable parameter sensitivity on the RAPMD.