FTM-RCA: A Fast Two-Stage Multi-dimensional Root-Cause Analysis of Network Anomalies.

Yeqing Meng,Qianli Zhang, Xiangyu Tang, Wanhao Zhang,Jilong Wang


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Multi-dimensional Root Cause Analysis (RCA) is often applied to identify abnormal traffic patterns, i.e., localizing the abnormal combination of traffic header fields. Several techniques have been proposed recently, but they were mainly designed for smaller-scaled datasets and were not feasible in the real network due to the high computational overhead. To overcome the aforementioned limitations, we propose FTM-RCA, which accelerates RCA by breaking the analysis procedure into two stages: coarse-grained rules filtering and fine-grained localization. In the first stage, an optimized frequent itemset mining (FIM) technique called CUSC is proposed, which can detect high-volume combinations faster based on the mutual exclusion of dimension values. Experiments on CUSC show that it can speed up by 44.87% and reduce memory consumption by 21.89% compared to the best previous FIM algorithms. In the second stage, a dimension-based search method is proposed to identify the root cause combinations, which consists of two key components: 1) drill-down strategy, which utilizes Contributive Power to measure the correlation between the combination and anomaly. 2) pruning strategy, which adopts the Shannon entropy to avoid generating trivial results. As a result, the overall diagnostic time of FTM-RCA is at least 25 times faster than the previous best research while improving accuracy by an average of 21.6%. Also, our practical application in real network also illustrates the applicability of FTM-RCA.
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