On the Fly Detection of Root Causes from Observed Data with Application to IT Systems
CoRR(2024)
摘要
This paper introduces a new structural causal model tailored for representing
threshold-based IT systems and presents a new algorithm designed to rapidly
detect root causes of anomalies in such systems. When root causes are not
causally related, the method is proven to be correct; while an extension is
proposed based on the intervention of an agent to relax this assumption. Our
algorithm and its agent-based extension leverage causal discovery from offline
data and engage in subgraph traversal when encountering new anomalies in online
data. Our extensive experiments demonstrate the superior performance of our
methods, even when applied to data generated from alternative structural causal
models or real IT monitoring data.
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