NetEventCause: Event-Driven Root Cause Analysis for Large Network System Without Topology.

Abstract

Root cause analysis (RCA) is a crucial technique in network systems for uncovering the abnormal nodes that lead to the network alarm flood. Within private cloud network systems, the calling chains and topologies among entities, such as hosts, routes, and services, are always incomplete due to nonstandardized management. Existing topology-free RCA techniques, which rely on the casual discovery, are inapplicable when the scale of the network system is extremely large or the number of triggered alarms is sparse. This article proposes NetEventCause (NEC), an event-driven, unsupervised, and nonintrusive RCA algorithm for large network systems, where the network topology is unknown. NEC learns from historical alarm events to model the occurrences of various alarm types using a multivariate neural temporal point process (TPP). Based on the conditional intensity predicted by the learned TPP, NEC can identify the root alarms from a cascade of alarm events and locate the causal alarms of derivative alarms using the attribution method. The experimental section evaluates the NEC using both a synthetic event dataset and a large real-world dataset. The real-world dataset is exported from the Huawei Shennong Intelligent Maintenance and Operation Center (IMOC), a platform deployed at one of China's largest airports and manages over 200000 entities. Results obtained from the two datasets demonstrate that NEC outperforms most state of the art (SOTA) TPP models in modeling alarm events and surpasses general RCA methods in terms of identifying root alarms and recovering transmission chains of anomalies.