An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks

TL;DR

This paper presents a novel data-driven framework combining causal inference and network embedding to improve root cause alarm localization in telecom networks, addressing challenges of network complexity and alarm volume.

Contribution

It introduces a hybrid causal graph learning method (HPCI) and a Causal Propagation-Based Embedding (CPBE) algorithm, advancing root cause analysis with less expert knowledge required.

Findings

Significant improvement over baseline methods on real telecom data

Effective real-time root cause alarm discovery

Enhanced accuracy in complex network scenarios

Abstract

Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery. In practice, accurate and self-adjustable alarm root cause analysis is a great challenge due to network complexity and vast amounts of alarms. A popular approach for failure root cause identification is to construct a graph with approximate edges, commonly based on either event co-occurrences or conditional independence tests. However, considerable expert knowledge is typically required for edge pruning. We propose a novel data-driven framework for root cause alarm localization, combining both causal inference and network embedding techniques. In this framework, we design a hybrid causal graph learning method (HPCI), which combines Hawkes Process with Conditional Independence tests, as well as propose a novel Causal Propagation-Based Embedding algorithm (CPBE) to infer edge weights. We subsequently discover root cause alarms in a real-time data stream by applying an influence maximization algorithm on the weighted graph. We evaluate our method on artificial data and real-world telecom data, showing a significant improvement over the best baselines.