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

• URL: http://arxiv.org/abs/2105.03092v1
• Date: Fri, 7 May 2021 07:41:46 GMT
• Title: An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks
• Authors: Keli Zhang, Marcus Kalander, Min Zhou, Xi Zhang and Junjian Ye
• Abstract summary: In practice, accurate and self-adjustable alarm root cause analysis is a great challenge due to network complexity and vast amounts of alarms. We propose a data-driven framework for root cause alarm localization, combining both causal inference and network embedding techniques. We evaluate our method on artificial data and real-world telecom data, showing a significant improvement over the best baselines.
• Score: 7.438302177990416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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