TempOpt -- Unsupervised Alarm Relation Learning for Telecommunication Networks

TL;DR

TempOpt is an unsupervised learning method that models alarm relations in telecommunication networks to help network operators identify root alarms more accurately and efficiently, improving fault resolution processes.

Contribution

The paper introduces TempOpt, a novel unsupervised approach that overcomes limitations of existing temporal dependency methods for alarm relation learning in networks.

Findings

TempOpt outperforms existing temporal dependency methods in alarm relation quality.

Experiments on real-world datasets validate the effectiveness of TempOpt.

TempOpt enhances root alarm identification accuracy in complex network environments.

Abstract

In a telecommunications network, fault alarms generated by network nodes are monitored in a Network Operations Centre (NOC) to ensure network availability and continuous network operations. The monitoring process comprises of tasks such as active alarms analysis, root alarm identification, and resolution of the underlying problem. Each network node potentially can generate alarms of different types, while nodes can be from multiple vendors, a network can have hundreds of nodes thus resulting in an enormous volume of alarms at any time. Since network nodes are inter-connected, a single fault in the network would trigger multiple sequences of alarms across a variety of nodes and from a monitoring point of view, it is a challenging task for a NOC engineer to be aware of relations between the various alarms, when trying to identify, for example, a root alarm on which an action needs to be taken. To effectively identify root alarms, it is essential to learn relation among the alarms for accurate and faster resolution. In this work we propose a novel unsupervised alarm relation learning technique Temporal Optimization (TempOpt) that is practical and overcomes the limitations of an existing class of alarm relational learning method-temporal dependency methods. Experiments have been carried on real-world network datasets, that demonstrate the improved quality of alarm relations learned by TempOpt as compared to temporal dependency method.