A framework for modelling mobile radio access networks for intelligent fault management

Postprin

An intelligent alarm management system for large-scale telecommunication companies

This paper introduces an intelligent system that performs alarm correlation and root cause analysis. The system is designed to operate in large- scale heterogeneous networks from telecommunications operators. The pro- posed architecture includes a rules management module that is based in data mining (to generate the rules) and reinforcement learning (to improve rule se- lection) algorithms. In this work, we focus on the design and development of the rule generation part and test it using a large real-world dataset containing alarms from a Portuguese telecommunications company. The correlation engine achieved promising results, measured by a compression rate of 70% and as- sessed in real-time by experienced network administrator staff

A rule induction approach to forecasting critical alarms in a telecommunication network

This paper proposes a white box method of predicting critical alarms so they can be mitigated and understood by engineers. Forecasting these alarms will avoid outages and maintain the agreed service level which is beneficial to both the provider of telecommunication services and the consumers. The paper evaluates several item set mining approaches on a set of alarms of the British Telecom (BT) national telecommunication network and proposes a novel transformation of the data to enable the discovery of patterns undetectable by current item set mining approaches. The result is a method for rule induction that predicts alarms with high precision using a wide range of features

Discovering Rules for Fault Management

. At the heart of the Internet revolution is global telecommunication systems. These systems initially designed for voice traffic provide the vast backbone bandwidth capabilities necessary for Internet traffic. They have builtin redundancy and complexity to ensure robustness and quality of service. To facilitate this, this requires complex fault identification and management systems. Fault identification and management is generally handled by reducing the amount of alarm events (symptoms) presented to the operating engineer through monitoring, filtering and masking. The ultimate goal is to determine and present the actual underlying fault. While en-route to automated fault identification it is useful to derive rules and techniques to attempt to present less symptoms with greater diagnostic assistance. With these objectives in mind computerassisted human discovery and human-assisted computer discovery techniques are discussed.