Pre-emptive network fault detection using analytics and machine learning

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Using analytics and machine learning to help predict faults, minimize downtime, improve user satisfaction.

the story

Our client, one of the largest European telecom service providers, depended on time-consuming and challenging fault detection methods and localization of network-related issues. It severely impacted the overall user experience and productivity and led to inefficient utilization of resources. Nagarro was onboarded to build a fault detection and localization framework using data analytics and AI/ML models. The solution allowed the client to pre-empt faults and take preventive action, thereby maintaining a high quality of service with no downtime.

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challenge

The existing fault localization & root cause analysis was reactive and required a manual analysis of log files & alarms after fault occurrence. The localization of the problem proved very complex and resulted in significant downtime.

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solution

By leveraging analytics and machine learning, we developed a pre-emptive fault detection platform that ingests different datasets like logs, SNMP traps, PM/FM counters, etc., in both batch and stream forms from multiple network elements. The machine learning models detect anomalies and predict faults after filtering and cleansing the data using data engineering principles.

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outcome

The solution enables the client to maintain a high quality of service and minimize SLA violations, resulting in greater user satisfaction. It also provides dashboards to depict the health of various network elements and to monitor the identified KPIs.

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