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success story

Pre-emptive network fault detection

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