ACL Digital Transformed Telecom Network Management with AI-Powered Anomaly Detection and Auto-Healing System
Overview
A major telecom operator struggled with manual monitoring and managing its extensive network infrastructure, including numerous servers spread across multiple locations. The traditional approach to monitoring server health, detecting anomalies, and applying fixes was labor-intensive, error-prone, and lacked comprehensive tracking. To streamline operations, improve reliability, and reduce costs, the company turned to ACL Digital for an AI-driven solution that would automate anomaly detection, predictive analysis, and self-healing capabilities.
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Challenges
Manual Monitoring
The telecom operator relied on manual methods for monitoring server health and identifying anomalies, making the process susceptible to human errors
Limited Defect Tracking
The existing defect management process was not exhaustive, leading to missed anomalies and incomplete error mapping
Inefficient Remediation
Fixing detected issues was a manual task that increased response times and operational inefficiencies
Solutions
By implementing ACL Digital’s intelligent system, the telecom operator achieved continuous network uptime, reduced operational overhead, improved anomaly detection accuracy, and laid the foundation for ongoing performance improvements.
- Automated anomaly detection through a built-in Machine Learning (ML) ability that learns about anomalies understands their patterns, analyses them, and predicts them before they occur.
- Trains neural network-based ML models on time series data, i.e., the system can predict anomalies and perform self-healing.
- Performs server health parameter check
- Runs solution scripts to auto-fix defects
- Send health status updates
- Show analytical view in Graphs/Charts of the anomaly trends, health status, etc.
- Runbook Automation
- Auto Monitoring & Reporting
- Auto Inventory Management
- Auto Provisioning
Benefits
Significant Reduction in Downtime:
The system achieved close to 100% uptime by proactively detecting and fixing anomalies before they escalated
Cost Savings
Automating anomaly detection and remediation reduced operational costs significantly, minimizing monetary losses associated with manual errors and delays
AI-Assistive Learning
The system’s AI learning process continually improved, enhancing the accuracy of anomaly predictions over time
Improved Efficiency
The system streamlines issue resolution with automatic ticket management and corrective threshold detection, reducing the time and resources spent on manual intervention
Comprehensive Monitoring and Reporting
The enhanced reporting capabilities, including detailed anomaly trend analysis and health status updates, allowed for better-informed decision-making and system management
