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Integrating AI agents in SDL in Nokia SDM

  • Unique Paper ID: 177947
  • Volume: 11
  • Issue: 12
  • PageNo: 2471-2473
  • Abstract:
  • This study introduces an AI-enhanced framework for Nokia's Shared Data Layer (SDL), boosting its ability to handle subscriber data within cloud-native telecom networks. By incorporating AI agents into SDL, the system gains intelligent diagnostics, predictive fault management, autonomous scaling, and proactive service assurance. These improvements are essential as telecom networks advance to accommodate extensive IoT and 5G applications. The suggested model showcases enhanced reliability, scalability, and operational efficiency through the application of real-time telemetry, centralized logging, and intelligent decision-making. Core Methodologies: Integrating AI agents into the Nokia SDL using a hybrid methodology that combines Saas and AI-driven intelligent systems can improve the performance, resilience, reliability and scalability of Nokia SDL. Embedding of AI agents into all the components can enhance the cross-component intelligence sharing, automated testing-deployment and revenue optimization analytics by analyzing the components usage metrics. AI agents can be placed throughout the SDL facilitate automatic recovery procedures, defect prediction, anomaly detection, and real-time diagnostics. They can also learn from telemetry and log data to adjust to network activity and ensure fault-free deployment cycles. Machine learning algorithms can be used to identify monetization options and plan proactive maintenance and resource balancing across SDL modules so that it can boost the overall performance of the SDL. Performance Insights: Embedding of AI agents into SDL modules enhances system intelligence and economic potential in Saas.They provide high availability and uptime, enhanced fault tolerance, dynamic scalability on demand for the customers, improved operational efficiency in the SDL, and business-centric optimization for the customers. AI-driven customizations and behavioral analytics of the user can lead to higher Average Revenue Per User (ARPU).Additionally, proactively helping customers in service suggestions and predictive churn models for FM increase customer loyalty and satisfaction. These advancements can be achieved through AI agents working in real-time traffic prediction, through regular resource usage diagnostics updates, and proactive service suggestions.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 2471-2473

Integrating AI agents in SDL in Nokia SDM

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