AI/ML-Driven IP Multimedia System (IMS) Application Scaling and Auto Tune Config for Telco Networks Operating in Cloud Platforms

This study explores how IP Multimedia Systems (IMS) applications can be optimized and scaled effectively within telecom networks that rely on cloud infrastructure. IMS supports a wide range of services, including Voice over IP (VoIP), Video over IP, and Rich Communication Services (RCS), and utilizes standardized components such as P-CSCF, S-CSCF, I-CSCF, TAS, RCS, and MRF in accordance with 3GPP specifications. A unified method for gathering diverse operational data has been proposed, covering performance indicators from the network, infrastructure usage, application-level behavior, and surrounding environmental factors. From this pool of data, key indicators—like call success and failure rates, scam call detection, and voicemail activity—are extracted and analyzed. To make sense of these trends, several machine learning models, including Random Cut Forest (RCF), XGBoost, and a basic K-Nearest Neighbors (KNN) approach, are used. Their predictive strength is measured using common statistical tools such as R-squared (R²), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). This evaluation helps determine which model is most suitable for anticipating specific challenges such as SIP errors, call drops, latency issues, and network congestion. Based on these predictions, the system can automatically fine-tune its settings to adapt to changing network conditions. By integrating with CI/CD pipelines, these adjustments can happen in near real-time. The end result is a responsive and cost-effective framework for managing IMS resources in cloud-based telecom environments.