International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF
Volume 73 | Issue 8 | Year 2025 | Article Id. IJCTT-V73I8P103 | DOI : https://doi.org/10.14445/22312803/IJCTT-V73I8P103

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

Harikishore Allu Balan, Bikash Agarwal

Received Revised Accepted Published
13 Jun 2025 20 Jul 2025 13 Aug 2025 30 Aug 2025

Citation :

Harikishore Allu Balan, Bikash Agarwal, "AI/ML-Driven IP Multimedia System (IMS) Application Scaling and Auto Tune Config for Telco Networks Operating in Cloud Platforms," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 8, pp. 15-24, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I8P103

Abstract

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.

Keywords

IP Multimedia System (IMS), Voice over IP (VoIP), Video over IP, Rich Communication Services (RCS), Cloud Platforms, Dynamic Scaling, Predictive Analytics, Machine Learning (ML), Random Cut Forest (RCF), XGBoost, K-Nearest Neighbors (KNN), R² (Coefficient of Determination), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), SIP Error Codes, Call Success Rate, Call Drop Rate, Registration Success Rate, Scam Call Detection, Voicemail Frequency, Network Congestion, Latency Forecasting, CI/CD Deployment Pipelines, Adaptive Network Configuration, Automated Resource Management component, formatting, style, styling, insert (key words). 

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