ADAPTIVE RESOURCE MANAGEMENT IN IP TELEPHONY USING AI TO IMPROVE QoS

Abstract

This paper presents a study of existing approaches to the use of artificial intelligence for adaptive resource management in IP telephony systems, which is critically important for ensuring high Quality of Service (QoS) in communication networks. The main challenges associated with resource management in IP telephony are considered, including traffic change prediction, packet prioritization, and real-time optimization of network and computational resource allocation. A classification of resources used in IP telephony systems has been defined and formalized, taking into account different network architecture levels, including computational, network, protocol, hardware, logical, energy, and security resources.

A methodology for AI-based adaptive resource management is proposed, which includes several key stages: data collection and analysis of network performance indicators, modeling and forecasting of traffic changes using machine learning algorithms, dynamic QoS parameter adjustment for different types of traffic, adaptive buffer management to reduce jitter, network anomaly detection, and real-time adaptation to network changes.

A series of experimental studies were conducted to evaluate the effectiveness of the proposed approach. A comparison of key QoS metrics before and after the application of AI models demonstrated significant improvements in such indicators as average packet transmission delay, packet loss rate, jitter, network bandwidth, and Mean Opinion Score (MOS). The obtained results confirm the feasibility of using AI-based adaptive resource management to enhance the efficiency of IP telephony systems and improve service quality.

The study also highlights the potential for further refinement of AI-driven approaches to resource management in IP telephony. Future research directions include the development of more sophisticated deep learning models for real-time traffic prediction, the integration of reinforcement learning techniques for autonomous decision-making in dynamic network environments, and the implementation of federated learning frameworks to enhance the scalability and adaptability of AI models across distributed telephony infrastructures. These advancements could lead to even more efficient and resilient IP telephony systems, further optimizing QoS and ensuring seamless communication in modern networks.