An Adaptive AI-Driven Sugeno Fuzzy Inference Framework for Dynamic Task Scheduling and Resource Allocation in Video Conferencing Systems

Abstract

Video conferencing systems are essential in supporting smooth long-distance cooperation where there should be minimal delays and effective use of computational and network resources. The traditional resource allocation algorithms are not able to change according to the dynamic network conditions, session time, and the variability of the device capacities and lead to contention, latency, and low Quality of Service (QoS). To cope with these issues, the proposed research is an AI-based Sugeno Fuzzy Inference System (SFIS) which is suggested to be used in flexible scheduling of tasks and distribution of resources. The model is trained and tested on the Web Camera People Behavior Dataset which is a collection of session logs, network statistics, CPU and memory utilization, and user activity logs giving a detailed system history. Preprocessing is also performed with Winsorization to help reduce the effects of the extreme outliers and Recursive Feature Elimination (RFE) is used to determine the most significant features. The scheduling model based on SFIS has good predictive performance Accuracy of 94.67%, Precision of 94.00%, Recall of 95.50%, and F1 Score of 94.74%. The analysis of the CPU and memory consumption, latency alleviation, throughput and the rate of accomplishing the tasks shows that there is a great improvement compared to the traditional strategies.