Deep Reinforcement Learning-based Resource Adaptive Scheduling for Cloud Video Conferencing Systems

Authors

Lei Yan Electronics and Communications Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Shiji Zhou Computer Science, University of Southern California, CA, USA
Wenxuan Zheng Applied Math, University of California, Los Angeles, CA, USA
Jingyi Chen Electrical and Computer Engineering, Carnegie Mellon University, PA, USA

DOI:

https://doi.org/10.53469/wjimt.2024.07(06).19

Keywords:

Deep Reinforcement Learning, Cloud Computing, Video Conferencing, Resource Scheduling

Abstract

This paper presents a new deep learning-based resource scheduling algorithm for online video chat. The framework addresses the issues of resource allocation efficiency and service efficiency in MCU environments. A comprehensive system architecture is designed, incorporating a unified resource pool and intelligent scheduling mechanisms. The deep reinforcement learning model employs an actor-critic network structure with custom-designed state space and reward functions optimized for video conferencing workloads. The framework uses adaptive resource allocation and load balancing techniques to ensure stability in heterogeneous systems. The experimental results show a significant improvement over traditional methods, achieving a 35.2% reduction in response time, a 28.7% increase in resource utilization, and a 23.5% improvement in performance. bandwidth. The system maintains consistent performance under high loads of up to 1000 users at the same time while ensuring 99.99% service. The solution provides a flexible and powerful way to control the cloud video conferencing, as well as potential applications in the delivery of large-scale business.

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Deep Reinforcement Learning-based Resource Adaptive Scheduling for Cloud Video Conferencing Systems