Scheduling Policies in Edge Computing for Real-Time Video Analytics

This study compares different scheduling algorithms used in edge computing for real-time video processing. Instead of sending video data to cloud servers, the video is processed directly at the edge, reducing delays and saving bandwidth. The focus is on how different scheduling methods manage resources, prioritize tasks, and reduce the time it takes to analyze video. The algorithms are tested in simulated environments to understand how they impact system efficiency and performance. The goal of this work is to find the best scheduling policies for real-time video processing at the edge, improving overall system performance and scalability.