Energy-Efficient Cloud Processing for Real-Time Packet Scheduling in Open Radio Access Network

This paper addresses energy-efficient processing in the Open Radio Access Network (O-RAN) within the O-RAN Cloud (O-Cloud) infrastructure, focusing on optimal processor power scaling while scheduling packets under delay constraints. Traditionally, real-time packet scheduling tends to be suboptimal, as it prioritizes processing capacity over energy efficiency. To address this issue, we formulate a Mixed Integer Programming (MIP) model and develop a heuristic approach, along with a Convolutional Neural Network(CNN) for real-time scheduling. To enhance the CNN model, we introduce the CNN Binary Search Scheduling (CNN-BSS) algorithm, guided by the heuristic solution. Simulations show a 29.17 % reduction in energy consumption, significantly outperforming conventional methods.