Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning

TL;DR

This paper introduces an online learning algorithm for energy-aware scheduling of virtualized base stations in O-RAN, capable of adapting to unpredictable traffic and network conditions, significantly reducing power consumption.

Contribution

It presents a novel online learning approach for vBS scheduling that guarantees performance in non-stationary environments and demonstrates substantial energy savings.

Findings

Achieves sub-linear regret in dynamic environments

Up to 74.3% power savings compared to benchmarks

Effective in non-stationary traffic conditions

Abstract

The design of Open Radio Access Network (O-RAN) compliant systems for configuring the virtualized Base Stations (vBSs) is of paramount importance for network operators. This task is challenging since optimizing the vBS scheduling procedure requires knowledge of parameters, which are erratic and demanding to obtain in advance. In this paper, we propose an online learning algorithm for balancing the performance and energy consumption of a vBS. This algorithm provides performance guarantees under unforeseeable conditions, such as non-stationary traffic and network state, and is oblivious to the vBS operation profile. We study the problem in its most general form and we prove that the proposed technique achieves sub-linear regret (i.e., zero average optimality gap) even in a fast-changing environment. By using real-world data and various trace-driven evaluations, our findings indicate savings of up to 74.3% in the power consumption of a vBS in comparison with state-of-the-art benchmarks.