Energy-Aware Dynamic VNF Splitting in O-RAN Using Deep Reinforcement Learning
Esmaeil Amiri, Ning Wang, Mohammad Shojafar, Rahim Tafazolli
Abstract
This letter proposes an innovative energy-efficient Radio Access Network (RAN) disaggregation and virtualization method for Open RAN (O-RAN) that effectively addresses the challenges posed by dynamic traffic conditions. In this case, the energy consumption is primarily formulated as a multi-objective optimization problem and then solved by integrating Advantage Actor-Critic (A2C) algorithm with a sequence-to-sequence model due to sequentially of RAN disaggregation and long-term dependencies. According to the results, our proposed solution for dynamic Virtual Network Functions (VNF) splitting outperforms approaches that do not involve VNF splitting, significantly reducing energy consumption. The solution achieves up to 56% and 63% for business and residential areas under traffic conditions, respectively.