Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network

The efficiency of the current cellular network is limited due to the imbalance between resource availability and traffic demand. To overcome these limitations, baseband units (BBUs) are deployed on virtual machines (VMs) to form a virtual pool of BBUs. This setup enables the pooling of hardware resources, reducing the costs associated with building base stations (BSs) and simplifying both management and control. However, extreme levels of server resource use within the pool can increase physical maintenance costs and impact virtual BBU performance. This study introduces an adaptive, threshold-based dynamic migration strategy for virtual BBUs within the iCanCloud framework by setting upper and lower limits on the servers’ resource usage in the pool. The proposed method determines whether to initiate a migration by evaluating resource usage on each compute node and identifies the target node for migration if required. This aims to balance server load and cut energy consumption, and also to avoid unnecessary migration because of too high or too low server load, and effectively determine the time to trigger migration and not depend only on a certain instantaneous peak of server resource utilization. This paper used a deep Q-network learning method to predict resource utilization and make an accurate migration decision based on a history dataset. Experimental results show that as compared with Kalman filter prediction and other traditional methods, this model can effectively lower the cost of VM migration by decreasing the migration time and occurrence of it to enhance overall performance while reducing energy consumption.