# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 6 Conclusions  
To enhance the use of resources for efficient use of resources and consumption of energy, C-RAN must manage processing workloads between BBUs in a pool of BBUs. In this work, a deep reinforcement learning (DRL) scheme for migration of a load between VBBU  
Figure 13: Migration time for the comparative study with time series methods. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **DQN** & \multicolumn{2}{c}{**Time Series Methods (ARIMA and SES)**} \\ \hline Energy Consumption & Learns optimal policies to reduce energy usage and balance loads. & ARIMA works well in stable settings; SES adapts to short-term changes but struggles with sudden spikes. \\ \hline Migration Time & Optimizes timing for faster and more efficient migrations. & Forecast-based migration can help, but errors may delay migrations. \\ \hline Load Variance & Dynamically balances workload, reducing resource hotspots. & Predicts future loads but lacks real-time adaptation. \\ \hline Number of Migrations & Minimizes unnecessary migrations, improving system stability. & Forecasting helps, but inaccurate predictions may cause excessive migrations. \\ \hline Computational Overhead and Implementation Complexity & Requires more processing power for training but offers long-term benefits. & Less resource-intensive but lacks adaptability. \\ \hline Decision Stability and Robustness & Learns from experience and adapts, ensuring long-term stability. & Works well in stable environments but struggles with sudden demand changes. \\ \hline Security & Can be enhanced with reinforcement learning-based anomaly detection for secure migrations. & Lacks built-in security features; relies on predefined patterns, making it vulnerable to unpredictable threats. \\ \hline Scalability & Adapts to high-dimensional data and large cloud infrastructures efficiently. & Requires re-tuning when scaling to larger environments. \\ \hline Adaptability to Dynamic Environments & Continuously learns and updates migration strategies in real-time. & Static thresholds or pre-defined patterns may not adjust well to sudden demand shifts. \\ \hline Implementation Complexity & More complex due to deep learning models but highly effective. & Easier to implement but may require frequent adjustments. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparative analysis between DQN vs. SES and ARIMA. nodes, evaluation of server uses in a pool of BBUs, and migration at predefined thresholds was analyzed and modeled. To prevent excessive consumption of energy through excessive VM migration in a pool of BBUs, dynamic migration selects VMs in overloaded hosts with minimum migration count and shortest migration duration. Energy consumption in a pool of BBUs is reduced through an adaptive dynamic migration scheme. The proposed deep Q-network (DQN)-based migration mechanism revolutionizes cloud radio access network (C-RAN) management by intelligently optimizing VM migrations, minimizing energy consumption, and improving load balancing. Unlike traditional Kalman filtering, statistical methods (LR, IQR, MAD), and time-series forecasting (SES, ARIMA), DQN learns dynamically from real-time data, adapting migration decisions proactively without static thresholds. This adaptive approach significantly reduces unnecessary migrations, enhances system efficiency, and ensures optimal resource allocation between cloud infrastructures. In addition, DQN demonstrates improved scalability, security, and robustness, making it an ideal match for modern AI-driven cloud environments. The model learns to adapt to high-dimensional data and dynamically changing workloads, where traditional methods fail. With deep reinforcement learning, this study provides an efficient, future-proof mechanism for resource utilization optimization in future wireless networks. Conceptualization, S.F.I. and D.J.K. ; methodology, S.F.I. and D.J.K. ; software, S.F.I. ; validation, S.F.I. and D.J.K. ; formal analysis, S.F.I. and D.J.K. ; investigation, S.F.I. and D.J.K.