# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 5 Simulation Results
### Median Absolute Deviation (MAD)
For example, nodes may be categorized as: \(\bigcirc\) **Underutilized:** Nodes with a low load ratio, indicating significant available capacity. \(\bigcirc\) **Balanced:** Nodes with a load ratio within an acceptable range. \(\bigcirc\) **Overutilized:** Nodes with a high load ratio, suggesting that they are approaching or exceeding their resource limits. * **Candidate filtering:** Filter out nodes based on their classification: \(\bigcirc\) **Underutilized nodes:** These are preferred since they have the greatest capacity headroom. \(\bigcirc\) **Acceptable nodes:** Considered if no underutilized nodes are available. \(\bigcirc\) **Overutilized nodes:** Excluded from selection to prevent potential overloads. * **Selection logic:** Preferentially select the node with the lowest load ratio to ensure efficient and balanced resource utilization. If multiple nodes exhibit similar ratios, additional criteria (such as current load or historical performance) can be used as tie-breakers. This load ratio approach allows the system to make dynamic, capacity-aware decisions during node selection, promoting proactive resource management and preventing any single node from becoming a bottleneck. The proposed DQN with these statistical methods simulated for about two hours for the scenario listed above to obtain a more robust result. Figures 8-10 show the energy consumption, number of migrations, and migration time of the proposed DQN for VM migration to effectively utilize the resources with MAD, LR, and IQR. Figure 8 depicts comparative energy consumption where the DQN-based migration policy has the minimum energy consumption compared to statistical methods (LR, IQR, MAD). While Figure 9 shows comparative migrations. The DQN policy has fewer migrations compared to MAD and IQR but LR is also comparable to DQN in terms of migrations, as load ratio (LR) considers forecasted utilization patterns, which helps in selecting nodes more efficiently compared to IQR and MAD. Both LR and DQN use threshold-based triggering, which prevents unnecessary migrations due to minor oscillations. LR, however, lacks learning and with time, DQN continues to improve its decisions and becomes more adaptive. Figure 10 shows the comparative migration time, the DQN model incurs the minimum migration time while IQR and MAD incur higher migration times. Figure 10: A comparative migration time. More enhancement results will be obtained from the proposed DQN due to the following:  
* **Dynamic adaptation:** Unlike static statistical thresholds derived from historical data, the DQN continually learns and adapts to current system dynamics, making it more resilient to changes in workload patterns. * **Multivariate optimization:** DQN considers multiple performance indicators simultaneously (e.g., CPU, memory, energy, and VM count) rather than relying on a single metric. This holistic approach can optimize trade-offs between competing objectives. * **Proactive decision making:** The DQN's ability to predict future states allows for proactive adjustments in node selection, potentially preventing overload before it occurs. * **Scalability:** As the system grows in complexity, the DQN model can scale to incorporate additional features and parameters, whereas the statistical methods might require significant re-tuning. Table 4 illustrates a comparative discussion between MAD, LR, LQR, and DQN. The statistical techniques (IQR, LR, and MAD) offer straightforward, reliable baseline methodologies that are simple to apply and understand. They function well in settings with consistent workloads or with constrained computational power. They are not, however, flexible enough to change with the times or take into account a wider variety of performance indicators. On the other hand, the DQN approach performs better in a dynamic, complex setting like BBU cloud systems. It is a better option for resource migration and node selection because of its capacity to learn from current system behavior, optimize across several dimensions, and predict future system states.