# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 5 Simulation Results
### The Proposed Evaluation Results and Analysis  
Three indicators are taken into consideration in order to compare the performance of the suggested migration method; the energy consumption of the BBU pool, the migration time, and the total number of migrations. The four VM selection strategies--KAL, RS, MU, and MC--are contrasted with DRL-VM migration selection. The statistics techniques used to analyze the data are:  
* MAD (median absolute deviation). It could be used to identify nodes with unusually high or low resource usage compared to the median usage across the pool. * IQR (interquartile range). IQR can be used to understand the range within which the central 50% of resource usage data lies. It helps in understanding the spread of the middle portion of the data and can be useful for setting thresholds. * LR (load ratio). LR can help determine how heavily a particular resource (such as CPU, memory, or network bandwidth) is being utilized relative to its total capacity. It is a key metric for deciding when to trigger migrations to balance the load. * Thr (threshold). A threshold is a predefined value used to trigger certain actions. In the context of resource management, thresholds are typically set for metrics like CPU usage, memory usage, etc. Figures 5-7 display the simulation results. When compared to the current approach, the suggested migration selection method successfully lowers energy usage, migration duration, and migration number. Figure 5 describes the energy consumption of the proposed DQN strategy which results in much less energy consumption compared to conventional methods. DQN minimizes unnecessary migrations and optimally decides when and where to migrate VMs, reducing power consumption in the BBU pool. Figure 6 describes the number of migration occurrences of the proposed DQN strategy. The DQN model achieves a lower number of VM migrations compared to the Kalman filter and other baseline methods. The model learns from past migration events and predicts future resource utilization, preventing unnecessary migrations caused by short-term spikes in server load. Figure 7 describes the migration time for the proposed DQN strategy. The migration time is significantly lower in the DQN-based method compared to conventional methods, as DQN optimally selects the best VM and target node, leading to faster migration execution. \begin{table}
\begin{tabular}{l l l} \hline
**Parameter** & **Value** & **Unit** \\ \hline Number of BBU nodes in each compute node & 150 & - \\ \hline Number of compute node & 2 & - \\ \hline CPU capacity of the host & 2000 and 4000 & MIPS \\ \hline Memory size of the host & 4096 and 6144 & MB \\ \hline Number of VMs in each compute node & 450 & - \\ \hline CPU capacity of the virtual machine & 1000, 2000 and 3000 & MIPS \\ \hline Memory size of the virtual machine & 256, 512 and 1024 & MB \\ \hline Number of users & 1500 & - \\ \hline \end{tabular}
\end{table} 
Table 2: Proposed simulation parameters. Table 3 shows the performance of the DQN migration model compared against the Kalman filter-based approach using the following metrics; CPU utilization, memory utilization, energy consumption, and number of migrations. From the above table, it can be concluded that  
* Resource utilization: The DQN model improved by **6.2%** and memory utilization improved by **6.4%** compared to the Kalman filter. This was because DQN was better at identifying and reacting to resource usage patterns over time. * Energy consumption: DQN also resulted in a **16%** reduction in energy consumption, as it optimized the system's load balancing by triggering migrations only when necessary and consolidating resources more effectively. Figure 5: Energy consumption of the proposed DQN strategy. Figure 6: Number of migrations occurrence of the proposed DQN strategy. Figure 7: Time of migration for the proposed DQN strategy. * Migration time and number of migrations: The DQN model reduced migration time by **31%** and the number of migrations by **28%**, compared to the Kalman filter.