# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 5 Simulation Results
### The Proposed Simulation Environment  
The iCanCloud platform, which can be used to implement various resource allocation techniques and assess strategy performance, simulates the dynamic migration strategy suggested in this paper. A laptop running Ubuntu 18 (64-bit) with an Intel Core i5-6200U CPU and 4 GB of RAM is the gear used in the iCanCloud simulation experiment. The OMNET++ platform is the foundation of iCanCloud. Thus, the iCanCloud 1.0 version is used to imitate OMNET++ 4.6.1 and INET 2.5.0 in this paper.  
The iCanCloud simulation tool was used to generate dummy data about CPU utilization, memory usage, energy consumption, migration time, and the number of migrations in a cloud environment. These data were then used to train a deep reinforcement learning (DRL) model aimed at improving the resource migration strategy. iCanCloud providesdata on CPU utilization, memory consumption, and energy usage at various times during the simulation.  
A number of virtual machines (VMs) are loaded on compute nodes, which are physical servers. A cloud manager can monitor and control the virtual BBU pool loaded on these nodes. In Figure 4, the proposed system scenario is illustrated that consists of a BBU pool (centralized baseband processing unit) where multiple virtualized BBUs (vBBUs) are hosted. Each BBU in the pool is designed as a virtual machine (VM) on compute nodes (physical servers). These VMs handle baseband processing tasks for multiple remote radio heads (RRHs). The system architecture is designed to enable dynamic migration of VMs to distribute the load and minimize power consumption. The compute nodes (physical servers) load multiple BBUs on VMs, offering efficient resource utilization. The DQN-based migration policy is employed to determine whether migration is required for load balancing. Cloud manager monitors and controls the entire virtualized BBU pool. It is in charge of monitoring server resource utilization (CPU, memory, power), deciding when to trigger migrations using a dual-threshold model (upper and lower thresholds), and finally selecting the optimal target compute node for VM migration.  
Based on the built simulation platform shown in Figure 4, two compute nodes which contain 150 BBU nodes in each of them and every BBU node contains three Virtual Machines. The allocation of processing power per virtual machine is based on 1000 MIPS, 2000 MIPS, and 3000 MIPS, whereas the processing power per BBU node is based on 2000 MIPS and 4000 MIPS. The number of users has submitted tasks is 1500, which are sequentially assigned to the virtual machine. Virtual machines are first assigned to odd ordinal hosts, then assigned to even ordinal hosts. Table 2 displays the specific experimental parameters.  
Figure 4: Proposed virtual cloud BBU architecture.  
To assess the effectiveness of the proposed strategy, a comparison and analysis with existing migration and VM selection strategies are required. In this experiment, four VM selection strategies are compared against the DRL selection strategy: the Kalman filter prediction (KAL), random selection (RS), minimum utilization (MU), and maximum correlation (MC) [40].