# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 2 Literature Review  
With virtual BBUs in virtual machines (VMs), cloud environments can use cloud VM migration for efficient use of resources and balancing loads. In cloud environments, balancing loads helps minimize unnecessary use of resources through workloads redistributed between overused and underused nodes. VM selection and migration allow the best migration options between overused and underused hosts. A load balancing with resource migration strategy across several BBUs in a super base station architecture was proposed by the authors in [25]. Their suggested architecture uses a radio network controller (RNC) to facilitate resource sharing amongst BBUs. A C-BBU's resources are distributed across several radio units located in various geographic regions.  
The authors in [26] proposed an online migration of a virtual BS, focusing on: reducing data loss during virtual machine migration and minimizing the duration of service interruptions. A load migration method with threshold-based for virtual base stations, utilizing virtualization, and cloud technologies, was introduced in [27]. The authors estimated both the total service interruption duration and migration time. In [28], an energy-efficient workload was proposed to distribute the processing load across the BBU pool. To decrease migration time and improve energy consumption in C-RAN, the authors used workload scheduling with queuing-based techniques. Research in [29] proposed the live migration of containerized baseband units (BBUs) in two wireless network settings: long-term evolution (LTE) networks and long-range wide-area networks (LoRaWAN). AVM live migration method for the data center environment was put forth by the authors in [30]. Based on downtime and migration duration, the authors have examined parallel and serial virtual machine migration approaches in their work. According to their comparison, parallel migration reduces downtime more effectively. The authors of [31] suggested a VM placement and migration method for the data center based on crow search. The authors of the paper suggested a meta-heuristic load-balancing strategy to minimize resource waste and lower power consumption. A QoS-aware VM migration method was presented in [32]. Their work concentrated on lowering a server's power consumption and optimizing the use of physical resources. They employed random virtual machine migration to lower a server's power consumption and the interquartile range (IQR) approach to boost resource usage. The minimum migration time (MMT) algorithm is used by the authors of references [33; 34] to choose a virtual machine (VM). Reference [35] uses the minimum power high available capacity approach to discover new locations for virtual machines (VMs) and chooses the VM selection strategy with maximum usage for migration and VM placement method. To select the destination host with the smallest total utilization (TU), reference [36] computes TU in conjunction with CPU and RAM. In reference [37], [PERSON] et al. create a prediction model, use historical data as a training set, and forecast CPU usage for the upcoming instant. Future resource use is predicted using a fuzzy logic-based prediction in reference [38]. The threshold and time sequence prediction technique which is employed by the authors in reference [39] does not carry out migration activities right away when it is discovered that server resource consumption is beyond the threshold. It observes multiple cycles to decide whether to start the migration. A dual threshold-based baseband resource migration technique for a C-RAN architecture was also suggested by the authors in [40]. The authors highlighted an adaptive, threshold-based process for selecting and migrating VMs within the C-RAN architecture. Deep reinforcement learning (D Q-learning network) is adopted in this work as an adaptive migration trigger mechanism, taking its ability to learn and update its controlling parameters.