# Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme
## 2 Related Works  
The utilization of machine learning in 5G for various objectives in 5G and 6G is substantial. Machine learning improvises the past communication system that relies on mathematical models [9]. In [9], the role of machine learning in 6G networks is classified from the physical, medium access control (MAC), network, transport, and application layer. It then elaborates on the challenges in respective domains and how various machine learning algorithms could resolve the limitations. Similarly, this study is driven to resolve the resource management issue by introducing a classification method from supervised learning. The requirements in 6G are more complex and require highly efficient resource management. Ref. [10] addressed the need to improve resource management from the Virtual Network Function (VNF) management in the Service Function Chain (SFC). The unpredictable value of traffic and the static resource allocation configuration are the main contributors to the inefficient resource management faced by the telecommunication service provider. In particular, the differences in the service demand create huge variations in resource allocation. Thus, the study in [10] adopts machine learning techniques to close the gap, enabling the dynamic resource allocation for chaining the VNFs needed by the predictive method on the resource requirement. The outcome from end-to-end VNF instances instantiated in an SFC improved VNF resource allocation compared to the conventional method. A similar mission is shared from the research works in [5], where the machine learning technique is embedded to aid the traffic steering decision by predicting the congestion on the VNFs serving URLLC traffic. Studies in [4] elaborated on the research from similar abrupt traffic demand problems by utilizing machine learning techniques to predict the traffic demand and enable dynamic resource management. Specifically, the study in [4] aims to provide a guaranteed latency requirement and maximize throughput for URLLC and eMBB, respectively. It leverages the Open Radio Access Network (O-RAN) alliance platform to enable two short-term and long-term optimization solutions strategies. The short-term strategy is to resolve congestion and optimize RAN resources using inferences from historical data collected from RAN, which are learned and modeled by the machine learning process offline. Then, the long-term strategy objective is to resolve the traffic steering process, which comprises the prediction of traffic demand, bandwidth-split distribution, and flow-split variables. The findings from the study in [4] indicate the workability of using two separate time-scale learning processes adopted in this study. The data from this study undergo a series of machine learning and optimization solver processes in offline mode, potentially inferring the learning outcome into the programmed RAN/node for execution. A comparative study was done by [PERSON] et al. to demonstrate the improvement of traffic steering performance using machine learning techniques [2] against traditional methods. Ref. [2] emphasizes the advantages of Mobile Edge Cloud (MEC) in 5G architecture because MEC handles essential resource management functions like computing, storage, and networking for last-mile connected nodes. Thus, the MEC node hosts and caches enormous transactional data that are highly useful for the machine learning process. In particular,ref. [2] focused the research on manipulating machine learning techniques for traffic steering decision-making in radio access technology (RAT), specifically for scenarios with the connectivity of third-generation partnership program (3 GPPP) radio access and non-3 GPP radio access to MEC, using deep learning networks. The learning started by executing the traffic steering algorithms to recognize the network conditions. The process of learning the network conditions in [2] is used in this study, and the mission is to capture the conditions of each transport network before the subsequent classification process.