# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 2 Related Works
(2018) indicate the optimum method to address strict SFC routing policy, the efficient management of VNF usability, and the identification of an algorithm that works efficiently in specific scenarios. The study in [PERSON] et al. (2018) demonstrated deep reinforcement learning (DRL), a subsection in machine learning frameworks. The authors of [PERSON] et al. (2018) explained the on-demand routing mechanism in wide area networks (WANs) of 6G. The specific DDPG was used to determine the routing action from the study. Ref. [PERSON] et al. (2018) included various factors to improve the routing action by computing the state via the routing agent. The optimal routing path was computed from the reward values learned from the routing agent that collects delay, jitter, and loss ratio. Each parameter was multiplied with weightage values generated from the links' congestion, thus significantly reducing the delay, jitter, and loss ratio. Since traffic engineering-based studies contribute to developing a state-aware infrastructure, highlighting Intent-Based Networks (IBNs) is crucial for developing the 6G network. Several ML and SDN NFV-related studies have influenced a few aspects, such as the IBN innovation, the model developed from the learning process, and the dynamic configuration ability from the SDN capability. This outcome enables the infrastructure to possess real-time configuration, dynamic network topology adaptation, and efficient management [PERSON] et al. (2018). A study by [PERSON] et al. assessed the ML, ML orchestrator, and pipeline to address the optical communication parameters in enabling network automation and IBN architecture [30]. Generally, IBNs are pivotal for producing a distributed architecture [16]. An ML pipeline is necessary at a network edge to make decisions, which involves evaluating the network condition and implementing SDN-based actions. Thus, [PERSON] et al. classified the automation of optical networks that managed and assigned light paths or channels in optical links regarding traffic behavior [30]. Notably, the study emphasized network adaptation enablement, where it monitors the network and reacts to maintain the threshold of the network's performance set by the network operator. Conversely, the study focused on traffic capacity variations and predicted link accommodations by ML and SDN regardless of traffic classification. Thus, this study complements the numerous methods with a novel approach to splitting traffic based on its classifications, assigning each to appropriate transport network technologies. Subsequently, the applicability of using IBN with an ML pipeline was explored. The ML pipeline produced a traffic classification output, and the MILP generated path selection decisions for the traffic in the heterogeneous transport network. A specific transport network technology type was also selected in this study, and the detailed properties of each transport network technology were applied in the learning steps. The intelligent mechanisms were aligned and feasible with the IBN architecture, and the ML enabled the orchestrator to use a suitable model. Therefore, this process computed a proper configuration for the network.