# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 2 Related Works
The algorithm also determined an optimum state that translated into a relocation scheme to address the bandwidth-blocking probability. The optimization scheme improved the overall traffic assignment or allocation and surpassed the traditional method. Nevertheless, the relocation policy enforced in the study was among the same optical transport network technology types. A recent growth of interest in the ML application for the traffic engineering sector has been observed. A study by [PERSON] et al. summarized the ML applicability for traffic classification with a high accuracy of classification outcomes [23]. The study concluded  
Figure 1: The use case expansion from 5G and 6G. that a more accurate type was critical for assigning and managing traffic for pertinent network applications. Thus, this classification was vital for optimizing resource allocation instead of relying on traditional methods (payload and port-based). Considering that the 6G transport network encompasses heterogeneous technologies, numerous ML techniques have been applied to various network circumstances concerning path and node selection. This process necessitates the efficient management of heterogeneous transport networks. Another study by [PERSON] et al. created an environment containing wired ethernet and satellite links [PERSON] et al. (2018). The study used this environment to experiment with the end-to-end traffic engineering application in a 5G environment. Better network visibility was presented when SDN was integrated into wired and satellite links, enabling routing computation and path restoration. The SDN in the infrastructure assisted in populating path options and determining the desired traffic path. The study also showcased the SDN in implementing traffic engineering, enabling the selection of the most efficient route for certain traffic types. Meanwhile, the performance of the user datagram protocol (UDP) traffic transport using a satellite link was optimal within the satellite link capacity. Only a minimum traffic amount with a high-bandwidth channel and higher link capacity was permitted in the forwarding transmission control protocol (TCP) traffic path using a satellite link. Consequently, the SDN features for incorporating technology into terrestrial networks were feasible in the study. On the contrary, conducting additional traffic classification research could be advantageous to comprehensively understand resource management in an infrastructure. Recent studies in integrating satellite-terrestrial networks focused on enabling computing functions in satellite ecosystems. Ref. [PERSON] et al. (2018) focused on enabling double-edge computing functions to reduce processing time when using satellite transport networks. As a result, the study in [PERSON] et al. (2018) computes a value that determines the cache for content delivery and a deterministic value to offload tasks in both resource nodes in terrestrial and satellite. The output from [PERSON] et al. (2018) demonstrates the ability of the network to react based on data on the ground and in space for higher efficiency than conventional architecture. Thus, it enabled the reduction of processing time and delay, which are crucial for future 6G with terrestrial and non-terrestrial heterogeneity. Another study proved the use case of machine learning in Multi-Access Edge Computing (MEC) [PERSON] et al. (2018) and explored the scaling mechanism, creating a dynamic scaling capability based on demand and producing a sufficient number of virtual network function (VNF) nodes to cope and maintain the QoS. In the traffic engineering framework, the study of [PERSON] et al. (2018) demonstrated the capability to locate the optimal placement of VNF to determine highly efficient service function chaining (SFC) in a network function virtualization (NFV) environment using MILP and approximation solution. The findings from [PERSON] et al.