# On End-to-End Intelligent Automation of 6G Networks
## 3 5G Orchestration: Related Works and Limitations
### Related Works
Accordingly, the authors formulated the problem of resource orchestration as a binary integer programming problem with the objective of minimizing the cost and delay of the vertical services to be hosted while meeting the capacity and delay requirements [75]. Additionally, the authors used three low-complexity algorithms, the first being a graph clustering-based algorithm, the second being a greedy distance-based algorithm, and the third being a greedy delay-based algorithm. Simulation results showed that the proposed strategies were able to determine suitable solutions that provided a good trade-off between the orchestration cost and service delay. [PERSON] et al., however, focused on the optimal cross-slice orchestration problem [76]. More specifically, the authors formulated the problem of network slice orchestration based on the service requirements and available resources as a Markov Decision Process (MDP). Accordingly, the authors aimed at finding the optimal policy for 5G networks' cross-slice admission control and resource allocation. Simulation results showed that the proposed framework successfully provided a slice-as-a-service solution that satisfied the service requirements and simultaneously maximized the provider's revenue. [PERSON] et al. also focused on the problem of multi-domain network provisioning [77]. Within this problem, the authors considered automated resource sharing and cooperative resource provisioning. To that end, they proposed an auction-based NFV orchestrator. Within this framework, inter-operator interactions and network resources' exchange are considered to be buyer/seller transactions [77]. This was motivated by the economic efficiency of such an approach, particularly given that it facilitated the automatic discovery of service chain market value and assignment of limited resources to the bidders who value them the most. Accordingly, the authors developed a distributed multi-agent deep reinforcement learning framework in which the agent learns strategic actions that maximize the service providers' (SPs) profits. Simulation results showed that the proposed framework outperformed other learning-based and learning-free methods in terms of SPs' profits [77]. [PERSON] et al. focused on the orchestration of the 5G SDN-based network slicing problem [78]. To that end, the authors proposed a hybrid metaheuristic-based solution that combined genetic algorithm (GA) and particle swarm optimization (PSO). In the proposed solution, the GA portion of the algorithm is used to update and optimize the network slices while the PSO portion is used to find the local/global optimal solution and determine the optimal network slice [78]. Accordingly, the authors chose the delay and bandwidth as key performance indicators of 5G networks and used them as part of the fitness evaluation function of the proposed hybrid GA-PSO algorithm. Experimental setup results showed that the proposed hybrid orchestration algorithm achieved good performance, particularly in high-load traffic scenarios of large-scale networks. In contrast, [PERSON] et al. focused on 5G RAN energy efficiency when developing their orchestration solution [79]. This was done since energy consumption is a major concern for 5G telecom operators, particularly given the introduction of paradigms such as RAN disaggregation, virtualization, and cloudification. To that end, the authors first formulated the problem of virtual RAN (vRAN) energy consumption optimization as an integer quadratic programming problem [79]. Additionally, the authors developed an innovative and computationally efficient energy-aware orchestrator that leveraged Lagrangian decomposition and simulated annealing algorithms. Using a real-world mobile network traffic dataset for a large metropolitan city, the authors' performance evaluation showed that the proposed energy-efficient orchestration framework achieved energy efficiency gains of up to 25% and 42% compared to state-of-the-art and traditional distributed RAN configurations, respectively.