## 5 Related Work
These strategies are specifically designed for 5G-enabled SDN-based transport networks that utilize both wired and wireless components. This mechanism improves QoS. All proposed schemes in [27] incorporate linear integer programming optimization modules to recompute the available paths among OVS switches, the SDN controller, servers and user equipment (UE). The work presented in [28] focuses on addressing elephant flow conflicts mitigation within data centre networks (DCNs). To achieve this goal, the proposedapproach introduces a dynamic multipath load balancing algorithm based on Particle Swarm Optimization. The survey by [PERSON] et al. [29] provides an overview of existing load balancing techniques and outlines potential future directions within SDNs. [PERSON] et al. propose in [30] a mechanism that combines Multipath TCP with MPLS Segment Routing in an SDN network. This solution is specifically designed for multimedia streaming that meets QoE requirements in 5G networks. The proposed algorithm searches for multiple paths in the network core that satisfy latency and throughput requirements. Considering the standard architecture of 5G networks, it is uncertain where this mechanism can be located, especially since the client and server may reside in different 5G operator networks. The label stacking proposed by the authors is an effective method of packet transmission along a selected path in the network while minimizing the number of entries in the core nodes. However, this solution requires sending a Packet_IN message to the controller for each new flow. The controller, utilizing the database of possible paths, subdivides the original flow into sub-flows and installs them in the switches. The controller must maintain a vast database of existing sub-flows. This approach incurs significant signaling overhead, which is minimized when using our mechanism (as detailed in [8]). The article lacks numerical values, particularly those showing the number of entries in the ingress switch flow tables. Lack of this information prevents us from making a fair comparison between the mechanism presented in [30] and ours. We would like to highlight that for OpenFlow switches, a critical parameter is the number of entries in flow tables, as emphasized in numerous studies. The authors of [31] propose a mechanism tailored for SDN Network Virtualization (SDN-NV), enabling tenant controllers to compute paths ensuring bandwidth and multipath transmission. This solution is considered in the context of datacentre transmission. In previous articles on SDN-NV, each tenant maintained its own topology and routing, independent of others, resulting in uncoordinated resource reservation. The solution presented in [31] addresses this challenge by introducing coordination of path creation within virtual topologies, thereby facilitating bandwidth reservation. Comparing this solution with our mechanism is currently impossible, as our mechanism operates within a single topology (non-virtual). However, we recognize the potential for integrating these mechanisms. Our mechanism could be implemented within each virtual topology, treated as a tenant topology, and coordinated by the network hypervisor to establish paths in a multi-tenant environment. In our mechanism, resource reservation is easier thanks to MPLS. Such an extension of our mechanism would require intensive implementation and further performance evaluation. In [32], a dynamic multipath routing approach is proposed, utilizing reinforcement learning to allocate the path for selected services. In this method, the SDN controller utilizes a machine learning algorithm to dynamically select the routing path that meets the bandwidth, latency, and packet loss requirements. This mechanism was fully implemented and tested in a Mininet environment. However, the evaluation was performed in a very simple topology, and the results primarily focus on machine learning metrics, which are inapplicable in case of our mechanism.