# [UOM - Free5 gc]
## 5 B5 gemini AI Components
### Novel Deep Learning Architectures
Solutions have been proposed to accelerate the DNN weight update stage using Evolution Strategies (ES) [64]. Nonetheless, solutions to accelerate the interaction with the environment remain unclear. Some proposals, such as Distributed RL or asynchronous methods, try to reduce the number of interactions between the agent and the environment in order to speed up the learning process, but the problem with these approaches is that the agent might not learn as efficiently as with a more traditional synchronous approach. From a more pragmatic perspective, we describe below some interesting use cases of GNN in the networking landscape, which can be leveraged to facilitate the deployment and management of 5G and beyond networks. To being with, some proposals have focused on modeling complex topological relationships that allow for generating useful representations to predict latency parameters such as delay, jitter, and packet loss for the different potential routes that can be considered in the transmission of data from one node of the network to another [65; 60]. The objective is to use this information to apply an optimization process that allows for the automatic planning of the routing of traffic in a network considering the different qualities of the service. Furthermore, this technology can also be applied to find the optimal links that should be placed between the different nodes of the network to reduce those latency and loss metrics. In addition, one study has used a combination of RDL and GNN to predict the optimal policy for the placement of VNFs in a dynamic network topology (i.e., nodes and links that can appear and disappear at any time), based on the resources required by the VNFs (e.g., memory and CPU) and certain QoS constraints (e.g., latency, bandwidth, packet loss, etc.) imposed for each packet flow [66]. In this context, due to the intrinsic dependence on the specific network structure of RDL-based methods, the incorporation of GNN helps to achieve generalization across network topologies, which is crucial to address the dynamic and heterogenous nature of 5G and beyond networks. On the other hand, another study proposed to use a Spatio-Temporal Graph Convolutional Network (ST-GCN) over the data plane of an SDN to map the network into a graph structure [67]. In this way, the ST-GCN can learn from certain features of the packet flows to generate a network representation that is fed to a DNN to obtain a classification by flow (normal or attack). The method can also provide information about the path that DDoS attacks follow through the network, facilitating DDoS mitigation. In another study, a GCN with Gated Recurrent Unit (GRU) cells was used to predict the state of links in a network [68]. In particular, the GCN was used to learn features of the network topology, while the GRU was used to model the temporal dependencies between link states. The model was trained with a dataset of link state characteristics (bandwidth, delay, and packet loss rate) and was able to predict the state of links in the network with lower error rates than other state-of-the-art methods such as LSTM-based models. Finally, the deployment and chaining of VNFs is a highly complex problem that can be formulated as a graph optimization problem. Some studies have proposed using a GNN to model the problem as a graph and find the optimal deployment of VNFs and the optimal sequence of VNFs for a given service request chain (SRC), while also considering the energy consumption of VNFs in the problem formulation [69; 70; 71; 72; 73]. In fact, the complex management and orchestration processes involved in 5G/6G networks are susceptible to being represented as graphs, opening up the possibility of applying GNNs to provide a high degree of intelligence to automate these processes.