# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 3 The KDN Framework
### Research Activity  
Recent advances in ML have attracted a lot of interest from the networking community, which has recently started to investigate how to build cost-efficient ML-based solutions to address network-related problems, such as routing optimisation, performance prediction, or traffic classification [10; 11]. Additionally, DL models in combination with optimisation strategies, such as reinforcement learning algorithms, have opened the possibility to efficiently solve complex decision-making and automated control problems [17; 18; 19]. In the ALLIANCE project, we have investigated how to effectively apply modern DRL methods to optimise the routing configuration in networks. In this context, previous DRL-based attempts for routing optimisation have failed to achieve good results, often under-performing traditional heuristics. In contrast to previous DRL-based solutions, in ALLIANCE we first proposed to use a more elaborate network representation that facilitates DRL agents to learn efficient routing strategies in optical networks [20], while also demonstrated the possibility to apply this network representation to other optimisation problems, such as QoS-aware routing optimisation in IP networks [21]. Second, we have proposed the use of GNNs [22] as effective tools for network modelling and optimisation (e.g., network orchestration). GNN is a recently proposed family of neural networks specifically intended to learn and generalise over graph-structured information. These models are focused on learning the relationships between different inter-connected elements in graphs, by exploiting the structure of the graph itself. As a result, they show good generalisation properties when applied to different graphs not seen during the training phase--i.e., they show strong relational inductive bias over graphs [23]. We refer the reader to [22; 23; 24] for more generic background on GNN. Earlier attempts to apply Deep Learning for network optimisation propose the use of well-known neural network models (e.g., fully connected, convolutional, recurrent neural networks, auto-encoders), which have been popularised for their outstanding applications in other fields (e.g., computer vision, natural language processing). However, these types of neural networks are not suitable for understanding and extracting deep knowledge from graph-structured data and, as a result, they show poor generalisation capabilities when applied to different network scenarios than those seen during the training phase (e.g., other topologies, routing configurations, traffic) [25]. In this context, we argue that many fundamental components in network optimisation problems involve data that is fundamentally represented as graphs (e.g., topology, routing, inter-flow dependencies) [26]. This makes GNN an especially well-suited neural network family for learning and reasoning about this network-related information. In the networking context, GNNs can be especially beneficial for global optimisation tasks, which often involve global graph-structured network state information and complex high-level optimisation goals (e.g., minimise end-to-end latency, maximise bandwidth). In general, these networking problems typically involve different network elements (e.g., routers, links, users, traffic flows) with associated state information and complex relationships between them that must be modelled and exploited to effectively pursue the global optimisation goal. In the ALLIANCE project, we have proposed RouteNet [27], a custom GNN-based architecture for network performance evaluation. This GNN model has as input: a network topology, a routing configuration, and a src-dst traffic matrix; and it produces as output accurate predictions of flow-level QoS metrics (e.g., delay, jitter, loss). A main advantage of this GNN model with respect to other traditional network modelling solutions (e.g., queuing theory, packet-level simulators) is that it can produce very accurate performance estimates at limited cost.