# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 3 The KDN Framework  
### Background on KDN  
In the last decade, the networking community has witnessed the so-called \"softwariisation\" process. As a result, networks are shifting towards increasingly programmable control planes. At the same time, a series of breakthroughs during the last decade in the ML field have marked the start of a new era of AI [4].  
In this context, KDN [5] emerges with the goal of facilitating the deployment of AI/ML techniques for network orchestration. To this end, KDN restates the concept of a knowledge plane for networks, earlier proposed in [6]. As initially proposed, the knowledge plane was a construct combining AI tools and cognitive systems to control and operate the network efficiently (e.g., via network orchestration). In this construct, the use of AI-based solutions was expected to achieve better-than-human network operation timescales and optimise the resource use in complex network environments beyond existing solutions (e.g., heuristics, analytical models).  
However, despite the much interest raised by the knowledge plane among the networking community, no prototype of this construct was built for real network deployments, mainly due to important technical limitations. According to the authors of KDN, one of the main limitations to deploy AI/ML techniques in legacy networks is that they are intrinsically distributed. Network devices have only a partial view of the network state, and their actions have an impact only on a small portion of the network--typically on their neighborhood. One example of this is routing in legacy networks, where forwarding devices are limited to select the next hop based on their local state.  
In this vein, KDN highlights the rise of two key technologies that may act as a catalyst to construct a functional and efficient AI-enabled knowledge plane: (i) the SDN paradigm, and (ii) modern network analytics techniques. First, in SDN the control plane permits the gathering of knowledge about the network state in a logically centralised entity. This may bring many advantages for modern ML-based network orchestration solutions, which can leverage the global state information to make decisions over the network as a whole. Second, data-plane devices in SDN offer improved computing and storage capabilities with respect to traditional networking equipment [7; 8]. This paves the way to develop a new breed of monitoring techniques--also known as telemetry [9]--that enable the maintenance of measurements in data-plane devices and collect timely information about the network state in a centralised platform. At this point, network analytics becomes an essential pillar to collect, structure, process, and maintain efficiently the network state information, which in real-world networks typically turns into Big Data.  
As a result, KDN proposes to leverage recent advances in the AI field--and specifically in ML [10; 11]--to construct a knowledge-based layer for efficient network orchestration in SDN-based networks. The resulting AI-empowered knowledge plane can be beneficial to efficiently implement emerging networking trends based on complex high-level operational goals, such as Intent-Driven Networking [12], or Beyond-Shannon Semanticcommunications [13]. Since it was conceived, KDN has served as a reference for a large body of literature aiming to deploy AI/ML techniques for network automation [14; 15; 16].