# A Comprehensive Survey on Knowledge-Defined Networking
###### Abstract
A framework for identifying heavy-hitter flows using machine learning in KDNs has been investigated in [47]. ML has also been used for video flow classification in 5G KDNs [48]. However, the KDN concept is still very pre-mature, and it lacks standardization and protocols for intra-plane communication [49]. Early surveys were totally focused on SDN architecture and applications without any review of intelligent networking [50]. More recent surveys have reviewed the concept of knowledge-based networking by categorizing machine learning applications in SDN and identifying challenges in applying machine learning in SDN [51, 52]. The survey carried out in [53] collects and reviews machine learning-based SDN solutions that emphasize machine learning-based solutions, evaluation parameters, and evaluation environments. More closely related to our survey is the survey carried out in [54], which provides an overview of the KDN architecture. However, the previously mentioned work discussed more on machine learning-based applications applicable in KDN and challenges associated with those applications rather than reviewing on the whole paradigm itself. However, our survey comprehensively reviews existing protocols, languages, standards, interfaces, models, and functions related to each plane of the KDN architecture and, finally, discusses the benefits and challenges, providing design guidelines and recommendations, thus providing a complete tutorial on the knowledge-defined networking concept to the reader, with reference to the existing literature that is analyzed and discussed. The hierarchical organization of this survey is graphically illustrated in Figure 1.  
As evident from Figure 1, the rest of the paper is organized as follows: Section 2 presents a brief introduction to data, information, and knowledge (Section 2.1), then presents an overview of the KDN architecture (Section 2.2), and compares it with SDN and traditional networks (Section 2.3). Section 3 presents an introduction to knowledge plane (Section 3.1); architecture of the knowledge plane (Section 3.2); functioning of the  
Figure 1: Hierarchical organization of the KDN survey. knowledge generation plane (the plane where knowledge generation occurs using heuristic model-based methods and different machine learning techniques) is discussed in detail with reference to the existing literature (Section 3.3); knowledge representation models, rule generation techniques, and knowledge composition examples from the existing literature are discussed in the knowledge composition plane (Section 3.4); and existing knowledge and rule management and exchange protocols/languages, flow scheduling, and prioritization approaches for knowledge and rule distribution are discussed in the knowledge management and distribution plane (Section 3.5) in detail. Section 4 presents the management plane, explaining its function (Section 4.1), architecture (Section 4.2), management interfaces/protocols (Section 4.3), network-monitoring frameworks (Section 4.4), and data collection strategies and types of data collected (Section 4.5). Section 5 presents an introduction to the control plane (Section 5.1), explains sub-planes and interfaces with protocols and standardization (Section 5.2), describes control models (Section 5.3), and describes control functions (Section 5.4). Section 6 presents the data plane, first with an introduction to the data plane (Section 6.1), then presents the architecture and operation of forwarding devices (Section 6.2), and discusses different transmission media used in the infrastructure plane (Section 6.3). Section 7 presents the application plane first with an introduction to the application plane (Section 7.1), then the architecture of the application plane (Section 7.2), and, finally, different application scenarios with respect to existing literature are discussed (Section 7.3). Section 8 discusses the benefits (Section 8.1), challenges (Section 8.2), design guidelines (Section 8.3), and ongoing research (Section 8.4) of KDN in detail.