Building a Digital Twin Network of SDN Using Knowledge Graphs

Abstract

In SDN-based networks, contextual understanding of network behaviour and safe execution of operational decisions are important as part of the network management process. Digital Twin Network (DTN) is a promising concept which creates a virtual twin of a live SDN-based network to monitor the network from different angles with various granularities and enables it to verify an operational change without disturbing the live one. However, modelling the arbitrary decisions by an SDN Controller to form a DTN and managing the contextual information in a DTN are challenging tasks in various aspects. This paper proposes a data representation based DTN architecture integrating Knowledge Graph (KG) for data modelling and storage and Template as the context description approach. The combination of KG and Template can make the DTN management scalable with the flexibility of defining the contextual information with the relationships between network entities. It also enables efficient querying of the data storage and provides the reusability of functions and data storage for the DTN applications development. The proposed DTN architecture was implemented using an ONOS-based SDN Controller and Neo4j-based KG with built-in DTN applications for practical use. The PoC implementation exhibited short query response time and high query throughput in reading from and updating a KG, though the initial creation of a KG incurs a considerable delay which increases with its size.

Keywords

• Digital twin
• digital twin networks
• knowledge graphs
• software defined networks (SDN)
• templates