Design and analysis of SDN, NFV, and NV topologies using Neo4J graph database

Abstract

Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Network Virtualization (NV) technologies have revolutionized the networking landscape by providing flexible, scalable, and programmable network infrastructures. Efficiently managing and analyzing these complex networks require advanced tools that can model, query, and analyze the paths and associated costs within the network. This thesis presents the development of an SDN-NFV-NV tool implemented in Django, a popular Python web framework, incorporating Neo4j, a graph database, for efficient modelling, querying, and path analysis. The tool aims to provide network administrators and researchers with a comprehensive platform to model SDN, NFV, and NV topologies, perform complex queries, and analyze paths and associated costs. The querying capabilities of the tool provide users with a flexible interface to define and execute queries against the SDN-NFV-NV network model. Users can specify search criteria based on various network attributes and traverse the graph to find desired network paths. Path analysis plays a crucial role in understanding network behavior and optimizing resource allocation. The tool incorporates shortest path algorithm, leveraging the graph database Neo4j's graph traversal capabilities, to efficiently find the shortest path and associated costs between network elements. Users can visualize and analyze these paths to gain insights into network performance and identify potential bottlenecks. The implementation of the SDN-NFV-NV tool demonstrates its effectiveness in managing and analyzing complex network infrastructures. Through Django and Neo4j integration, it provides a user-friendly interface for modelling, querying, and path analysis. The tool's modular architecture allows for future enhancements and integration with other network management frameworks.