ReasoNet: Inferring Network Policies Using Ontologies

Charalampos Rotsos, Arsham Farshad, Daniel King, David Hutchison, Qianru Zhou, Alasdair J. G. Gray, Cheng-Xiang Wang, Stephen McLaughlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)
60 Downloads (Pure)


Modern Software Defined Networking (SDN) control stacks consist of multiple abstraction and virtualization layers to enable flexibility in the development of new control features. Rich data modeling frameworks are essential when sharing information across control layers. Unfortunately, existing Network Operating System (NOS) data modeling capabilities are limited to simple type-checking and code templating. We present an exploration of a more extreme point on SDN data modeling: ReasoNet. Developers can use semantic web technologies to enrich their data models with reasoning rules and integrity/consistency constraints, and automate state inference across layers. We demonstrate the ability of ReasoNet to automate state verification and cross-layer debugging, through the implementation of two popular control applications, a learning switch and a Quality of Service (QoS) policy engine.

Original languageEnglish
Title of host publication2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft)
Number of pages5
ISBN (Electronic)9781538646335
Publication statusPublished - 13 Sept 2018
Event4th IEEE Conference on Network Softwarization and Workshops 2018 - Montreal, Canada
Duration: 25 Jun 201829 Jun 2018


Conference4th IEEE Conference on Network Softwarization and Workshops 2018
Abbreviated titleNetSoft 2018

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Safety, Risk, Reliability and Quality


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