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

Abstract

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)
PublisherIEEE
Pages217-221
Number of pages5
ISBN (Electronic)9781538646335
DOIs
Publication statusPublished - 13 Sep 2018
Event4th IEEE Conference on Network Softwarization and Workshops 2018 - Montreal, Canada
Duration: 25 Jun 201829 Jun 2018

Conference

Conference4th IEEE Conference on Network Softwarization and Workshops 2018
Abbreviated titleNetSoft 2018
CountryCanada
CityMontreal
Period25/06/1829/06/18

Fingerprint

Ontology
Data structures
Semantic Web
Quality of service
Switches
Engines
Software defined networking

ASJC Scopus subject areas

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

Cite this

Rotsos, C., Farshad, A., King, D., Hutchison, D., Zhou, Q., Gray, A. J. G., ... McLaughlin, S. (2018). ReasoNet: Inferring Network Policies Using Ontologies. In 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft) (pp. 217-221). IEEE. https://doi.org/10.1109/NETSOFT.2018.8460050
Rotsos, Charalampos ; Farshad, Arsham ; King, Daniel ; Hutchison, David ; Zhou, Qianru ; Gray, Alasdair J. G. ; Wang, Cheng-Xiang ; McLaughlin, Stephen. / ReasoNet : Inferring Network Policies Using Ontologies. 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft). IEEE, 2018. pp. 217-221
@inproceedings{72dcb7422802415485dfc20a303ba413,
title = "ReasoNet: Inferring Network Policies Using Ontologies",
abstract = "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.",
author = "Charalampos Rotsos and Arsham Farshad and Daniel King and David Hutchison and Qianru Zhou and Gray, {Alasdair J. G.} and Cheng-Xiang Wang and Stephen McLaughlin",
year = "2018",
month = "9",
day = "13",
doi = "10.1109/NETSOFT.2018.8460050",
language = "English",
pages = "217--221",
booktitle = "2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft)",
publisher = "IEEE",
address = "United States",

}

Rotsos, C, Farshad, A, King, D, Hutchison, D, Zhou, Q, Gray, AJG, Wang, C-X & McLaughlin, S 2018, ReasoNet: Inferring Network Policies Using Ontologies. in 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft). IEEE, pp. 217-221, 4th IEEE Conference on Network Softwarization and Workshops 2018, Montreal, Canada, 25/06/18. https://doi.org/10.1109/NETSOFT.2018.8460050

ReasoNet : Inferring Network Policies Using Ontologies. / Rotsos, Charalampos; Farshad, Arsham; King, Daniel; Hutchison, David; Zhou, Qianru; Gray, Alasdair J. G.; Wang, Cheng-Xiang; McLaughlin, Stephen.

2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft). IEEE, 2018. p. 217-221.

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

TY - GEN

T1 - ReasoNet

T2 - Inferring Network Policies Using Ontologies

AU - Rotsos, Charalampos

AU - Farshad, Arsham

AU - King, Daniel

AU - Hutchison, David

AU - Zhou, Qianru

AU - Gray, Alasdair J. G.

AU - Wang, Cheng-Xiang

AU - McLaughlin, Stephen

PY - 2018/9/13

Y1 - 2018/9/13

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85054385337&partnerID=8YFLogxK

U2 - 10.1109/NETSOFT.2018.8460050

DO - 10.1109/NETSOFT.2018.8460050

M3 - Conference contribution

SP - 217

EP - 221

BT - 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft)

PB - IEEE

ER -

Rotsos C, Farshad A, King D, Hutchison D, Zhou Q, Gray AJG et al. ReasoNet: Inferring Network Policies Using Ontologies. In 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft). IEEE. 2018. p. 217-221 https://doi.org/10.1109/NETSOFT.2018.8460050