An Ontology-Based Information Extraction System for bridging the configuration gap in hybrid SDN environments

Hybrid Software-Defined Networks (SDNs) are growing at a remarkable speed, so network administrators need to deal with the configuration of a plethora of devices including OpenFlow elements, traditional equipment, and nodes supporting both OpenFlow and traditional features. The OpenFlow Management a...

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Bibliographic Details
Published in2015 IFIP/IEEE International Symposium on Integrated Network Management (IM) pp. 441 - 449
Main Authors Martinez, A., Yannuzzi, M., Lopez de Vergara, J. E., Serral-Gracia, R., Ramirez, W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2015
Subjects
Data models
Information retrieval
Ontologies
OWL
Protocols
Semantics
Switches
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Summary:Hybrid Software-Defined Networks (SDNs) are growing at a remarkable speed, so network administrators need to deal with the configuration of a plethora of devices including OpenFlow elements, traditional equipment, and nodes supporting both OpenFlow and traditional features. The OpenFlow Management and Configuration Protocol (OF-CONFIG) is positioned as a solid candidate for the remote configuration of OpenFlow devices, but the fact that OF-CONFIG relies on NETCONF for its transport constrains its potential considerably. Indeed, the lack of comprehensive and standardized data models has hindered the utilization of NETCONF itself in traditional networks, and will likely confine OF-CONFIG to an elementary set of configurations until the expected data models arrive. In this paper, we present a semantic-based approach that eases and automates the configuration of network devices while complementing the capabilities of OF-CONFIG and NETCONF. Our main contributions can be summarized as follows. First, we have formalized the semantics of the switch/router configuration domain using the Web Ontology Language (OWL). Second, we have developed an Ontology-Based Information Extraction (OBIE) system from the Command-Line Interface (CLI) of network devices. Third, we have defined a learning algorithm that enables automated interpretation of CLIs' configuration capabilities in heterogeneous (multi-vendor) network scenarios. The potential of our approach is demonstrated through experiments carried out on different network elements.
ISSN:1573-0077
DOI:10.1109/INM.2015.7140321