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Advances in Communication Network Management through Ontologies and Data Modeling

This paper explores the application of data and information models in communication network management, focusing on the representation of entities within managed environments. It defines models that provide a common language for management information and addresses the importance of ontologies as a unifying framework for understanding data. It highlights the challenges posed by heterogeneous systems that necessitate diverse data models and examines various technologies, data harmonization issues, and the semantic web's role in improving data interoperability in networking.

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Advances in Communication Network Management through Ontologies and Data Modeling

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  1. Communication – Network Management Technologies KNOWLEDGE REPRESENTATION Ontologies Rashid Mijumbi Barcelona, April 2011

  2. Data and Information Models Definition • A model is a representation of the entities in a managed environment. • Provides a common terminology for representing management information, relationships, constraints, rules, and operations to specify data syntax for a chosen domain of discourse Concrete model (for implementors) – Low Level Representation – More Details Conceptual/Abstract model for designers and operators – High Level Representation IM 1 DM 3 DM 2 DM 1

  3. Data Models (1) The “Necessary Evil” • Heterogeneity in systems makes different Data Models a necessity N Different Technologies IPsec VPN MPLS VPN MPLS – TE MPLS – QoS Translation Layer Atleast N*M translations needed Specific Device Model Specific Device Model Specific Device Model Specific Device Model Specific Device #3 Specific Device #2 Specific Device #4 Specific Device #1 M Devices

  4. Data Models (2) Problems Data harmonisation problem in Data Models Security Application Customer Name: rmijumbi Billing Application Customer Name: rashid.mijumbi Fault Management Application Customer Name: mrashid

  5. Enterprise wide managed objects define data Information Models (1) Abstraction, Data Harmonised (no Conflicts) Platform, language and protocol dictate vendor-independent possibilities Information Model 1 : N Vendor implementations dictate working implementation Standards – Based Data Model 1 : M Vendor – Based Data Model

  6. Information Models (2) Router Configuration Example Router(config)# router bgp autonomous-system Router(config-router)# neighbor { ip-address | peer-group-name} remote-as number Router(config-router)# neighbor ip-address activate DEFINING BGP PEERS CISCO • Different Languages • Different Semantics • Different programming models routing-instances { routing-instance-name { protocols { bgp { group group-name; { peer-as as-number; neighbor ip-address; } } } } } Juniper

  7. Ontologies (1)

  8. Ontologies (2) • Ontology refers to the shared understanding of some domain of interest which may be used as a unifying framework – Uschold and Gruininger (1996) • An ontology is an explicit specification of a conceptualisation. – Gruber 1993 • Ontologies offer a formal mechanism for defining an understanding of data • Ontological Commitments • Ontology Requirements: Clarity, Coherence, Extensibility, Minimal encoding bias, Minimal ontological commitment

  9. Ontology Languages • An ontology language is made up of three components • syntax, • semantics (model theory), • proof theory. • The syntax of an ontology language is itself divided into three areas • Logic lexicon, non-logic lexicon and Grammar. • By Syntax • CycL and KIF are examples of languages that support expressions in first-order logic. • By Structure • These languages use a markup scheme to encode knowledge, most commonly XML. • Ontology Inference Layer (OIL), OWL.

  10. Ontology Tools • Ontology development tools • Ontology development tools can be further distinguished as: those that are independent of an ontology language, and those that are tightly dependent on one. • Protégé, Ontolingua. • Ontology merging tools • PROMPT, Chimaera.

  11. Semantic Web (1) • A new form of web content that is meaningful to computers - Berners-Lee 2001 UsedCars Website User • <car> • <location>Hospitalet</location> • <price>€400</price> • <colour>maroon</colour> • <description>Old banger</description> • <model>Ford Escort</model> • </car> This is because computers cannot process the semantics that are associated with web content • User lives in Barcelona and wants to buy a car locally. He can afford up to £500. He wants a red car.

  12. Semantic Web (2) UsedCars Website User Mapping Service BCN Cars • Ontologies: • Define relationships: relationship between, say, a postcode, a town, a suburb, etc Wordnet Ford New Cars

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