Knowledge Graphs for Enhanced Cross-Operator Incident Management and Network Design
draft-tailhardat-nmop-incident-management-noria-01
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| Authors | Lionel Tailhardat , Raphaël Troncy , Yoan Chabot | ||
| Last updated | 2024-08-29 | ||
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draft-tailhardat-nmop-incident-management-noria-01
Network Management Operations L. Tailhardat
Internet-Draft Orange
Intended status: Informational R. Troncy
Expires: 2 March 2025 EURECOM
Y. Chabot
Orange
29 August 2024
Knowledge Graphs for Enhanced Cross-Operator Incident Management and
Network Design
draft-tailhardat-nmop-incident-management-noria-01
Abstract
Operational efficiency in incident management on telecom and computer
networks requires correlating and interpreting large volumes of
heterogeneous technical information. Knowledge graphs can provide a
unified view of complex systems through shared vocabularies. YANG
data models enable describing network configurations and automating
their deployment. However, both approaches face challenges in
vocabulary alignment and adoption, hindering knowledge capitalization
and sharing on network designs and best practices. To address this,
the concept of a IT Service Management (ITSM) Knowledge Graph (KG) is
introduced to leverage existing network infrastructure descriptions
in YANG format and enable abstract reasoning on network behaviors.
The key principle to achieve the construction of such ITSM-KG is to
transform YANG representations of network infrastructures into an
equivalent knowledge graph representation, and then embed it into a
more extensive data model for Anomaly Detection (AD) and Risk
Management applications. In addition to use case analysis and design
pattern analysis, an experiment is proposed to assess the potential
of the ITSM-KG in improving network quality and designs.
About This Document
This note is to be removed before publishing as an RFC.
The latest revision of this draft can be found at
https://genears.github.io/draft-tailhardat-nmop-incident-management-
noria/draft-tailhardat-nmop-incident-management-noria.html. Status
information for this document may be found at
https://datatracker.ietf.org/doc/draft-tailhardat-nmop-incident-
management-noria/.
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noria.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Conventions and Definitions . . . . . . . . . . . . . . . . . 6
3. An ITSM-KG for learning and sharing network behavioral
models . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.1. Principles . . . . . . . . . . . . . . . . . . . . . . . 6
3.2. Relation to the Digital Map . . . . . . . . . . . . . . . 7
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3.2.1. Core Requirements . . . . . . . . . . . . . . . . . . 8
3.2.2. Design Requirements . . . . . . . . . . . . . . . . . 8
3.2.3. Architectural Requirements . . . . . . . . . . . . . 9
4. Strategies for the ITSM-KG construction . . . . . . . . . . . 9
4.1. From YANG-based configurations to meta-knowledge graph . 9
4.2. Implementing alignments of model-specificities to a
multi-faceted knowledge graph . . . . . . . . . . . . . . 11
4.2.1. The network of ontologies approach . . . . . . . . . 11
4.2.2. Explicit linking in the ONTO-META . . . . . . . . . . 14
4.3. Extract-Transform-Load pipelines for the ITSM-KG . . . . 15
4.3.1. Handling event streams . . . . . . . . . . . . . . . 16
4.3.2. Federated data architecture . . . . . . . . . . . . . 18
5. Experiments . . . . . . . . . . . . . . . . . . . . . . . . . 19
5.1. Experimental plan . . . . . . . . . . . . . . . . . . . . 20
5.2. Implementation status . . . . . . . . . . . . . . . . . . 21
5.2.1. NORIA . . . . . . . . . . . . . . . . . . . . . . . . 21
6. Security Considerations . . . . . . . . . . . . . . . . . . . 22
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 23
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 23
8.1. Normative References . . . . . . . . . . . . . . . . . . 23
8.2. Informative References . . . . . . . . . . . . . . . . . 23
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 26
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 26
1. Introduction
Incident management on telecom and computer networks, whether it is
related to infrastructure or cybersecurity issues, requires the
ability to simultaneously and quickly correlate and interpret a large
number of heterogeneous technical information sources. Knowledge
graphs, by structuring heterogeneous data through shared
vocabularies, enable providing a unified view of complex technical
systems, their ecosystem, and the activities and operations related
to them (see [I-D.marcas-nmop-knowledge-graph-yang] and
[NORIA-O-2024]). Using such formal knowledge representation allows
for a simplified interpretation of networks and their behavior, both
for NetOps & SecOps teams and artificial intelligence (AI) algorithms
(e.g. anomaly detection, root cause analysis, diagnostic aid,
situation summarization), and paves the way, in line with the Network
Digital Twin vision [I-D.irtf-nmrg-network-digital-twin-arch], for
the development of tools for detecting and analyzing complex network
incident situations through explainable, actionable, and shareable
models (see [FOLIO-2018], [SLKG-2023], and [GPL-2024]).
However, despite potential benefits of using knowledge graphs, these
are not mainstream yet in commercial network deployment systems and
decision support systems (see [NORIA-UI-2024] for more on the
decision support systems perspective). YANG is a widely used
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standard among operators for describing network configurations and
automating their deployment. Using YANG representations in the form
of a KG, as suggested in [I-D.marcas-nmop-knowledge-graph-yang],
would minimize the effort required to adapt network management tools
towards the unified vision and applications evoked above. The lack
of alignment between various YANG models on key concepts (e.g. for
describing network topology) is, however, hindering this evolution
[I-D.boucadair-nmop-rfc3535-20years-later].
Furthermore, although [I-D.netana-nmop-network-anomaly-lifecycle]
addresses the capitalization of incident management knowledge through
a YANG model, it can be observed that the overall scope of YANG
models does not naturally cover the description of the networks'
ecosystem (e.g. physical equipment location, operator organization,
supervision systems) or the description of network operations from an
IT service management (ITSM) perspective (e.g. business processes and
design rules used by the company, scheduled modification operations,
remediation actions performed during incident handling). As a
consequence, the continuous improvement of network quality & designs
requires additional data cross-referencing operations to properly
contextualize incidents and learn from remediation actions taken
(e.g. analyzing intervention technicians' verbatim, comparing actions
performed on similar incidents but occurring on different networks).
As a result of these additional efforts of contextualization, the
capitalization of knowledge typically remains confined at the level
of each network operator. This, in turn, hinders the sharing of
information within the community of researchers and system designers
regarding failure modes and best practices to adopt, considering the
concept of overall improvement of IT systems and the Internet.
Realizing an ITSM knowledge graph for network deployment, anomaly
detection and risk management applications has been studied for
several years in the Semantic Web community (i.e. knowledge
representation and automated reasoning leveraging Web technologies
such as [RDF], [RDFS], [OWL], and [SKOS]). Among other examples: the
DevOpsInfra ontology [DevOpsInfra-2021] allows for describing sets of
computing resources and how they are allocated for hosting services;
the NORIA-O ontology [NORIA-O-2024] allows for describing a network
infrastructure & ecosystem, its events, diagnosis and repair actions
performed during incident management. Assuming the continuous
integration into a knowledge graph of data from ticketing systems,
network monitoring solutions, and network configuration management
databases, we remark that the resulting knowledge graph (Figure 1)
implicitely holds the necessary information to (automatically) learn
incident contexts (i.e. the network topology, its set of states and
set of events prior to the incident) and remediation procedures (i.e.
the set of actions and network configuration changes carried-out to
resolve the incident).
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┌───Incident context────────────────────────────┐
│ ┌────────────┐ │
│ │skos:Concept│ │
│ └─┬┬─────────┘ │
│ <server> │
│ ▲ │
│ │ │
│ resourceType │
│ ┌────────┐ │ │ ┌─────────────┐
│ │Resource│ │ │ │TroubleTicket│
│ └──────┬┬┘ │ │ └─────┬┬──────┘
│ ││ │ │ ││
│ <ne_2>──<ne_1>◄──troubleTicketRelatedResource──<incident_01>
│ │ │ │ │
│ │ │ │ problemCategory
│<ne_5>──<ne_4>────┼──<ne_3>────<log_2> │ │
│ │ │ │ │ ▼
│ │ │ │ │ <packet-loss>
│ <log_3> │ <ne_6> │ ││
│ │ │ ┌────┴┴──────┐
│ logOriginatingManagedObject │ │skos:Concept│
│ │ │ └────────────┘
│ ▼ │
│ <log_1>──────┐ │
│ ┌─────────┴┴┐ dcterms:type │
│ │EventRecord│ │ │
│ └───────────┘ ▼ │
│ <integrityViolation> │
│ ┌────┴┴──────┐ │
│ │skos:Concept│ │
│ └────────────┘ │
└───────────────────────────────────────────────┘
Figure 1: Learning an incident signature seen as a classification
model that is trained on the relationship of the incident context
(i.e. a subgraph centered around a Resource entity concerned by a
given TroubleTicket) to the problem class defined at the
TroubleTicket entity level. Arrows are for object properties
(owl:ObjectProperty), double line edges are for object class
relationships (rdf:type).
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By going a step further, we notice that a generic understanding of
incident context can be extracted and shared among operators from
knowledge graphs. Indeed, a knowledge graph, being an instantiation
of shared vocabularies (e.g. RDFS/OWL ontologies and controlled
vocabularies in SKOS syntax), sharing incident signatures can be done
without revealing infrastructure details (e.g. hostname, IP address),
but rather the abstract representation of the network (i.e. the class
of the knowledge graph entities and relationships, such as "server"
or "router", and or "IPoWDM link").
The remainder of this document is organized as follows. Firstly, the
concept of an ITSM-KG is introduced in Section 3 towards leveraging
existing network infrastructure descriptions in YANG format and
enabling abstract reasoning on network behaviors. The relation of
the ITSM-KG proposal to the Digital Map
[I-D.havel-nmop-digital-map-concept] is notably discussed in this
section. Secondly, strategies for the ITSM-KG construction are
discussed in Section 4. This include YANG models transformation in
Section 4.1, implementing alignments of models with the ITSM-KG in
Section 4.2, and knowledge graph construction pipeline designs in
Section 4.3. The Section 4.3 notably focuses on addressing the
handling of event data streams and providing a unified view for
different stakeholders, also known as the data federation
architecture. Finally, an experiment is proposed in Section 5 to
assess the potential of the ITSM-KG in improving network quality and
designs. The implementation status related to this document is also
reported in this section.
2. Conventions and Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
3. An ITSM-KG for learning and sharing network behavioral models
3.1. Principles
As evoked in Section 1, a detailed characterization of network
behavior requires combining several facets of data related both to
the configuration of the networks and to their lifecycle, as well as
the ecosystem in which they are operated. In this document, we will
consider the following fundamental definitions as a means to achieve
the combination of all these facets of data in a convenient way,
regardless of their origin, for operational efficiency in incident
management and change management with the aid of AI tools:
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ITSM-KG: A knowledge graph in RDFS/OWL syntax tha enables change
management activities, anomaly detection, and risk analysis at the
organizational level by combining heterogeneous data sources from
the configuration data of the network's structural elements,
events occurring on this network, and any other data useful to the
business for the effective management of the services provided by
this network.
ONTO-ITSM: For a given ITSM-KG, the RDFS/OWL ontology that
structures the ITSM-KG.
ONTO-YANG-MODEL: For a given YANG model, its equivalent RDFS/OWL
representation.
ONTO-META: An ontology that contributes to structuring some ITSM-KG,
regardless of the specifics of a given application domain or ITSM-
KG instance, in the sense that it provides an abstract IT Service
Management model (i.e. it holds generic concept and property
definitions for realizing IT Service Management activities).
ONTO-LINKER: For a given (set of) ONTO-YANG-MODEL and a given ONTO-
META, the implementation of the equivalence relationships between
the key concepts and key properties of the (set of) ONTO-YANG-
MODEL and ONTO-META.
Based on these definitions, which will be discussed in more detail
later in this document, Figure 1 can be seen as an illustration of
ITSM-KG from which a subgraph has been extracted, allowing for
incident situation to be analyzed through querying. For example,
close to ideas from [I-D.netana-nmop-network-anomaly-lifecycle],
querying the evolution of network entities states from the ITSM-KG
during some incident remediation stage could bring to identify the
causal graph underlying incident resolution. As the querying would
go through the ONTO-ITSM, the causal graph would de-facto be an
abstraction of the situation, thereby enabling knowledge
capitalization and sharing for similar incidents that could occur
later.
3.2. Relation to the Digital Map
Similar to the concept of ITSM-KG discussed in this document, the
concept of Digital Map discussed in
[I-D.havel-nmop-digital-map-concept] emphasizes the need to structure
heterogeneous data describing networks in order to simplify network
management operations through unified access to this data. The ITSM-
KG can be seen as a meta-knowledge graph that extends the Digital Map
concept by adding information about the lifecycle of infrastructures
and services, as well as the context of their usage. These
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additional pieces of information are considered essential for
learning shareable activity models of systems.
To clarify this positioning, the following lists (Section 3.2.1,
Section 3.2.2, and Section 3.2.3) reflect the compliance of the meta-
KG concept with the Digital Map Requirements defined in
[I-D.havel-nmop-digital-map-concept]. A symbol to the right of each
requirement name indicates the nature of compliance: *+* for
compatibility, */* for partial satisfaction, *-* for non-compliance
with the requirement. A comment is provided as necessary.
3.2.1. Core Requirements
*+* REQ-BASIC-MODEL-SUPPORT: nothing to report (n.t.r.)
*+* REQ-LAYERED-MODEL: n.t.r.
*/* REQ-PROG-OPEN-MODEL: Partially satifying the requirement as the
concept of meta-KG mainly relate to the knowledge representation
topic rather than to the platform running the Digital Map service
on top of the meta-knowledge graph.
*/* REQ-STD-API-BASED: Same remark as for REQ-PROG-OPEN-MODEL.
*+* REQ-COMMON-APP: n.t.r.
*+* REQ-SEMANTIC: n.t.r.
*+* REQ-LAYER-NAVIGATE: n.t.r.
*+* REQ-EXTENSIBLE: Knowledge graphs implicitly satisfy this
requirement, notably with OWL [OWL] and SKOS [SKOS] constructs if
considering RDF knowledge graphs for the meta-KG (e.g. owl:sameAs
to relate a meta-KG entity to some other entity of another
knowledge graph, owl:equivalentClass to link concepts and
properties used to interpret the meta-KG to concepts and
properties from other data models, skos:inScheme to group new
items of a controled-vocabulary as part of a skos:ConceptScheme).
*+* REQ-PLUGG: Same remark as for REQ-EXTENSIBLE.
*+* REQ-GRAPH-TRAVERSAL: This capability is naturally enabled as the
meta-KG concept involves using a graph data structure.
3.2.2. Design Requirements
*-* REQ-TOPO-ONLY: Requirement not satisfied as the meta-KG involves
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to have more than topological data to interpret and contextualize
the network behavior.
*-* REQ-PROPERTIES: Same remark as for REQ-TOPO-ONLY.
*-* REQ-RELATIONSHIPS: Same remark as for REQ-TOPO-ONLY.
*+* REQ-CONDITIONAL: Native, notably considering the expressiveness
of SPARQL [SPARQL11-QL] if using the Semantic Web protocol stack
to run the meta-KG concept.
*+* REQ-TEMPO-HISTO: n.t.r.
3.2.3. Architectural Requirements
*+* REQ-DM-SCALES: This capability applies as we can use data
aggregation at the graph level (Figure 10 and Figure 11 compared
to Figure 8 and Figure 9), aggregation without loss of information
(Figure 10 and Figure 11), and load balancing (horizontal scaling)
by partitioning the meta-KG (Figure 12). Further, ease of
integration is enabled thanks to existing standard graph data
access protocols (e.g. SPARQL Federated Queries [SPARQL11-FQ], as
illustrated in Figure 12).
*/* REQ-DM-DISCOVERY: Same remark as for REQ-PROG-OPEN-MODEL.
4. Strategies for the ITSM-KG construction
In this section, we firstly define in Section 4.1 two YANG-based data
transformation scenario, namely the YANG-KG-SEMANTIC-EQUIVALENCE and
YANG-KG-SEMANTIC-GENERALIZATION scenarios. The YANG-KG-SEMANTIC-
GENERALIZATION scenario is then used as a basis in Section 4.2 to
illustrate strategies to reuse YANG models transformed in RDFS/OWL
syntax in a higher-level ontology that would structure the ITSM-KG.
Finally, two Extract-Transform-Load (ETL) pipeline approaches and a
data federation architecture are presented in Section 4.3 to meet the
needs of constructing and exploiting the ITSM-KG.
4.1. From YANG-based configurations to meta-knowledge graph
In the following, we consider the use of Semantic Web technologies as
the foundation for representing data in the form of a knowledge
graph. We also assume the ability to transform a description of
configurations and network infrastructures expressed accordingly to a
given (set of) YANG model(s) into a knowledge graph representation.
For the realization of this data transformation, we identify the
following scenarios:
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YANG-KG-SEMANTIC-EQUIVALENCE: The ontology structuring the target
knowledge graph is an exact equivalence of the many YANG models
organizing the configuration data.
YANG-KG-SEMANTIC-GENERALIZATION: The ontology structuring the target
KG is a generalization of the YANG models organizing the
configuration data.
We note that the YANG-KG-SEMANTIC-EQUIVALENCE case requires a
significant knowledge engineering effort to align all YANG models
into a coherent ontology with a sufficient level of abstraction to
enable the discovery and analysis of emergent behavioral models of
networks independently of local configuration specifics. However,
this case has the advantage of being relatively easy to implement
based on the available configuration data of an operator, for
example, by implementing [RML] rules for constructing a knowledge
graph from this data.
For the YANG-KG-SEMANTIC-GENERALIZATION case, we observe that the
transformation effort involves:
1. Being able to transform YANG models into their RDFS/OWL
equivalent to provide a consistent interpretation of
configuration data in a knowledge graph that aligns with each
data source.
2. Being able to provide a generalized interpretation of these
transformed YANG models by identifying alignments between key
concepts in these models and those in a more expressive ontology.
As an example, the YANG-KG-SEMANTIC-GENERALIZATION case could involve
wanting to integrate Service and Network topology data, matching the
Network Topologies [RFC8345] and Service Assurance [RFC9418] YANG
data models, into a knowledge graph structured by the NORIA-O
ontology [NORIA-O-2024].
Although identifying alignments in the YANG-KG-SEMANTIC-
GENERALIZATION case may appear non-trivial for "constructor" YANG
models, it is worth noting that the design of YANG models generally
relies on principles of concept hierarchies and reuse of common
concepts between models to promote model interoperability, as is the
case with the Abstract Network Model of [RFC8345]. Therefore, the
task of identifying alignments can theoretically benefit from these
design principles.
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In continuity of the above RFC8345 / NORIA-O example, providing an
alignment may mean asserting a semantic equivalence between the RDFS/
OWL representation of the "node" concept from [RFC8345] with the
"noria:Resource" concept from [NORIA-O-2024]. Examples of approaches
for linking ontologies are provided in Section 4.2.
4.2. Implementing alignments of model-specificities to a multi-faceted
knowledge graph
Building on the previously defined YANG-KG-SEMANTIC-GENERALIZATION
scenario, this section presents two approaches to construct the
structuring ontology of the ITSM-KG by combining YANG models
translated into RDFS/OWL and a meta-ontology enabling the analysis of
the operational context of the network lifecycle. As techniques for
identifying alignments between data models is beyond the scope of
this document, we refer interested readers to specialized literature
in this field, such as [ONTO-MATCH-2022].
To present the approaches, we assume the ability to convert a given
YANG model into its ONTO-YANG-MODEL (i.e. its equivalent RDFS/OWL
representation). The code snippet in Figure 2 is a fictional example
of translating the "node" concept from [RFC8345] into its RDFS/OWL
equivalent.
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
<urn:ietf:params:xml:ns:yang:ietf-network#node>
rdf:type owl:Class ;
rdfs:comment "The inventory of nodes of this network." ;
.
Figure 2: Snippet of the ONTO-YANG-MODEL describing the 'node'
concept from RFC8345 into its RDFS/OWL equivalent, in Turtle
syntax.
The following sub-sections build on the ONTO-YANG-MODEL example from
Figure 2.
4.2.1. The network of ontologies approach
The network of ontologies approach is a common practice in the field
of knowledge engineering and Semantic Web technologies. The
principle involves assembling vocabularies from different domains to
form a coherent set, for example to infer - through graph traversal
or reasoning - relationships between entities in the graph, starting
from a concept defined in one of the vocabularies and leading to an
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instance of a concept from another vocabulary.
In our example, the code snippet of Figure 3 implements the ONTO-ITSM
by importing concepts from the ONTO-YANG-MODEL (Figure 2) and
concepts from the ONTO-META (Figure 4). An additional import in
Figure 5 relates to the ONTO-LINKER.
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
<https://example.com/ontologies/itsm/>
rdf:type owl:Ontology ;
owl:imports
# ===> Import of one of the ONTO-YANG-MODEL <===
<https://example.com/ontologies/ietf-network-topology> ,
# ===> Import of the ONTO-META <===
<https://w3id.org/noria/ontology/> ,
# ===> Import of the ONTO-LINKER definitions <===
<https://example.com/ontologies/ietf-noria-linker> ;
.
Figure 3: The implementation of the ONTO-ITSM to structure the
relation of ONTO-YANG-MODEL(s) with ONTO-META, in Turtle syntax.
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@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix seas: <https://w3id.org/seas/>. # Smart Energy Aware Systems
@prefix bot: <https://w3id.org/bot#> . # Building Topology Ontology
@prefix observable: # Unified Cybersecurity Ontology (UCO)
<https://unifiedcyberontology.org/ontology/uco/observable#> .
@prefix log: <https://w3id.org/sepses/ns/log#> . # a.k.a. SLOGERT
@prefix noria: <https://w3id.org/noria/ontology/> .
noria:Resource
rdf:type owl:Class ;
rdfs:label "Resource" ;
rdfs:comment """General resource record of the Communication Device
kind from the logistics park. It is a managed entity that can be
either Physical or Virtual."""@en ;
rdfs:subClassOf noria:StructuralElement ;
rdfs:subClassOf
seas:System,
seas:CommunicationDevice,
bot:Element ,
observable:Device ,
log:Host ;
rdfs:isDefinedBy noria: ;
.
Figure 4: Snippet of the ONTO-META describing the
'noria:Resource' concept from NORIA-O v0.3, in Turtle syntax.
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix noria: <https://w3id.org/noria/ontology/> .
noria:Resource
owl:equivalentClass <urn:ietf:params:xml:ns:yang:ietf-network#node> ;
.
Figure 5: Snippet of the ONTO-LINKER to relate ONTO-YANG-MODEL
definition(s) with ONTO-META definition(s), in Turtle syntax.
As a result, querying any ITSM-KG structured by the ONTO-ITSM, as
shown in Figure 6, enables retrieving entities of the ITSM-KG using
ONTO-META concepts, even if entities are described with ONTO-YANG-
MODEL concepts.
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PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX noria: <http://data-noria.securite.fr.intraorange/ontology/>
SELECT ?res
WHERE {
# Pattern for the base class from ONTO-META
# or any equivalent class from ONTO-YANG-MODEL
?resClass (owl:equivalentClass|^owl:equivalentClass)* noria:Resource .
# Pattern to retrieve instances from the ITSM-KG
?res rdf:type ?resClass .
}
Figure 6: Snippet to retrieve entities of the ITSM-KG assuming
the relatedness of ONTO-META concepts with ONTO-YANG-MODEL
concepts, in SPARQL syntax.
4.2.2. Explicit linking in the ONTO-META
In this approach, we assume that we have the means to evolve ONTO-
META, which allows for the implementation of equivalence
relationships between the concepts of ONTO-META and ONTO-YANG-MODEL
directly within ONTO-META, as shown in Figure 7.
In this sense, ONTO-ITSM is part of ONTO-META, and ONTO-LINKER is
within ONTO-META. The query in Figure 6 applies here as well and
will yield the same results.
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@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix seas: <https://w3id.org/seas/>. # Smart Energy Aware Systems
@prefix bot: <https://w3id.org/bot#> . # Building Topology Ontology
@prefix observable: # Unified Cybersecurity Ontology (UCO)
<https://unifiedcyberontology.org/ontology/uco/observable#> .
@prefix log: <https://w3id.org/sepses/ns/log#> . # a.k.a. SLOGERT
@prefix noria: <https://w3id.org/noria/ontology/> .
<https://w3id.org/noria/ontology/>
a owl:Ontology ;
# ===> Import of one of the ONTO-YANG-MODEL <===
<https://example.com/ontologies/ietf-network-topology> .
noria:Resource
rdf:type owl:Class ;
rdfs:label "Resource" ;
rdfs:comment """General resource record of the Communication Device
kind from the logistics park. It is a managed entity that can be
either Physical or Virtual."""@en ;
rdfs:subClassOf noria:StructuralElement ;
rdfs:subClassOf
seas:System,
seas:CommunicationDevice,
bot:Element ,
observable:Device ,
log:Host ;
rdfs:isDefinedBy noria: ;
# ===> Explicit linking to ONTO-YANG-MODEL <===
owl:equivalentClass <urn:ietf:params:xml:ns:yang:ietf-network#node>
.
Figure 7: Snippet of the ONTO-META describing the
'noria:Resource' concept from NORIA-O v0.3 with added linking to
ONTO-YANG-MODEL, in Turtle syntax.
4.3. Extract-Transform-Load pipelines for the ITSM-KG
Based on [I-D.marcas-nmop-knowledge-graph-yang] and [NORIA-DI-2023],
which present the technical means to implement a pipeline for
constructing the ITSM-KG, this section focuses on two complementary
viewpoints: Section 4.3.1 the management of streaming data such as
alarms and logs, and Section 4.3.2 the deployment of a federated data
architecture when various technical foundations or business units are
involved in providing the ITSM-KG.
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From the perspective of the Digital Map Requirements (Section 3.2),
the Figure 10, Figure 11 and Figure 12 particularly address the REQ-
DM-SCALES requirement.
4.3.1. Handling event streams
The following figures illustrate different scenarios for constructing
a ITSM-KG through an Extract-Transform-Load (ETL) data integration
pipeline.
Figure 8 illustrates a common design pattern providing the capability
to record event streams into a knowledge graph, such as an ITMS-KG if
considering that event data are mapped to ONTO-META concepts and
network entities to ONTO-YANG-MODEL concepts. The Figure 9 provides
an example of the resulting representation in the form of a knowledge
graph.
┌──────┐ ┌─────────┐ ┌──────┐ ┌────────┐ ┌──────┐
┌──────┐ │ │ │ Stream │ │ │ │ Stream │ │┌────┐│
│Events├─►│E.S.B.├─►│ mapping ├─►│S.S.B.├─►│ loader ├─►││K.G.││
└──────┘ │ │ │ │ │ │ │ │ │└────┘│
└──────┘ └─────────┘ └──┬───┘ └────────┘ └──────┘
│
┌───────────────────┴──────────────────────┐
│(event/LOG_login_03)=>(object/RES/router1)│
└─┌──────────────────────────────────────────┐
│(event/LOG_login_03)=>(object/RES/router1)│
└─┌──────────────────────────────────────────┐
│(event/LOG_login_03)=>(object/RES/router1)│
└──────────────────────────────────────────┘
Figure 8: KG-only data integration architecture for event data
streams.
<object/RES_router3>
<object/RES_router2> │
│ │ ┌────────┐
<object/RES_router1>─rdf:type─┤Resource│
│ └────────┘
│
logOriginatingManagedObject
│
<event/LOG_login_01> ┌───────────┐
<event/LOG_login_02>──rdf:type─┤EventRecord│
<event/LOG_login_03> └───────────┘
Figure 9: Resulting knowledge representation for the KG-only data
integration architecture for event data streams
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As event streams can be high-paced, it could be beneficial to
leverage input/output (I/O) performance optimizations specific to
each type of database management system (DBMS), such as Time-Series
DataBases (TSDBs) for streaming data and graph databases for
knowledge graphs. Figure 10 illustrates the capability to handle
both a knowledge graph and a time-series representation of the
network's lifecycle while maintaining a link between the two
representations (Figure 11). Each serve different purposes, such as
context analysis with the knowledge graph representation and trend
analysis with the TSDB. Thanks to the linking between the two
storage systems, users browsing aggregated data from the knowledge
graph can access the raw data within the relevant time span for
further analysis, and vice versa.
┌────────────┐
│ Complex │
│ Event │
│ Processing │
└────┬──┬────┘
┌──────┐ ┌──┴──┴───┐ ┌──────┐ ┌────────┐ ┌──────┐
┌──────┐ │ │ │ Stream │ │ │ │ Stream │ │┌────┐│
│Events├─►│E.S.B.├─►│ mapping ├─►│S.S.B.├─►│ loader ├─►││K.G.││
└──────┘ │ │ │ │ │ │ │ │ │└────┘│
└──┬───┘ └─────────┘ └──┬───┘ └────────┘ └──────┘
│ │
│ ┌───────────────────┴──────────────────────┐
│ │(event/AIS_login_01)=>(object/RES/router1)│
│ └──────────────────────────────────────────┘
│ ┌────────┐ ┌──────┐
│ │ Stream │ │┌────┐│
└────────────────────────────►│ loader ├─►││TSDB││
│ │ │└────┘│
└────────┘ └──────┘
Figure 10: Mixed KG/non-KG data integration architecture for
event data streams.
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<object/RES_router3>
<object/RES_router2> │
│ │ ┌────────┐
<object/RES_router1>─rdf:type─┤Resource│
│ └────────┘
logOriginatingManagedObject
│ ┌───────────┐
┌──────────────────►<event/AIS_login_01>──rdf:type─┤EventRecord│
│ │ │ \ └───────────┘
│ duration │ \
│ │ │ dcterms:type
│ "P0Y0M0DT0H3M30S"^^xsd:duration │ \
│ │ <Notification/
│ loggingTime EventType/inferredAlert>
│ │ │
│ "2024-02-07T16:22:42Z"^^xsd:dateTime rdf:type
│ ┌─────┴──────┐
│ │skos:Concept│
│ KG knowledge representation └────────────┘
│ ==============================================================
│ Time series database (TSDB) data representation
│
│ Timestamp Origin Event
│ 2024-02-07T16:22:42Z <object/RES_router1> Login Attempt
│ 2024-02-07T16:23:13Z <object/RES_router1> Login Attempt
│ 2024-02-07T16:26:12Z <object/RES_router1> Login Attempt
│ ▲
└──shared─identifier──────────────┘
Figure 11: Resulting knowledge representation for the mixed KG/
non-KG data integration architecture for event data streams.
4.3.2. Federated data architecture
The Figure 12 illustrates the principles for providing unified access
to data distributed across various technological platforms and
stakeholders thanks to Federated Queries [SPARQL11-FQ] and the use of
a shared ONTO-ITSM across data management platforms.
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───On-premise──────────────────────────── ┌─┐ Scope-based querying
┌Dom.─A─┐ │ │
│┌─────┐│ ┌──────┐ ┌─────────┐ │ │ ┌───────────┐
─►││ KG ││◄─┤KGDBMS├───────────┤SPARQL EP├─►│ ├─Network &─┤ NetOps │
│└─────┘│ └──────┘ └─────────┘ │ ├─Usage─────┤Application│
└UG.─2──┘ │ │ └───────────┘
┌Dom. B─┐ │ │ ┌───────────┐
│┌─────┐│ ┌──────┐ ┌─────────┐ │ ├─Network &─┤ SecOps │
─►││ KG ││◄─┤KGDBMS├───────────┤SPARQL EP├─►│ ├─Security──┤Application│
│└─────┘│ └──────┘ └─────────┘ │F│ └───────────┘
└UG.─1┬─┘ │E│
└────────────────────────────────────│D│─────────────┐
───On-premise / public-cloud───────────── │E│ │
┌Dom.─C─┐ │R│ ▼ Usage
│┌─────┐│ ┌──────┐ ┌───┐ ┌─────────┐ │A│ ┌────scope──┐
─►││ RDB ││◄─┤RDBMS ├─┤VKG├─────┤SPARQL EP├─►│T│ │* │
│└─────┘│ └──────┘ └───┘ └─────────┘ │E│ Network │ * * │
└UG.─1&2┘ │D│ scope───│────────┐ │
┌Dom.─D─┐ │ │ │ │ * * │ │
│┌─────┐│ ┌──────┐ ┌───┐ ┌─────────┐ │Q│ │ *└───────────┘
─►││NoSQL││◄─┤RDBMS ├─┤VKG├─────┤SPARQL EP├─►│U│ │ ┌───────────┐
│└─────┘│ └──────┘ └───┘ └─────────┘ │E│ │* │ * * │ │
└UG.─1──┘ │R│ └──│─────────┘ │
┌Dom.─E─┐ │I│ ▲ │ * │
│┌─────┐│ ┌──────┐ ┌───────┐ ┌─────────┐ │E│ │ │ * * │
─►││ LPG ││◄─┤GDBMS ├─┤QL tlt.├─┤SPARQL EP├─►│S│ │ └──Security─┘
│└─────┘│ └──────┘ └───────┘ └─────────┘ │ │ │ scope ▲
└UG.┬2──┘ │ │ │ │
└──────────────────────────────────────│ │────────┼──────────┘
│ │ │
───Public-cloud────────────────────────── │ │ │
┌Dom.─F─┐ │ │ │
│┌─────┐│ ┌──────┐ ┌─────────┐ │ │ │
─►││ KG ││◄─┤KGDBMS├───────────┤SPARQL EP├─►│ │ │
│└─────┘│ └──────┘ └─────────┘ │ │ │
└UG.┬1&2┘ └─┘ │
└─────────────────────────────────────────────────┘
Figure 12: Unified access to data distributed across various
technological platforms.
5. Experiments
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5.1. Experimental plan
In terms of experimentation, we consider the YANG-KG-SEMANTIC-
GENERALIZATION case defined in Section 4 as the reference approach
and recommend implementing a data processing pipeline that performs
the following use cases:
Y-MODEL-FROM-DATA: Based on a dataset of configuration data
expressed in YANG models, the goal is to enable extracting the
list of models involved for their conversion to their RDFS/OWL
equivalent.
Y-MODEL-DEPENDENCIES: Based on a given YANG model, the goal is to
enable identifying and retrieving all the YANG models that the
model refers to, in order to build a complete corpus of models for
their conversion to their RDFS/OWL equivalent as a coherent set.
Y-MODEL-TO-RDFS-OWL: Based on a YANG model and the associated model
corpus (i.e. Y-MODEL-DEPENDENCIES), the goal is to enable
producing a semantically equivalent RDFS/OWL representation (i.e.
ONTO-YANG-MODEL).
Ideally, a YANG to RDFS/OWL/YANG projection algebra would be used
to provide a formal proof of semantic equivalence; testing
mechanisms should be implemented as a fallback to provide a proof
of equivalence.
Y-INSTANCE-TO-KG: Based on a dataset of configuration data expressed
in YANG models and the related (set of) ONTO-YANG-MODEL, the goal
is to enable constructing a knowledge graph from the configuration
data, with the knowledge graph structured by the (set of) ONTO-
YANG-MODEL.
Y-MODEL-META-KG-ALIGNMENT: Based on a corpus of YANG models
transformed into RDFS/OWL (i.e. Y-MODEL-TO-RDFS-OWL) and a
reference ontology structuring the ITSM-KG, the goal is to enable
querying of the configuration entities present in the graph (i.e.
data derived from the Y-INSTANCE-TO-KG case) through the concepts
of the reference ontology.
In addition to identifying the class and property correspondences
between the resulting Y-MODEL-TO-RDFS-OWL models and the reference
ontology, this capability requires implementing a necessary and
sufficient number of class equivalence relations and property
equivalence relations.
META-KG-BEHAVIORAL-MODEL: Based on the ITSM-KG, which results from
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the composition of the Y-INSTANCE-TO-KG case with Y-MODEL-META-KG-
ALIGNMENT and additional operational data structured by ONTO-META,
the goal is to learn behavioral models (e.g. incident signatures)
in a formalism that can be interpreted through the lenses of ONTO-
ITSM and shared with other stakeholders with minimal discrepancies
in the underlying configuration data.
5.2. Implementation status
This section provides pointers to existing open source
implementations of this document or in close relation to it.
5.2.1. NORIA
The NORIA project aims at enabling advanced network anomaly detection
using knowledge graphs. Among the components resulting from this
project, the following ones serve the use case described in this
document:
* NORIA-O [NORIA-O-2024], is a data model for IT networks, events
and operations information. The ontology is developed using web
technologies (e.g. RDF, OWL, SKOS) and is intended as a structure
for realizing an ITSM knowledge graph for Anomaly Detection (AD)
and Risk Management applications. The NORIA-O implementation is
available as open source at https://w3id.org/noria/
(https://w3id.org/noria/). Its use for anomaly detection is
discussed in:
- [SLKG-2023] with a model-based design approach (i.e. query the
graph to retrieve anomalies and their context) and a
statistical learning approach (i.e. relate entities based on
context similarities, then use this relatedness to alert and
guide the repair).
- [GPL-2024] with a process mining approach to align a sequence
of entities to activity models, then use this relatedness to
guide the repair actions.
- [NORIA-UI-2024] a Web-based knowledge graph exploration design
for incident management that combines the above [SLKG-2023] and
[GPL-2024] techniques for broader coverage of anomaly cases and
knowledge capitalization.
* A knowledge graph-based platform design [NORIA-DI-2023] using
Semantic Web technologies and open source data integration tools
to build an ITSM knowledge graph:
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- SMASSIF-RML, a Semantic Web stream processing solution with
declarative data mapping capability. Available as open source
at https://github.com/Orange-OpenSource/smassif-rml
(https://github.com/Orange-OpenSource/smassif-rml).
- ssb-consum-up, a Kafka to SPARQL gateway enabling end-to-end
Semantic Web data flow architecture with a Semantic Service Bus
(SSB) approach. Available as open source at
https://github.com/Orange-OpenSource/ssb-consum-up
(https://github.com/Orange-OpenSource/ssb-consum-up).
- grlc, a fork of CLARIAH/grlc with SPARQL UPDATE and GitLab
interface features to facilitate the call and versioning of
stored user queries in SPARQL syntax (e.g. for anomaly
detection following the model-based design approach).
Available as open source at https://github.com/Orange-
OpenSource/grlc (https://github.com/Orange-OpenSource/grlc).
* SemNIDS [SemNIDS-2023], a test bench involving network trafic
generation, open source Network Intrusion Detection Systems
(NIDS), knowledge graphs, process mining and conformance checking
components.
Note that the NORIA project does not currently address the Y-MODEL-
FROM-DATA, Y-MODEL-DEPENDENCIES, and Y-MODEL-TO-RDFS-OWL use cases.
6. Security Considerations
As this document covers the _ITSM-KG_ concepts, and use cases, there
is no specific security considerations.
However, as the concept of a meta-knowledge graph involves the
construction of a multi-faceted graph (i.e. including network
topologies, operational data, and service and client data), it poses
the risk of simplifying access to network operational data and
functions that fall outside the knowledge graph users' responsibility
or that could facilitate the intervention of malicious individuals.
To support the discussion on mitigating this risk, we suggest
referring to Figure 12, which illustrates the concept of partial
access to the meta-knowledge graph based on rights associated with
each user group (UG) at the data domain level. We also recommend
referring to [AMO-2012] for an example of implementation of access
rights in a content management system that relies on Semantic Web
models and technologies. This implementation uses the AMO ontology,
which includes a set of classes and properties for annotating
resources that require access control, as well as a base of inference
rules that model the access management strategy to carry out.
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7. IANA Considerations
This document has no IANA actions.
8. References
8.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/rfc/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.
[RFC8345] Clemm, A., Medved, J., Varga, R., Bahadur, N.,
Ananthakrishnan, H., and X. Liu, "A YANG Data Model for
Network Topologies", RFC 8345, DOI 10.17487/RFC8345, March
2018, <https://www.rfc-editor.org/rfc/rfc8345>.
[RFC9418] Claise, B., Quilbeuf, J., Lucente, P., Fasano, P., and T.
Arumugam, "A YANG Data Model for Service Assurance",
RFC 9418, DOI 10.17487/RFC9418, July 2023,
<https://www.rfc-editor.org/rfc/rfc9418>.
8.2. Informative References
[AMO-2012] Buffa, M. and C. Faron-Zucker, "Ontology-Based Access
Rights Management", 2012,
<https://doi.org/10.1007/978-3-642-25838-1_3>.
[DevOpsInfra-2021]
Corcho, O., Chaves-Fraga, D., Toledo, J., Arenas-Guerrero,
J., Badenes-Olmedo, C., Wang, M., Peng, H., Burrett, N.,
Mora, J., and P. Zhang, "A High-Level Ontology Network for
ICT Infrastructures", 2021,
<https://doi.org/10.1007/978-3-030-88361-4_26>.
[FLAGSM-2021]
Steenwinckel, B., Paepe, D. D., Hautte, S. V., Heyvaert,
P., Bentefrit, M., Moens, P., Dimou, A., Bossche, B. V.
D., Turck, F. D., Hoecke, S. V., and F. Ongenae, "FLAGS: A
Methodology for Adaptive Anomaly Detection and Root Cause
Analysis on Sensor Data Streams by Fusing Expert Knowledge
with Machine Learning", 2021,
<https://doi.org/10.1016/j.future.2020.10.015>.
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[FOLIO-2018]
Steenwinckel, B., Heyvaert, P., Paepe, D. D., Janssens,
O., Hautte, S. V., Dimou, A., Turck, F. D., Hoecke, S. V.,
and F. Ongenae, "Towards Adaptive Anomaly Detection and
Root Cause Analysis by Automated Extraction of Knowledge
from Risk Analyses", 2018,
<https://www.ceur-ws.org/Vol-2213/paper2.pdf>.
[GPL-2024] Tailhardat, L., Stach, B., Chabot, Y., and R. Troncy,
"Graphameleon: Relational Learning and Anomaly Detection
on Web Navigation Traces Captured as Knowledge Graphs",
2024, <https://doi.org/10.1145/3589335.3651447>.
[I-D.boucadair-nmop-rfc3535-20years-later]
Boucadair, M., Contreras, L. M., de Dios, O. G., Graf, T.,
and R. Rahman, "RFC 3535, 20 Years Later: An Update of
Operators Requirements on Network Management Protocols and
Modelling", Work in Progress, Internet-Draft, draft-
boucadair-nmop-rfc3535-20years-later-04, 22 July 2024,
<https://datatracker.ietf.org/doc/html/draft-boucadair-
nmop-rfc3535-20years-later-04>.
[I-D.havel-nmop-digital-map-concept]
Havel, O., Claise, B., de Dios, O. G., and T. Graf,
"Digital Map: Concept, Requirements, and Use Cases", Work
in Progress, Internet-Draft, draft-havel-nmop-digital-map-
concept-00, 4 July 2024,
<https://datatracker.ietf.org/doc/html/draft-havel-nmop-
digital-map-concept-00>.
[I-D.irtf-nmrg-network-digital-twin-arch]
Zhou, C., Yang, H., Duan, X., Lopez, D., Pastor, A., Wu,
Q., Boucadair, M., and C. Jacquenet, "Network Digital
Twin: Concepts and Reference Architecture", Work in
Progress, Internet-Draft, draft-irtf-nmrg-network-digital-
twin-arch-06, 7 July 2024,
<https://datatracker.ietf.org/doc/html/draft-irtf-nmrg-
network-digital-twin-arch-06>.
[I-D.marcas-nmop-knowledge-graph-yang]
Martinez-Casanueva, I. D. and L. C. Rodríguez, "Knowledge
Graphs for YANG-based Network Management", Work in
Progress, Internet-Draft, draft-marcas-nmop-knowledge-
graph-yang-03, 5 July 2024,
<https://datatracker.ietf.org/doc/html/draft-marcas-nmop-
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Tailhardat, L., Troncy, R., and Y. Chabot, "NORIA-O: An
Ontology for Anomaly Detection and Incident Management in
ICT Systems", 2024,
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[NORIA-UI-2024]
Tailhardat, L., Chabot, Y., Py, A., and P. Guillemette,
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Acknowledgments
We would like to thank Benoit Claise for spontaneously seeking to
include the work of the NORIA research project in the vision of the
NMOP working group through direct contact.
We would also like to thank Fano Ramparany for his initial analysis
of the possibilities of defining a model conversion algebra for going
from YANG data models to OWL ontologies.
Authors' Addresses
Lionel Tailhardat
Orange
Email: lionel.tailhardat@orange.com
Raphaël Troncy
EURECOM
Email: raphael.troncy@eurecom.fr
Yoan Chabot
Orange
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Email: yoan.chabot@orange.com
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