]> Telefonica

ana.mendezperez@telefonica.com

Telefonica

diego.r.lopez@telefonica.com

Operations and Management Operations and Management Area Working Group normalization datastore equivalent representation This document defines a normalization procedure for YANG-modeled information. The procedure enables different serialized representations conveying equivalent information to produce the same normalized representation independently of the input encoding and applicable representation conventions. The normalized representation is intended to support comparison, hashing, integrity verification, provenance, and other procedures that require consistent representation of equivalent YANG-modeled information. About This Document The latest revision of this draft can be found at . Status information for this document may be found at . Discussion of this document takes place on the Operations and Management Area Working Group Working Group mailing list (), which is archived at . Subscribe at . Source for this draft and an issue tracker can be found at .

Introduction YANG is widely used to model configuration data, operational state, notifications, and other information exchanged between network management systems. Data modeled using YANG can be represented through different encodings, including XML, JSON, and CBOR. Furthermore, equivalent YANG-modeled information may be serialized differently by distinct implementations due to representation-specific conventions, ordering choices, or encoding mechanisms. These differences make it difficult to determine whether two representations convey the same information. As a result, operations such as datastore comparison, data deduplication, fingerprint generation, integrity verification, provenance processing, and message-broker compaction often depend on representation-specific artifacts rather than on the underlying information itself. Existing canonicalization mechanisms operate at the encoding level. For example, XML, JSON, and CBOR provide independent procedures for producing deterministic encodings of individual documents. However, these mechanisms do not address the broader problem of obtaining a common representation across different encodings. This document defines a normalization procedure for YANG-modeled information that produces a deterministic representation independent of the original encoding. The procedure enables equivalent YANG-modeled information to converge into the same normalized representation, providing a stable basis for comparison, fingerprint generation, integrity verification, and other operational uses.

Scope This document focuses on representation equivalence of YANG-modeled data. The procedure defined in this document is intended to identify when different serialized representations convey equivalent information. Semantic equivalence is outside the scope of this document.

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 when, and only when, they appear in all capitals, as shown here.

• Representation: A concrete serialized einstance of YANG-modeled data. Examples include XML, JSON, and CBOR encodings conveying the same underlying information.
• Normalized Representation: A deterministic representation generated by the normalization procedure defined in this document.
• Representation Equivalence: Two representations are representation-equivalent when they produce identical normalized representations.
• Fingerprint: A cryptographic digest calculated from a normalized representation.
• Schema-Independent Normalization: A normalization procedure that does not require access to the corresponding YANG schema during processing.
• Semantic Equivalence: The property whereby two representations correspond to the same YANG-modeled information after considering all schema-dependent semantics.

Problem Statement YANG-modeled data can be represented using different encodings, including XML , JSON , and CBOR . Existing canonicalization mechanisms operate within a particular encoding and produce deterministic representations of individual documents. Examples include XML Canonicalization (XML-C14N), the JSON Canonicalization Scheme (JCS) , and the deterministic encoding rules defined for CBOR . These mechanisms enable a representation expressed using a particular encoding to be transformed into a deterministic byte sequence. However, they do not provide a common representation across different encodings, nor do they necessarily address all representation variations that may arise from encoding rules or implementation choices. Ordering is one possible source of such variation. These differences may arise from implementation choices, internal datastore representations, module augmentations, or serialization procedures. For example, one implementation may produce: ]]> while another implementation may produce: ]]> If the order of these nodes is not semantically significant, both serialized representations convey equivalent information despite having different element ordering and byte sequences. As a result, equivalent YANG-modeled information may produce different serialized representations and therefore different fingerprints, hashes, or signatures. Direct comparison of serialized representations cannot reliably determine whether two representations convey the same information. A normalization procedure operating above the encoding-specific representation layer is therefore needed to enable representation-independent comparison of YANG-modeled data.

Operational Use Cases The absence of a representation-independent form of YANG-modeled data affects several operational scenarios, including:

• Datastore State Comparison: Network management systems frequently compare datastore snapshots obtained from different devices, controllers, or processing stages. Equivalent YANG-modeled data represented through different encodings or ordering conventions may appear different when compared directly, despite conveying the same information.
• Message Broker Applications: YANG-modeled information is increasingly exchanged through message broker infrastructures and data distribution systems. Consistent representation of equivalent information can support operations such as message deduplication, topic compaction, change detection, and content synchronization.
• Data Synchronization and Replication: Operational environments frequently exchange YANG-modeled data between multiple systems. A deterministic normalized representation can simplify data comparison, synchronization procedures, and consistency verification across distributed environments.
• Data Deduplication: Storage and processing systems may benefit from the ability to identify equivalent information independently of the original representation. A normalized representation provides a stable basis for content identification and deduplication.
• Provenance and Integrity Verification: Where stable and deterministic representations are required to generate reproducible fingerprints, hashes, and signatures and provenance records.

Normalization Procedure The normalization procedure takes a serialized YANG data document as input and produces a deterministic normalized representation.

Abstract Syntax Tree The procedure parses the input document according to its encoding and constructs an abstract syntax tree (AST) of the serialized data representation. Abstract syntax trees are described in Section 1.1 of . For the purposes of this document, the AST does not represent every detail of the concrete input syntax. It preserves only the structural and content-related information required by the normalization procedure. This information consists of node identifiers, textual values, hierarchical relationships, and node multiplicity. The AST provides a common, encoding-independent representation of serialized YANG-modeled data. It is constructed without consulting a YANG schema. Consequently, it represents the observable structure and textual values exposed by the serialized document but does not resolve schema-dependent YANG semantics. The tree contains a synthetic document root. The synthetic root provides a common container for the top-level nodes of the serialized input document. It does not correspond to a YANG data node and uses an empty identifier reserved for this purpose. Each node below the synthetic document root has a non-empty identifier and is one of the following:

• a leaf node, which contains a textual value and has no child nodes;
• an inner node, which contains zero or more child nodes and has no textual value.

A node cannot contain both a textual value and child nodes. The abstract syntax tree preserves node multiplicity. Multiple nodes may have the same identifier, and identical nodes are not implicitly removed.

Tree Construction The input document is first parsed according to the syntax and structure of the selected encoding. Malformed input or an unsupported document structure causes normalization to fail. The procedure creates the synthetic document root and adds the top-level data nodes obtained from the serialized document as its children. The following conceptual rules are then applied:

1. An XML element, JSON object member, or CBOR map entry that contains nested data is represented as an inner node.
2. An XML element, JSON object member, or CBOR map entry that contains a scalar value is represented as a leaf node.
3. The name exposed by the input encoding is used to derive the node identifier.
4. A sequence of data items is represented as sibling nodes having the identifier associated with the sequence. The sequence itself does not produce an additional node.
5. An empty sequence produces no nodes.
6. An empty structured value is represented as an inner node having no children.
7. The encoding-specific representation of the YANG empty type is represented as a leaf having an empty textual value.
8. Scalar values are converted to textual form.
9. The same construction rules are applied to every nested data item until the complete abstract data tree has been constructed.

These rules are recursive in definition. An implementation is not required to use recursion internally, provided that it produces the same abstract data tree. XML attributes are not represented in the abstract data tree defined by this version, the treatment of attributes is TBD. XML elements and their textual content are processed as data-tree nodes and values, respectively. TBD: Specify the treatment of YANG metadata annotations, namespace-qualified identifiers, namespace declarations, format-specific constructs, and malformed or unsupported input structures. This initial version focuses on the fundamental normalization procedure. The treatment of schema-specific semantics, including default values, identityref and leafref resolution, and ordered-by-user semantics, is TBD. Further versions of this document are expected to determine whether these aspects require specific normalization rules or are inherently addressed by the general normalization procedure. Where specific handling is required to preserve equivalence, the corresponding rules will be incorporated into the normalization procedure. The same YANG-modeled information MUST produce the same normalized representation regardless of the original serialization format.

Examples This section illustrates how equivalent YANG-modeled data represented using different serialization formats and encodings can produce the same normalized representation. The following XML and JSON representations examples are based on the interface data model originally defined in and convey equivalent information. eth0 iana-if-type:ethernetCsmacd false eth1 iana-if-type:ethernetCsmacd true true eth1.10 iana-if-type:l2vlan true eth1 10 lo1 iana-if-type:softwareLoopback true ]]> The following non-normative textual tree illustrates the intended outcome: equivalent XML, JSON, and CBOR representations are expected to produce the same normalized representation.

Relationship to Existing Work The normalization procedure defined in this document is intended to complement existing efforts related to the processing, identification, and distribution of YANG-modeled information. The YANG provenance mechanisms defined in operate on serialized representations of YANG data and can benefit from a stable, representation-independent normalization procedure capable of producing equivalent representations across different serialization formats. Similarly, work related to message broker identification and key generation for YANG-modeled information, such as , may benefit from a normalization procedure that produces stable representations for equivalent information independently of their original encoding. This document does not modify or replace such mechanisms. Instead, it provides a potential foundation that can be used by higher-level procedures requiring consistent representation of equivalent YANG-modeled information.

Security Considerations The normalization procedure defined in this document is intended to provide a deterministic representation of YANG-modeled data. It does not, by itself, provide integrity, authenticity, confidentiality, or provenance guarantees. Security properties may be obtained by applying appropriate mechanisms, such as cryptographic hash functions, digital signatures, or provenance procedures, to the resulting normalized representation. Implementations relying on the normalized representation for integrity verification or provenance applications MUST use cryptographically secure algorithms appropriate for the intended use case. Incorrect normalization procedures may result in different representations producing the same normalized output or in equivalent information producing different normalized representations. Such situations may negatively affect comparison, fingerprint generation, and verification procedures.

IANA Considerations This document has no IANA actions.

Implementation status An open-source reference implementation, written in Java, is available at https://github.com/tefiros/yang-data-normalization.

References Normative References In many standards track documents several words are used to signify the requirements in the specification. These words are often capitalized. This document defines these words as they should be interpreted in IETF documents. This document specifies an Internet Best Current Practices for the Internet Community, and requests discussion and suggestions for improvements. RFC 2119 specifies common key words that may be used in protocol specifications. This document aims to reduce the ambiguity by clarifying that only UPPERCASE usage of the key words have the defined special meanings. Informative References Cambridge University Press, ISBN 978-1-107-15030-0 This document defines a YANG data model for the management of network interfaces. It is expected that interface-type-specific data models augment the generic interfaces data model defined in this document. The data model includes configuration data and state data (status information and counters for the collection of statistics). YANG is a data modeling language used to model configuration data, state data, Remote Procedure Calls, and notifications for network management protocols. This document describes the syntax and semantics of version 1.1 of the YANG language. YANG version 1.1 is a maintenance release of the YANG language, addressing ambiguities and defects in the original specification. There are a small number of backward incompatibilities from YANG version 1. This document also specifies the YANG mappings to the Network Configuration Protocol (NETCONF). This document defines encoding rules for representing configuration data, state data, parameters of Remote Procedure Call (RPC) operations or actions, and notifications defined using YANG as JavaScript Object Notation (JSON) text. YANG (RFC 7950) is a data modeling language used to model configuration data, state data, parameters and results of Remote Procedure Call (RPC) operations or actions, and notifications. This document defines encoding rules for YANG in the Concise Binary Object Representation (CBOR) (RFC 8949). Cryptographic operations like hashing and signing need the data to be expressed in an invariant format so that the operations are reliably repeatable. One way to address this is to create a canonical representation of the data. Canonicalization also permits data to be exchanged in its original form on the "wire" while cryptographic operations performed on the canonicalized counterpart of the data in the producer and consumer endpoints generate consistent results. This document describes the JSON Canonicalization Scheme (JCS). This specification defines how to create a canonical representation of JSON data by building on the strict serialization methods for JSON primitives defined by ECMAScript, constraining JSON data to the Internet JSON (I-JSON) subset, and by using deterministic property sorting. The Concise Binary Object Representation (CBOR) is a data format whose design goals include the possibility of extremely small code size, fairly small message size, and extensibility without the need for version negotiation. These design goals make it different from earlier binary serializations such as ASN.1 and MessagePack. This document obsoletes RFC 7049, providing editorial improvements, new details, and errata fixes while keeping full compatibility with the interchange format of RFC 7049. It does not create a new version of the format. Telefonica Telefonica Deutsche Telekom Telefonica Fraunhofer SIT This document defines a mechanism based on CBOR Object Signing and Encryption (COSE) signatures to provide and verify the provenance of YANG data, so it is possible to verify the origin and integrity of a dataset, even when those data are going to be processed and/or applied in workflows where a crypto-enabled data transport directly from the original data source is not available. As the application of evidence-based OAM automation and the use of tools such as AI/ML grow, provenance validation becomes more relevant in all scenarios, in support of the assuring the origin and integrity of data. The use of compact signatures facilitates the inclusion of provenance strings in any YANG schema requiring them. Swisscom Swisscom Deutsche Telekom Everything OPS NTT This document specifies a mechanism to define a unique Message key for a YANG to Message Broker integration and a topic addressing scheme based on YANG-Push subscription type and YANG Schema Node Identifier. This enables YANG data consumption of a subset of subscribed YANG data, either per specific YANG data node, identifier or telemetry message type, by indexing and organizing in Message Broker topics. It helps to index the information by using data taxonomy and organizes data in partitions and shards of Message Brokers and time series databases.

Acknowledgments This document is based on work partially funded by the EU Horizon Europe projects CYBERNEMO (grant 101168182) and 3C4AI (grant 101298630).