MAYA: Exploring multiform attributes of node to align YANG data models

Abstract

Nowadays Network Configuration Protocol (NETCONF) has become an essential part of network management for its flexibility and scalability. Vendors intend to use NETCONF to replace Command-Line Interface (CLI) and Simple Network Management Protocol (SNMP) to manage devices. Yet Another Next Generation (YANG) models are tailored to model NETCONF protocol messages. However, there exists heterogeneity issue with YANG models as a consequence of vendors proposing proprietary YANG models which differ from each other in structure or content. Thus, managing network devices from different vendors requires expert knowledge and plenty of resources. In this paper, we present MAYA, a solution to automatically accomplish alignment of YANG models from different vendors or organizations by exploring multiform node attributes. In MAYA, different semantic similarity techniques are used to measure distance between different attributes, such as name, description and type, in nodes from various YANG models. A customized SMP based matching algorithm for YANG models alignment is proposed to generate mapping relations between models based on the semantic similarity. The real cases analysis and experiments show that MAYA is able to meet the demands in production on the problem of YANG model alignment.