Knowledge Graph Embedding in Intent-Based Networking

TL;DR

This paper introduces a novel method combining knowledge graph embeddings with intent-based networking to improve network management, service orchestration, and resource allocation through dynamic intent translation and validation.

Contribution

It presents a new approach integrating knowledge graph embeddings into intent-based networking for improved service mapping and intent validation.

Findings

Service prediction and intent verification accuracy exceeds 80%.

Efficient mapping of high-level intents to network services.

Dynamic adaptation to network changes and resource availability.

Abstract

This paper presents a novel approach to network management by integrating intent-based networking (IBN) with knowledge graphs (KGs), creating a more intuitive and efficient pipeline for service orchestration. By mapping high-level business intents onto network configurations using KGs, the system dynamically adapts to network changes and service demands, ensuring optimal performance and resource allocation. We utilize knowledge graph embedding (KGE) to acquire context information from the network and service providers. The KGE model is trained using a custom KG and Gaussian embedding model and maps intents to services via service prediction and intent validation processes. The proposed intent lifecycle enables intent translation and assurance by only deploying validated intents according to network and resource availability. We evaluate the trained model for its efficiency in service mapping and intent validation tasks using simulated environments and extensive experiments. The service prediction and intent verification accuracy greater than 80 percent is achieved for the trained KGE model on a custom service orchestration intent knowledge graph (IKG) based on TMForum's intent common model.