Knowledge-Driven Intent Life-Cycle Management for Cellular Networks

The management of cellular networks and services has evolved due to the rapidly changing demands and complexity of service modeling and management. This paper uses intent-based networking (IBN) as a solution and couples it with contextual information from knowledge graphs (KGs) of network and service components to achieve the objective of service orchestration in cellular networks. Fusing IBN with KGs facilitates an intelligent, flexible, and resilient service orchestration process.We propose an intent completion approach using knowledge graph learning and a mapping model capable of inferring and validating the service intents in the network. Subsequently, these service intents are deployed using available network resources in a simulated fifth generation (5G) non-standalone (NSA) network. The compliance of the deployed intents is monitored, and mutual optimization against their required service key performance indicators is performed using Simultaneous Perturbation Stochastic Approximation (SPSA) and Multiple Gradient Descent Algorithm (MGDA). The numerical results show that the knowledge graph with Gaussian embedding (KG2E) model outperforms other distance-based embedding models for the proposed service KG. Different combinations of strict latency (SL) and non-strict latency (NSL) intents are deployed, and compliance is evaluated for increasing numbers of deployed intents against baseline deployment scenarios. The results show a higher level of compliance for SL intents to target latencies in comparison to NSL intents for the proposed intent deployment and optimization algorithm.