Direct-Conflict Resolution in Intent-Driven Autonomous Networks

TL;DR

This paper investigates conflict resolution strategies in Intent-Based Networks, introducing advanced bargaining solutions to improve fairness and demonstrating their effectiveness through simulations in radio access networks.

Contribution

It extends conflict resolution methods in IBNs beyond Nash Bargaining to include WNBS, KSBS, and SEBS, enhancing fairness and optimization.

Findings

KSBS provides the most equitable solutions based on Jain Fairness Index.

Distinct antenna tilt values are obtained for each bargaining method.

Simulations validate the effectiveness of proposed conflict resolution strategies.

Abstract

As network systems evolve, there is an escalating demand for automated tools to facilitate efficient management and configuration. This paper explores conflict resolution in Intent-Based Network (IBN) management, an innovative approach that holds promise for effective network administration, especially within radio access domain. Nevertheless, when multiple intents are in operation concurrently, conflicts may emerge, presenting a significant issue that remains under-addressed in the current literature. In response to this challenge, our research expands the range of conflict resolution strategies beyond the established Nash Bargaining Solution (NBS), to incorporate the Weighted Nash Bargaining Solution (WNBS), the Kalai-Smorodinsky Bargaining Solution (KSBS), and the Shannon Entropy Bargaining Solution (SEBS). These methods are employed with the objective to identify optimal parameter values, aiming to ensure fairness in conflict resolution. Through simulations, it is demonstrated that distinct antenna tilt values are yielded as the respective solutions for each method. Ultimately, based on Jain Fairness Index, the KSBS is identified as the most equitable method under the given conditions.