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Direct-Conflict Resolution in Intent-Driven Autonomous Networks

Idris Cinmere, Kashif Mehmood, Katina Kralevska, Toktam Mahmoodi

TL;DR

The paper tackles conflict resolution in Intent-Based Networking for radio access networks, addressing how concurrent intents can compete over network controls. It broadens the typical Nash Bargaining Solution by incorporating Weighted Nash, Kalai-Smorodinsky, and Shannon Entropy bargaining, and uses Jain fairness to compare results. Through a disaster-recovery RAN scenario, it shows that each bargaining method yields distinct antenna tilt settings, with Kalai-Smorodinsky achieving the highest fairness under the tested conditions. This work highlights KSBS as a promising, fair approach and points to learning-based hybrids as a fruitful direction for integrating multiple bargaining paradigms in practice.

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.

Direct-Conflict Resolution in Intent-Driven Autonomous Networks

TL;DR

The paper tackles conflict resolution in Intent-Based Networking for radio access networks, addressing how concurrent intents can compete over network controls. It broadens the typical Nash Bargaining Solution by incorporating Weighted Nash, Kalai-Smorodinsky, and Shannon Entropy bargaining, and uses Jain fairness to compare results. Through a disaster-recovery RAN scenario, it shows that each bargaining method yields distinct antenna tilt settings, with Kalai-Smorodinsky achieving the highest fairness under the tested conditions. This work highlights KSBS as a promising, fair approach and points to learning-based hybrids as a fruitful direction for integrating multiple bargaining paradigms in practice.

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.
Paper Structure (15 sections, 15 equations, 4 figures, 2 tables)

This paper contains 15 sections, 15 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. The Cooperative Bargaining Problem
  3. The Nash Bargaining Solution (NBS)
  4. Kalai-Smorodinsky Bargaining Solution (KSBS)
  5. Shannon Entropy Bargaining Solution (SEBS)
  6. System Model
  7. Intent-based Bargaining Problem
  8. The Nash Bargaining Solution (NBS)
  9. The Kalai-Smorodinsky Bargaining Solution (KSBS)
  10. The Shannon Entropy Bargaining Solution (SEBS)
  11. Jain-based Optimum Value
  12. Performance Evaluation
  13. Simulation setup
  14. Simulation results
  15. Conclusion

Figures (4)

  • Figure 1: Relations between Intents, Functions, Network Control Parameters.
  • Figure 2: Conflict Resolution Workflow.
  • Figure 3: Simulation Scenario.
  • Figure 4: Variation of SINR (Intent B) and CQI (Intent A) Utility Functions with Antenna Tilt and Corresponding Bargaining Solutions.