Model Predictive Control-Based Dynamic Resource Allocation: Enabler for Resilient Network Slicing

The advent of sixth-generation networks (6G) introduces transformative capabilities in terms of ultra-reliable low-latency communications, massive connectivity, and edge cloud integration, that promise to enable time-sensitive applications and immersive services. These ambitious capabilities, however, pose significant challenges for resilient and dynamic resource allocation, requiring adaptation of orchestration frameworks towards efficient and sustainable resources management. In this paper, we propose a Model Predictive Control (MPC)-based framework for 6G network slicing, focusing on minimizing power consumption, end-to-end delay, and slice reconfiguration costs. The framework is designed to operate as an optimization mechanism integrated within a 6G network slice management architecture, aligned with emerging virtualization, monitoring and analytics functionalities. The proposed MPC-based framework leverages future demand forecasts to optimize resource allocation over a receding time horizon. Additionally, we introduce a proxy-based adaptation of the MPC to enhance scalability and decision speed, ensuring efficient operations even in large-scale 6G settings. Through simulations, we demonstrate the efficacy of the proposed solution compared to other approaches.