Adaptive Relaxation Approach for End-to-End Latency Constraints in Complex Communication Networks

In System of Systems (SoS) applications leveraging 5G communications, maintaining end-to-end (E2E) latency constraints is critical for performance and reliability. However, enforcing strict latency constraints at all times can lead to inefficient resource utilization, network congestion, and increased operational costs. This paper proposes a Dynamic Relaxation Approach using Deep Reinforcement Learning (DRL), where each autonomous subsystem dynamically adjusts its latency relaxation factor based on network conditions, task criticality, and system load. The proposed framework is evaluated under three congestion scenarios using metrics of reward convergence and relaxation state dynamics, demonstrating rapid adaptation and significant improvements in balancing performance with resource consumption. Additionally, despite employing reinforcement learning, our approach remains lightweight due to its binary, threshold-based decision-making process, which determines whether to relax constraints based solely on network parameters.