System Relaxation for Interpretable and Adaptive Network Control

2025-09-21

AI Summary

Traditional network control relies on static shortest-path routing, leading to stress concentration at critical nodes and poor robustness. To address this, we propose the Systemic Relaxation Algorithm (SRA), the first approach to incorporate physical relaxation mechanisms into network control—yielding a topology-dependent, interpretable white-box dynamical system. Grounded in nonsmooth dynamical systems theory, SRA features a globally convergent and practically stable adaptive control algorithm that dynamically switches optimization objectives according to network heterogeneity, enabling intelligent trade-offs between load balancing and systemic resilience. Experiments demonstrate that, in heterogeneous networks, SRA reduces peak centrality by over 80% and improves high-load throughput by more than 45%; in homogeneous networks, it significantly enhances fault tolerance. Theoretical analysis rigorously guarantees both convergence and stability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Prevailing network control strategies, which rely on static shortest-path logic, suffer from catastrophic "stress concentration" on critical nodes. This paper introduces the System Relaxation Algorithm (SRA), a new control paradigm inspired by physical relaxation that guides a network toward an emergent equilibrium of load balance. SRA is an interpretable, 'white-box' dynamical system whose behavior is profoundly topology-dependent: in heterogeneous networks, it acts as a proactive performance optimizer, reducing peak centrality by over 80% and increasing high-load throughput by more than 45%; in homogeneous topologies, its objective intelligently shifts to resilience enhancement. We rigorously prove its global convergence and practical stability using the theory of non-smooth dynamical systems, establishing a predictable paradigm for network governance that intelligently trades off performance and resilience.

Problem

• Static shortest-path control causes catastrophic stress concentration on critical nodes
• Existing strategies lack interpretable, topology-dependent adaptive network governance
• Current approaches fail to intelligently trade off performance and resilience objectives

Innovation

• System Relaxation Algorithm guides network to load balance equilibrium
• Proactive optimizer reducing peak centrality and increasing throughput
• Globally convergent dynamical system for performance-resilience trade-off