Distributed resource allocation (DRA) is fundamental to modern networked systems, spanning applications from economic dispatch in smart grids to CPU scheduling in data centers. Conventional DRA approaches require reliable communication, yet real-world networks frequently suffer from link failures, packet drops, and communication delays due to environmental conditions, network congestion, and security threats. We introduce a novel resilient DRA algorithm that addresses these critical challenges, and our main contributions are as follows: (1) guaranteed constraint feasibility at all times, ensuring resource-demand balance even during algorithm termination or network disruption; (2) robust convergence despite sector-bound nonlinearities at nodes/links, accommodating practical constraints like quantization and saturation; and (3) optimal performance under merely uniformly-connected networks, eliminating the need for continuous connectivity. Unlike existing approaches that require persistent network connectivity and provide only asymptotic feasibility, our graph-theoretic solution leverages network percolation theory to maintain performance during intermittent disconnections. This makes it particularly valuable for mobile multi-agent systems where nodes frequently move out of communication range. Theoretical analysis and simulations demonstrate that our algorithm converges to optimal solutions despite heterogeneous time delays and substantial link failures, significantly advancing the reliability of distributed resource allocation in practical network environments.

翻译：分布式资源分配（DRA）是现代网络系统的核心基础，其应用范围涵盖从智能电网的经济调度到数据中心的CPU调度等多个领域。传统的DRA方法依赖于可靠的通信，然而实际网络常因环境条件、网络拥塞和安全威胁而遭受链路故障、数据包丢失和通信延迟等问题。本文提出了一种新型的鲁棒DRA算法以应对这些关键挑战，主要贡献如下：（1）始终保证约束可行性，即使在算法终止或网络中断期间也能维持资源-需求平衡；（2）在节点/链路存在扇区有界非线性的情况下仍能稳健收敛，可适应量化与饱和等实际约束；（3）在仅满足均匀连通性的网络条件下即可实现最优性能，无需持续连通性。与现有方法需要持续网络连通性且仅提供渐近可行性不同，本工作基于图论的解决方案利用网络渗流理论，在间歇性断连期间保持性能稳定。这使得该算法对节点频繁移出通信范围的移动多智能体系统具有特殊价值。理论分析与仿真实验表明，即使存在异构时间延迟和严重链路故障，该算法仍能收敛至最优解，显著提升了实际网络环境中分布式资源分配的可靠性。