Distributed Allocation and Resource Scheduling Algorithms Resilient to Link Failure

TL;DR

This paper presents a resilient distributed resource allocation algorithm that maintains feasibility and convergence despite link failures, delays, and intermittent connectivity, suitable for dynamic and unreliable network environments.

Contribution

It introduces a novel graph-theoretic approach that ensures constraint feasibility and optimality under network disruptions, advancing the robustness of distributed algorithms.

Findings

Algorithm guarantees resource-demand balance during disruptions

Converges to optimal solutions despite link failures and delays

Effective in mobile multi-agent systems with intermittent connectivity

Abstract

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.