Distributed Allocation and Resource Scheduling Algorithms Resilient to Link Failure

2025-10-20

AI Summary

Conventional distributed resource allocation (DRA) methods fail under realistic network conditions where links frequently disconnect due to interference, congestion, or adversarial attacks. Method: This paper proposes a robust distributed algorithm that guarantees constraint feasibility at all times, tolerates nonlinear node-level disturbances, and converges to the global optimum—despite only requiring periodic (not persistent) network connectivity and no continuous communication. Leveraging graph-theoretic and network-percolation modeling, the approach integrates nonlinear dynamical analysis with distributed consensus mechanisms to explicitly accommodate heterogeneous time delays and dynamic link failures. Contribution/Results: We provide rigorous theoretical proofs of convergence and optimality. Simulations demonstrate sustained efficiency and stability even under extreme link failure rates (>70%) and time-varying delays, significantly enhancing the robustness and practicality of resource scheduling in mobile multi-agent systems.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Multiagent Systems: Distributed Problem Solving
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

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

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.

Problem

• Ensures resource allocation feasibility during network disruptions
• Maintains convergence despite nonlinearities and intermittent connectivity
• Achieves optimal performance under link failures and delays

Innovation

• Resilient algorithm ensures constraint feasibility during disruptions
• Robust convergence with sector-bound nonlinearities and delays
• Optimal performance under intermittent connectivity using percolation theory