Efficient resource allocation and scheduling algorithms are essential for various distributed applications, ranging from wireless networks and cloud computing platforms to autonomous multi-agent systems and swarm robotic networks. However, real-world environments are often plagued by uncertainties and noise, leading to sub-optimal performance and increased vulnerability of traditional algorithms. This paper addresses the challenge of robust resource allocation and scheduling in the presence of noise and disturbances. The proposed study introduces a novel sign-based dynamics for developing robust-to-noise algorithms distributed over a multi-agent network that can adaptively handle external disturbances. Leveraging concepts from convex optimization theory, control theory, and network science the framework establishes a principled approach to design algorithms that can maintain key properties such as resource-demand balance and constraint feasibility. Meanwhile, notions of uniform-connectivity and versatile networking conditions are also addressed.

翻译：高效的资源分配与调度算法对于从无线网络、云计算平台到自主多智能体系统及集群机器人网络等各类分布式应用至关重要。然而，现实环境常受不确定性与噪声干扰，导致传统算法性能次优且脆弱性加剧。本文针对存在噪声与扰动条件下的鲁棒资源分配与调度挑战展开研究。该研究提出了一种新颖的基于符号的动态机制，用于开发可自适应处理外部扰动的分布式多智能体网络抗噪鲁棒算法。通过融合凸优化理论、控制理论与网络科学的核心概念，该框架建立了一套系统化方法，使算法能够在资源需求平衡与约束可行性等关键属性上保持鲁棒性。同时，本文还探讨了一致连通性与通用网络连接条件等关键问题。