Robust-to-Noise Algorithms for Distributed Resource Allocation and Scheduling
CoRR(2023)
摘要
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.
更多查看译文
关键词
distributed optimization,graph theory,distributed resource allocation,random noise,optimality gap
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要