  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.
