# Dynamic Networks of Finite State Machines

## TL;DR

This paper introduces a new distributed algorithm for maximal independent set in dynamic networks, ensuring local confinement of changes and efficient recovery, with performance proportional to the number of topology changes.

## Contribution

It presents a novel MIS algorithm that maintains local confinement and efficiency in dynamic networks under the Stone Age model, addressing topology changes.

## Key findings

- Algorithm guarantees local confinement of topology changes.
- Surviving nodes perform O((C+1) log^2 n) steps, with C being the number of changes.
- Linear dependency on C in performance cannot be avoided.

## Abstract

Like distributed systems, biological multicellular processes are subject to dynamic changes and a biological system will not pass the survival-of-the-fittest test unless it exhibits certain features that enable fast recovery from these changes. In particular, a question that is crucial in the context of biological cellular networks, is whether the system can keep the changing components \emph{confined} so that only nodes in their vicinity may be affected by the changes, but nodes sufficiently far away from any changing component remain unaffected. Based on this notion of confinement, we propose a new metric for measuring the dynamic changes recovery performance in distributed network algorithms operating under the \emph{Stone Age} model (Emek \& Wattenhofer, PODC 2013), where the class of dynamic topology changes we consider includes inserting/deleting an edge, deleting a node together with its incident edges, and inserting a new isolated node. Our main technical contribution is a distributed algorithm for maximal independent set (MIS) in synchronous networks subject to these topology changes that performs well in terms of the aforementioned new metric. Specifically, our algorithm guarantees that nodes which do not experience a topology change in their immediate vicinity are not affected and that all surviving nodes (including the affected ones) perform $\mathcal{O}((C + 1) \log^{2} n)$ computationally-meaningful steps, where $C$ is the number of topology changes; in other words, each surviving node performs $\mathcal{O}(\log^{2} n)$ steps when amortized over the number of topology changes. This is accompanied by a simple example demonstrating that the linear dependency on $C$ cannot be avoided.