Early Adapting to Trends: Self-Stabilizing Information Spread using Passive Communication

Amos Korman, Robin Vacus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

How to efficiently and reliably spread information in a system is one of the most fundamental problems in distributed computing. Recently, inspired by biological scenarios, several works focused on identifying the minimal communication resources necessary to spread information under faulty conditions. Here we study the self-stabilizing bit-dissemination problem, introduced by Boczkowski, Korman, and Natale in [SODA 2017]. The problem considers a fully-connected network of n agents, with a binary world of opinions, one of which is called correct. At any given time, each agent holds an opinion bit as its public output. The population contains a source agent which knows which opinion is correct. This agent adopts the correct opinion and remains with it throughout the execution. We consider the basic PULL model of communication, in which each agent observes relatively few randomly chosen agents in each round. The goal of the non-source agents is to quickly converge on the correct opinion, despite having an arbitrary initial configuration, i.e., in a self-stabilizing manner. Once the population converges on the correct opinion, it should remain with it forever. Motivated by biological scenarios in which animals observe and react to the behavior of others, we focus on the extremely constrained model of passive communication, which assumes that when observing another agent the only information that can be extracted is the opinion bit of that agent. We prove that this problem can be solved in a poly-logarithmic in∼n number of rounds with high probability, while sampling a logarithmic number of agents at each round. Previous works solved this problem faster and using fewer samples, but they did that by decoupling the messages sent by agents from their output opinion, and hence do not fit the framework of passive communication. Moreover, these works use complex recursive algorithms with refined clocks that are unlikely to be used by biological entities. In contrast, our proposed algorithm has a natural appeal as it is based on letting agents estimate the current tendency direction of the dynamics, and then adapt to the emerging trend.

Original languageAmerican English
Title of host publicationPODC 2022 - Proceedings of the 2022 ACM Symposium on Principles of Distributed Computing
Pages235-245
Number of pages11
ISBN (Electronic)9781450392624
DOIs
StatePublished - 20 Jul 2022
Externally publishedYes
Event41st ACM Symposium on Principles of Distributed Computing, PODC 2022 - Salerno, Italy
Duration: 25 Jul 202229 Jul 2022

Publication series

NameProceedings of the Annual ACM Symposium on Principles of Distributed Computing

Conference

Conference41st ACM Symposium on Principles of Distributed Computing, PODC 2022
Country/TerritoryItaly
CitySalerno
Period25/07/2229/07/22

Keywords

  • biological distributed algorithms
  • consensus protocols
  • passive communication
  • population protocols
  • self-stabilization

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Early Adapting to Trends: Self-Stabilizing Information Spread using Passive Communication'. Together they form a unique fingerprint.

Cite this