Population Stability: Regulating Size in the Presence of an Adversary
Name
1803.02540.pdf
Description
Submitted version
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277.88 KB
Format
Adobe PDF
Checksum (MD5)
a271abf7bea40b5128cbf7d0b305a677
Author(s)
Goldwasser, Shafrira
Ostrovsky, Rafail
Scafuro, Alessandra
Sealfon, Adam
Date Issued
July 2018
Journal
Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing
Publisher
Association for Computing Machinery (ACM)
Citation
Goldwasser, Shafi et al. "Population Stability: Regulating Size in the Presence of an Adversary." Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing, July 2018, Egham, United Kingdom, Association for Computing Machinery, 2018. © 2018 The Authors
Version
Original manuscript
Abstract
We introduce a new coordination problem in distributed computing that we call the population stability problem. A system of agents each with limited memory and communication, as well as the ability to replicate and self-destruct, is subjected to attacks by a worst-case adversary that can at a bounded rate (1) delete agents chosen arbitrarily and (2) insert additional agents with arbitrary initial state into the system. The goal is perpetually to maintain a population whose size is within a constant factor of the target size N. The problem is inspired by the ability of complex biological systems composed of a multitude of memory-limited individual cells to maintain a stable population size in an adverse environment. Such biological mechanisms allow organisms to heal after trauma or to recover from excessive cell proliferation caused by inflammation, disease, or normal development. We present a population stability protocol in a communication model that is a synchronous variant of the population model of Angluin et al. In each round, pairs of agents selected at random meet and exchange messages, where at least a constant fraction of agents is matched in each round. Our protocol uses three-bit messages and (log2 N) states per agent. We emphasize that our protocol can handle an adversary that can both insert and delete agents, a setting in which existing approximate counting techniques do not seem to apply. The protocol relies on a novel coloring strategy in which the population size is encoded in the variance of the distribution of colors. Individual agents can locally obtain a weak estimate of the population size by sampling from the distribution, and make individual decisions that robustly maintain a stable global population size.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1145/3212734.3212747
