Fully Adaptive Self-Stabilizing Transformer for LCL Problems

Fuente: arXiv
Salvato in:
Dettagli Bibliografici
Autori principali: Bitton, Shimon, Emek, Yuval, Izumi, Taisuke, Kutten, Shay
Natura: Preprint
Pubblicazione: 2021
Soggetti:
Accesso online:
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
_version_ 1866910591561498624
author Bitton, Shimon
Emek, Yuval
Izumi, Taisuke
Kutten, Shay
author_facet Bitton, Shimon
Emek, Yuval
Izumi, Taisuke
Kutten, Shay
contents The first generic self-stabilizing transformer for local problems in a constrained bandwidth model is introduced. This transformer can be applied to a wide class of locally checkable labeling (LCL) problems, converting a given fault free synchronous algorithm that satisfies certain conditions into a self-stabilizing synchronous algorithm for the same problem. The resulting self-stabilizing algorithms are anonymous, size-uniform, and \emph{fully adaptive} in the sense that their time complexity is bounded as a function of the number $k$ of nodes that suffered faults (possibly at different times) since the last legal configuration. Specifically, for graphs whose degrees are up-bounded by $Δ$, the algorithms produced by the transformer stabilize in time proportional to $\log (k + Δ)$ in expectation, independently of the number of nodes in the graph. As such, the transformer is applicable also for infinite graphs (with degree bound $Δ$). Another appealing feature of the transformer is its small message size overhead. The transformer is applied to known algorithms (or simple variants thereof) for some classic LCL problems, producing the first anonymous size-uniform self-stabilizing algorithms for these problems that are provably fully adaptive. From a technical point of view, the transformer's key design feature is a novel probabilistic tool that allows different nodes to act in synchrony even though their clocks may have been adversarially manipulated.
format Preprint
id arxiv_https___arxiv_org_abs_2105_09756
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Fully Adaptive Self-Stabilizing Transformer for LCL Problems
Bitton, Shimon
Emek, Yuval
Izumi, Taisuke
Kutten, Shay
Distributed, Parallel, and Cluster Computing
The first generic self-stabilizing transformer for local problems in a constrained bandwidth model is introduced. This transformer can be applied to a wide class of locally checkable labeling (LCL) problems, converting a given fault free synchronous algorithm that satisfies certain conditions into a self-stabilizing synchronous algorithm for the same problem. The resulting self-stabilizing algorithms are anonymous, size-uniform, and \emph{fully adaptive} in the sense that their time complexity is bounded as a function of the number $k$ of nodes that suffered faults (possibly at different times) since the last legal configuration. Specifically, for graphs whose degrees are up-bounded by $Δ$, the algorithms produced by the transformer stabilize in time proportional to $\log (k + Δ)$ in expectation, independently of the number of nodes in the graph. As such, the transformer is applicable also for infinite graphs (with degree bound $Δ$). Another appealing feature of the transformer is its small message size overhead. The transformer is applied to known algorithms (or simple variants thereof) for some classic LCL problems, producing the first anonymous size-uniform self-stabilizing algorithms for these problems that are provably fully adaptive. From a technical point of view, the transformer's key design feature is a novel probabilistic tool that allows different nodes to act in synchrony even though their clocks may have been adversarially manipulated.
title Fully Adaptive Self-Stabilizing Transformer for LCL Problems
topic Distributed, Parallel, and Cluster Computing
url https://arxiv.org/abs/2105.09756