Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 1903.03487

Set CRDT com M\'ultiplas Pol\'iticas de Resolu\c{c}\~ao de Conflitos

André Rijo·Carla Ferreira·Nuno Preguiça
View original source ↗Read source document ↗Export metadata ↓

Abstract

Um CRDT \'e um tipo de dados que pode ser replicado e modificado concorrentemente sem coordena\c{c}\~ao, garantindo-se a converg\^encia das r\'eplicas atrav\'es da resolu\c{c}\~ao autom\'atica de conflitos. Cada CRDT implementa uma pol\'itica espec\'ifica para resolver conflitos. Por exemplo, um conjunto CRDT add-wins d\'a prioridade ao "add" aquando da execu\c{c}\~ao concorrente de um "add" e "rem" do mesmo elemento. Em algumas aplica\c{c}\~oes pode ser necess\'ario usar diferentes pol\'iticas para diferentes execu\c{c}\~oes de uma opera\c{c}\~ao -- por exemplo, uma aplica\c{c}\~ao que utilize um conjunto CRDT add-wins pode querer que alguns "removes" ganhem sobre "adds" concorrentes. Neste artigo \'e apresentado e avaliado o desenho dum conjunto CRDT que implementa as sem\^anticas referidas. --- Conflict-Free Replicated Data Types (CRDTs) allow objects to be replicated and concurrently modified without coordination. CRDTs solve conflicts automatically and provide eventual consistency. Typically each CRDT uses a specific policy for solving conflicts. For example, in an add-wins set CRDT, when an element is concurrently add and removed in different replicas, priority is given to add, i.e., the element stays in the set. Unfortunately, this may be inadequate for some applications - it may be desired to overrule the default policy for some operation executions. For example, an application using an add-wins set may want some removes to win over concurrent adds. This paper present the design of a set CRDT that implements such semantics.

Explore related subjects

cs.DS

Keep this discovery

BibTeXRIS

André Rijo, Carla Ferreira, Nuno Preguiça. 2019-03-08. Set CRDT com M\'ultiplas Pol\'iticas de Resolu\c{c}\~ao de Conflitos. https://arxiv.org/abs/1903.03487

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS↗
arXiv2026

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS↗