交换共识的空间复杂度

IF 2.3 2区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Journal of the ACM Pub Date : 2023-11-02 DOI:10.1145/3631390
Sean Ovens
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引用次数: 0

摘要

近三十年前,研究表明需要\(\Omega (\sqrt {n}) \)读写寄存器来解决n个进程之间的随机无等待共识。这个下限在2018年被改进为n个寄存器,这与已知算法完全匹配。\(\Omega (\sqrt {n}) \)空间复杂度下限实际上适用于一类称为无历史对象的对象,其中包括寄存器、测试和设置对象以及可读交换对象。然而,每个已知的无历史对象的n进程无阻塞共识算法都使用Ω (n)个对象。本文给出了两类无历史对象共识算法的第一个Ω (n)空间复杂度下界。首先,我们证明了任何交换对象的无阻碍一致性算法至少使用n−1个对象。更一般地说,我们证明了任何来自交换对象的无阻碍k集协议算法至少使用\(\lceil \frac{n}{k}\rceil - 1 \)对象。k集协议问题是共识的概括,其中进程同意不超过k个不同的输出值。这是当k >时,求解与交换对象的k集协议的空间复杂度的第一个非常数下界;1. 我们还提出了一种基于n−k交换对象的无阻碍k集协议算法,该算法与k = 1时的下界完全匹配。其次,我们证明了任何来自域大小为b的可读交换对象的无障碍二进制一致性算法至少使用\(\frac{n-2}{3b+1} \)对象。当b为常数时,该算法从具有无界域的可读交换对象中渐近匹配已知的无阻碍一致性算法。由于任何无历史对象都可以通过具有相同域的可读交换对象来模拟,因此我们的结果表明,任何来自域大小为b的无历史对象的无障碍共识算法都至少使用\(\frac{n-2}{3b+1} \)对象。对于b = 2,我们给出了一个稍微好一点的n - 2下界。利用域大小为2的2个n−1个可读交换对象,渐近地匹配我们的下界,给出了一种无阻碍的二元一致性算法。
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The Space Complexity of Consensus from Swap
Nearly thirty years ago, it was shown that \(\Omega (\sqrt {n}) \) read/write registers are needed to solve randomized wait-free consensus among n processes. This lower bound was improved to n registers in 2018, which exactly matches known algorithms. The \(\Omega (\sqrt {n}) \) space complexity lower bound actually applies to a class of objects called historyless objects, which includes registers, test-and-set objects, and readable swap objects. However, every known n -process obstruction-free consensus algorithm from historyless objects uses Ω ( n ) objects. In this paper, we give the first Ω ( n ) space complexity lower bounds on consensus algorithms for two kinds of historyless objects. First, we show that any obstruction-free consensus algorithm from swap objects uses at least n − 1 objects. More generally, we prove that any obstruction-free k -set agreement algorithm from swap objects uses at least \(\lceil \frac{n}{k}\rceil - 1 \) objects. The k -set agreement problem is a generalization of consensus in which processes agree on no more than k different output values. This is the first non-constant lower bound on the space complexity of solving k -set agreement with swap objects when k > 1. We also present an obstruction-free k -set agreement algorithm from n − k swap objects, which exactly matches our lower bound when k = 1. Second, we show that any obstruction-free binary consensus algorithm from readable swap objects with domain size b uses at least \(\frac{n-2}{3b+1} \) objects. When b is a constant, this asymptotically matches the best known obstruction-free consensus algorithms from readable swap objects with unbounded domains. Since any historyless object can be simulated by a readable swap object with the same domain, our results imply that any obstruction-free consensus algorithm from historyless objects with domain size b uses at least \(\frac{n-2}{3b+1} \) objects. For b = 2, we show a slightly better lower bound of n − 2. There is an obstruction-free binary consensus algorithm using 2 n − 1 readable swap objects with domain size 2, asymptotically matching our lower bound.
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来源期刊
Journal of the ACM
Journal of the ACM 工程技术-计算机:理论方法
CiteScore
7.50
自引率
0.00%
发文量
51
审稿时长
3 months
期刊介绍: The best indicator of the scope of the journal is provided by the areas covered by its Editorial Board. These areas change from time to time, as the field evolves. The following areas are currently covered by a member of the Editorial Board: Algorithms and Combinatorial Optimization; Algorithms and Data Structures; Algorithms, Combinatorial Optimization, and Games; Artificial Intelligence; Complexity Theory; Computational Biology; Computational Geometry; Computer Graphics and Computer Vision; Computer-Aided Verification; Cryptography and Security; Cyber-Physical, Embedded, and Real-Time Systems; Database Systems and Theory; Distributed Computing; Economics and Computation; Information Theory; Logic and Computation; Logic, Algorithms, and Complexity; Machine Learning and Computational Learning Theory; Networking; Parallel Computing and Architecture; Programming Languages; Quantum Computing; Randomized Algorithms and Probabilistic Analysis of Algorithms; Scientific Computing and High Performance Computing; Software Engineering; Web Algorithms and Data Mining
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