3,427 research outputs found

    Consensus with Max Registers

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    We consider the problem of implementing randomized wait-free consensus from max registers under the assumption of an oblivious adversary. We show that max registers solve m-valued consensus for arbitrary m in expected O(log^* n) steps per process, beating the Omega(log m/log log m) lower bound for ordinary registers when m is large and the best previously known O(log log n) upper bound when m is small. A simple max-register implementation based on double-collect snapshots translates this result into an O(n log n) expected step implementation of m-valued consensus from n single-writer registers, improving on the best previously-known bound of O(n log^2 n) for single-writer registers

    Doing-it-All with Bounded Work and Communication

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    We consider the Do-All problem, where pp cooperating processors need to complete tt similar and independent tasks in an adversarial setting. Here we deal with a synchronous message passing system with processors that are subject to crash failures. Efficiency of algorithms in this setting is measured in terms of work complexity (also known as total available processor steps) and communication complexity (total number of point-to-point messages). When work and communication are considered to be comparable resources, then the overall efficiency is meaningfully expressed in terms of effort defined as work + communication. We develop and analyze a constructive algorithm that has work O(t+plog⁥p (plog⁥p+tlog⁥t ))O( t + p \log p\, (\sqrt{p\log p}+\sqrt{t\log t}\, ) ) and a nonconstructive algorithm that has work O(t+plog⁥2p)O(t +p \log^2 p). The latter result is close to the lower bound Ω(t+plog⁥p/log⁥log⁥p)\Omega(t + p \log p/ \log \log p) on work. The effort of each of these algorithms is proportional to its work when the number of crashes is bounded above by c pc\,p, for some positive constant c<1c < 1. We also present a nonconstructive algorithm that has effort O(t+p1.77)O(t + p ^{1.77})

    How to Elect a Leader Faster than a Tournament

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    The problem of electing a leader from among nn contenders is one of the fundamental questions in distributed computing. In its simplest formulation, the task is as follows: given nn processors, all participants must eventually return a win or lose indication, such that a single contender may win. Despite a considerable amount of work on leader election, the following question is still open: can we elect a leader in an asynchronous fault-prone system faster than just running a Θ(log⁥n)\Theta(\log n)-time tournament, against a strong adaptive adversary? In this paper, we answer this question in the affirmative, improving on a decades-old upper bound. We introduce two new algorithmic ideas to reduce the time complexity of electing a leader to O(log⁡∗n)O(\log^* n), using O(n2)O(n^2) point-to-point messages. A non-trivial application of our algorithm is a new upper bound for the tight renaming problem, assigning nn items to the nn participants in expected O(log⁥2n)O(\log^2 n) time and O(n2)O(n^2) messages. We complement our results with lower bound of Ω(n2)\Omega(n^2) messages for solving these two problems, closing the question of their message complexity

    Communication-efficient randomized consensus

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    We consider the problem of consensus in the challenging classic model. In this model, the adversary is adaptive; it can choose which processors crash at any point during the course of the algorithm. Further, communication is via asynchronous message passing: there is no known upper bound on the time to send a message from one processor to another, and all messages and coin flips are seen by the adversary. We describe a new randomized consensus protocol with expected message complexity O(n2log2n) when fewer than n / 2 processes may fail by crashing. This is an almost-linear improvement over the best previously known protocol, and within logarithmic factors of a known Ω(n2) message lower bound. The protocol further ensures that no process sends more than O(nlog3n) messages in expectation, which is again within logarithmic factors of optimal. We also present a generalization of the algorithm to an arbitrary number of failures t, which uses expected O(nt+t2log2t) total messages. Our approach is to build a message-efficient, resilient mechanism for aggregating individual processor votes, implementing the message-passing equivalent of a weak shared coin. Roughly, in our protocol, a processor first announces its votes to small groups, then propagates them to increasingly larger groups as it generates more and more votes. To bound the number of messages that an individual process might have to send or receive, the protocol progressively increases the weight of generated votes. The main technical challenge is bounding the impact of votes that are still “in flight” (generated, but not fully propagated) on the final outcome of the shared coin, especially since such votes might have different weights. We achieve this by leveraging the structure of the algorithm, and a technical argument based on martingale concentration bounds. Overall, we show that it is possible to build an efficient message-passing implementation of a shared coin, and in the process (almost-optimally) solve the classic consensus problem in the asynchronous message-passing model

    Randomized Mutual Exclusion with Constant Amortized RMR Complexity on the DSM

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    International audienceIn this paper we settle an open question by determining the remote memory reference (RMR) complexity of randomized mutual exclusion, on the distributed shared memory model (DSM) with atomic registers, in a weak but natural (and stronger than oblivious) adversary model. In particular, we present a mutual exclusion algorithm that has constant expected amortized RMR complexity and is deterministically deadlock free. Prior to this work, no randomized algorithm with o(log n/ log log n) RMR complexity was known for the DSM model. Our algorithm is fairly simple, and compares favorably with one by Bender and Gilbert (FOCS 2011) for the CC model, which has expected amortized RMR complexity O(log^2 log n) and provides only probabilistic deadlock freedom

    Solving the At-Most-Once Problem with Nearly Optimal Effectiveness

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    We present and analyze a wait-free deterministic algorithm for solving the at-most-once problem: how m shared-memory fail-prone processes perform asynchronously n jobs at most once. Our algorithmic strategy provides for the first time nearly optimal effectiveness, which is a measure that expresses the total number of jobs completed in the worst case. The effectiveness of our algorithm equals n-2m+2. This is up to an additive factor of m close to the known effectiveness upper bound n-m+1 over all possible algorithms and improves on the previously best known deterministic solutions that have effectiveness only n-log m o(n). We also present an iterative version of our algorithm that for any m=O(n/log⁥n3+Ï”)m = O\left(\sqrt[3+\epsilon]{n/\log n}\right) is both effectiveness-optimal and work-optimal, for any constant Ï”>0\epsilon > 0. We then employ this algorithm to provide a new algorithmic solution for the Write-All problem which is work optimal for any m=O(n/log⁥n3+Ï”)m=O\left(\sqrt[3+\epsilon]{n/\log n}\right).Comment: Updated Version. A Brief Announcement was published in PODC 2011. An Extended Abstract was published in the proceeding of ICDCN 2012. A full version was published in Theoretical Computer Science, Volume 496, 22 July 2013, Pages 69 - 8

    Fast Deterministic Consensus in a Noisy Environment

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    It is well known that the consensus problem cannot be solved deterministically in an asynchronous environment, but that randomized solutions are possible. We propose a new model, called noisy scheduling, in which an adversarial schedule is perturbed randomly, and show that in this model randomness in the environment can substitute for randomness in the algorithm. In particular, we show that a simplified, deterministic version of Chandra's wait-free shared-memory consensus algorithm (PODC, 1996, pp. 166-175) solves consensus in time at most logarithmic in the number of active processes. The proof of termination is based on showing that a race between independent delayed renewal processes produces a winner quickly. In addition, we show that the protocol finishes in constant time using quantum and priority-based scheduling on a uniprocessor, suggesting that it is robust against the choice of model over a wide range.Comment: Typographical errors fixe

    Randomized protocols for asynchronous consensus

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    The famous Fischer, Lynch, and Paterson impossibility proof shows that it is impossible to solve the consensus problem in a natural model of an asynchronous distributed system if even a single process can fail. Since its publication, two decades of work on fault-tolerant asynchronous consensus algorithms have evaded this impossibility result by using extended models that provide (a) randomization, (b) additional timing assumptions, (c) failure detectors, or (d) stronger synchronization mechanisms than are available in the basic model. Concentrating on the first of these approaches, we illustrate the history and structure of randomized asynchronous consensus protocols by giving detailed descriptions of several such protocols.Comment: 29 pages; survey paper written for PODC 20th anniversary issue of Distributed Computin
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