3,427 research outputs found
Consensus with Max Registers
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
We consider the Do-All problem, where cooperating processors need to
complete 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
and a nonconstructive
algorithm that has work . The latter result is close to the
lower bound on work. The effort of each of
these algorithms is proportional to its work when the number of crashes is
bounded above by , for some positive constant . We also present a
nonconstructive algorithm that has effort
How to Elect a Leader Faster than a Tournament
The problem of electing a leader from among contenders is one of the
fundamental questions in distributed computing. In its simplest formulation,
the task is as follows: given 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 -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 , using
point-to-point messages. A non-trivial application of our algorithm is a new
upper bound for the tight renaming problem, assigning items to the
participants in expected time and messages. We
complement our results with lower bound of messages for solving
these two problems, closing the question of their message complexity
Communication-efficient randomized consensus
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 âin flightâ (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
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
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 is both
effectiveness-optimal and work-optimal, for any constant . We
then employ this algorithm to provide a new algorithmic solution for the
Write-All problem which is work optimal for any
.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
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
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
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