A modular approach to shared-memory consensus, with applications to the probabilistic-write model

Aspnes, James

doi:10.1145/1835698.1835802

Cited by 11 publications

(8 citation statements)

References 24 publications

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“…For example, a content-oblivious adversary [Cha96] or value-oblivious adversary [Aum97] is restricted from seeing the values contained in registers or pending write operations and from observing the internal states of processes directly. A location-oblivious adversary [Asp12b] can distinguish between values and the types of pending operations, but can't discriminate between pending operations based one which register they are operating on. These classes of adversaries are modeled by imposing an equivalence relation on partial executions and insisting that the adversary make the same choice of processes to go next in equivalent situations.…”

Section: Role Of the Adversary In Randomized Algorithmsmentioning

confidence: 99%

“…A later paper by Aspnes and Censor [AC09] showed that it was also possible to get an O(n) bound on individual step complexity. For weak adversaries, the best known upper bound on individual step complexity was O(log n) for a long time [Cha96, Aum97, Asp12b], with an O(n) bound on total step complexity for some models [Asp12b]. More recent work has lowered the bound to O(log log n), under the assumption of an oblivious adversary [Asp12a].…”

Section: Historymentioning

confidence: 99%

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Notes on Theory of Distributed Systems

Aspnes¹

2020

Preprint

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Table of contents ii List of figures xiv List of tables xv List of algorithms xvi Preface xxMany parts of these notes were improved by feedback from students taking various versions of this course, as well as others who have kindly pointed out errors in the notes after reading them online. Many of these suggestions, sadly, went unrecorded, so I must apologize to the many students who should be thanked here but whose names I didn't keep track of in the past. However, I can thank Mike Marmar and Hao Pan in particular for suggesting improvements to some of the posted solutions, and Guy Laden for suggesting corrections to Figure 12.1.xx

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Section: Role Of the Adversary In Randomized Algorithmsmentioning

confidence: 99%

Section: Historymentioning

confidence: 99%

Notes on Theory of Distributed Systems

Aspnes¹

2020

Preprint

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“…In task T1, processes proceed in asynchronous rounds aiming at writing a single non-⊥ value to D. An adopt-commit object and a safe agreement object denoted respectively AC[r] and SA[r] are associated with each round r. Following a standard design pattern, e.g., [3,24], the processes that enter round r first try to reach agreement by accessing the safe agreement object SA[r] (line 9) and then check whether agreement has been achieved using the adopt-commit object AC[r] (line 14).…”

Section: Termination Every Operation Performed By a Non-faulty Procementioning

confidence: 99%

Anonymity-Preserving Failure Detectors

Bouzid

Travers

2016

Lecture Notes in Computer Science

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The paper investigates the consensus problem in anonymous, failures prone and asynchronous shared memory systems. It introduces a new class of failure detectors, called anonymitypreserving failures detectors suited to anonymous systems. As its name indicates, a failure detector in this class cannot be relied upon to break anonymity. For example, the anonymous perfect detector AP , which gives at each process an estimation of the number of processes that have failed belongs to this class. The paper then determines the weakest failure detector among this class for consensus. This failure detector, called C, may be seen as a loose failures counter: (1) after a failure occurs, the counter is eventually incremented, and (2) when at least two processes do not fail, it eventually stabilizes. Finally, the paper also introduces failure detector C k , a simple generalization of C and shows that it can be used to solve k-set-agreement, a generalization of consensus in which up to k distinct values may be decided.

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“…A ratifier, or adopt-commit, object [13] is a one-shot shared object that encapsulates the safety property of a round. Hence, from a high-level perspective, the alpha of consensus can be seen as successive (consistent) calls to adopt-commit objects (see [2] or [6,Fig.5]). Our notion of racing, introduced in Section 3.3, aims at further abstracting how processes iteratively access such objects.…”

Section: Related Workmentioning

confidence: 99%

“…We base our construction on two novel abstractions: a grafarius and a racing. A grafarius is close to the more common notion of ratifier, or adopt-commit object [2,13]. A racing object encapsulates the behavior of algorithms that repeatedly access new objects to progress.…”

Section: Introductionmentioning

confidence: 99%

A Practical Distributed Universal Construction with Unknown Participants

Sutra

Rivière

Felber

2014

Lecture Notes in Computer Science

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Modern distributed systems employ atomic read-modify-write primitives to coordinate concurrent operations. Such primitives are typically built on top of a central server, or rely on an agreement protocol. Both approaches provide a universal construction, that is, a general mechanism to construct atomic and responsive objects. These two techniques are however known to be inherently costly. As a consequence, they may result in bottlenecks in applications using them for coordination. In this paper, we investigate another direction to implement a universal construction. Our idea is to delegate the implementation of the universal construction to the clients, and solely implement a distributed shared atomic memory at the servers side. The construction we propose is obstruction-free. It can be implemented in a purely asynchronous manner, and it does not assume the knowledge of the participants. It is built on top of grafarius and racing objects, two novel shared abstractions that we introduce in detail. To assess the benefits of our approach, we present a prototype implementation on top of the Cassandra data store, and compare it empirically to the Zookeeper coordination service.

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A modular approach to shared-memory consensus, with applications to the probabilistic-write model

Cited by 11 publications

References 24 publications

Notes on Theory of Distributed Systems

Notes on Theory of Distributed Systems

Anonymity-Preserving Failure Detectors

A Practical Distributed Universal Construction with Unknown Participants

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