The Strong At-Most-Once Problem

Kentros, Sotirios; Kari, Chadi; Kiayias, Aggelos

doi:10.1007/978-3-642-33651-5_27

Cited by 5 publications

(9 citation statements)

References 24 publications

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“…Algorithm Effort-Priority can be initialized with T 1 satisfying Equation (40) so that all processors halt by round T 1 while executing Part-One provided that the number of crashes in the execution is at most f 1 .…”

Section: Lemma 31mentioning

confidence: 99%

“…Kentros and Kiayias [41] solve At-Most-Once with improved effectiveness. Kentros et al [40] introduced the Strong-At-Most-Once problem, in which all tasks must be performed in the absence of crashes, and showed that it has consensus number 2. Censor-Hillel [8] used a randomized wait-free solution to multi-valued consensus as a building block in an algorithm for At-Most-Once of optimal effectiveness.…”

Section: Introductionmentioning

confidence: 99%

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Doing-it-All with bounded work and communication

Chlebus

Gąsieniec

Kowalski

et al. 2017

Information and Computation

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We consider the Do-All problem, where p cooperating processors need to complete t 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 + p log p ( √ p log p + √ t log t )) and a nonconstructive algorithm that has work O(t + p log 2 p). The latter result is close to the lower bound Ω(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 p, for some positive constant c < 1. We also present a nonconstructive algorithm that has effort O(t + p 1.77 ). The results of this paper were announced in a preliminary form in [11] and published in their final version as [10].

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Section: Lemma 31mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Doing-it-All with bounded work and communication

Chlebus

Gąsieniec

Kowalski

et al. 2017

Information and Computation

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“…Following the conference version of this paper [25] and motivated by the difficulty of implementing wait-free deterministic solutions for the at-most-once problem that are effectiveness optimal, Kentros et al [24] introduced the strong at-most-once problem and studied its feasibility. The strong at-most-once problem refers to the setting where effectiveness is measured only in terms of the jobs that need to be executed and the processes that took part in the computation and crashed.…”

Section: Introductionmentioning

confidence: 99%

“…The strong at-most-once problem demands solutions that are adaptive, in the sense that the effectiveness depends only on the behavior of processes that participate in the execution. In this manner trivial solutions are excluded and, as demonstrated in [24], processes have to solve an agreement primitive in order to make progress and provide a solution for the problem. Kentros et al [24] prove that the strong at-most-once problem has consensus number 2 as defined by Herlihy [21] and observe that it belongs in the Common2 class as defined by Afek et al [1].…”

Section: Introductionmentioning

confidence: 99%

Solving the at-most-once problem with nearly optimal effectiveness

Kentros

Kiayias

2013

Theoretical Computer Science

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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( 3+ǫ n/ log n) is both effectiveness-optimal and work-optimal, for any constant ǫ > 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( 3+ǫ n/ log n). 1The problem becomes very challenging for autonomous processes in a system with concurrent invocations on multiple objects. At-most-once semantics have been thoroughly studied in the context of at-most-once message delivery [8,30,33] and at-most-once process invocation for RPC [6,31,37]. However, finding effective solutions for asynchronous shared-memory multiprocessors, in terms of how many at-most-once invocations can be performed by the cooperating processes, is largely an open problem. Solutions for the at-most-once problem, using only atomic read/write memory, and without specialized hardware support such as conditional writing, provide a useful tool in reasoning about the safety properties of applications developed for a variety of multiprocessor systems, including those not supporting bus-interlocking instructions and multi-core systems. Specifically, in recent years, attention has shifted from increasing clock speed towards chip multiprocessing, in order to increase the performance of systems. Because of the differences in each multi-core system, asynchronous shared memory is becoming an important abstraction for arguing about the safety properties of parallel applications in such systems. In the next years, one can expect chip multiprocessing to appear in a wide range of applications, many of which will have components that need to satisfy at-most-once semantics in order to guarantee safety. Such applications may include autonomous robotic devices, robotic devices for assisted living, automation in production lines or medical facilities. In such applications performing specific jobs at-most-once may be of paramount importance for safety of patients, the workers in a facility, or the devices themselves. Such jobs could be the triggering of a motor in a robotic arm, the activation of the X-ray gun in an X-ray machine, or supplying a dosage of medicine to a patient.Perhaps the most important question in this area is devising algorithms for the at-most-once problem with good effectiveness. The complexity measure of effectiveness [26] describes the number of jobs completed (at-mo...

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“…Recent work on the do-all problem considered at-most-once semantics in shared memory, e.g. [20,21], instead of at-least-once. Assuming stronger synchronization primitives than [20,21], our solution gives exactly-once semantics (see Section 2 for details).…”

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confidence: 99%

Dynamic Task Allocation in Asynchronous Shared Memory

Alistarh¹,

Aspnes²,

Bender³

et al. 2013

Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms

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Task allocation is a classic distributed problem in which a set of p potentially faulty processes must cooperate to perform a set of tasks. This paper considers a new dynamic version of the problem, in which tasks are injected adversarially during an asynchronous execution. We give the first asynchronous shared-memory algorithm for dynamic task allocation, and we prove that our solution is optimal within logarithmic factors. The main algorithmic idea is a randomized concurrent data structure called a dynamic to-do tree, which allows processes to pick new tasks to perform at random from the set of available tasks, and to insert tasks at random empty locations in the data structure. Our analysis shows that these properties avoid duplicating work unnecessarily. On the other hand, since the adversary controls the input as well the scheduling, it can induce executions where lots of processes contend for a few available tasks, which is inefficient. However, we prove that every algorithm has the same problem: given an arbitrary input, if OPT is the worst-case complexity of the optimal algorithm on that input, then the expected work complexity of our algorithm on the same input is O(OPT log 3 m), where m is an upper bound on the number of tasks that are present in the system at any given time.

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The Strong At-Most-Once Problem

Cited by 5 publications

References 24 publications

Doing-it-All with bounded work and communication

Doing-it-All with bounded work and communication

Solving the at-most-once problem with nearly optimal effectiveness

Dynamic Task Allocation in Asynchronous Shared Memory

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