An example of stepwise refinement of distributed programs: quiescence detection

Chandy, Mani; Misra, Jayadev

doi:10.1145/5956.5958

Cited by 72 publications

(14 citation statements)

References 13 publications

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“…In other words, to evaluate the termination condition with respect to the snapshot D, as far as a channel is concerned, it is safe to use its state with respect to the earlier snapshot C. Termination detection algorithms based on using marker messages to detect emptiness of channels can be found in [41,11]. For example, the quiescence detection algorithm in [11] uses the ring-based approach to record a snapshot.…”

Section: Channel Flushing-based Approachmentioning

confidence: 99%

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Efficient detection of a locally stable predicate in a distributed system

Atreya

Mittal

Kshemkalyani

et al. 2007

Journal of Parallel and Distributed Computing

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We present an efficient approach to detect a locally stable predicate in a distributed computation. Examples of properties that can be formulated as locally stable predicates include termination and deadlock of a subset of processes. Our algorithm does not require application messages to be modified to carry control information (e.g., vector timestamps), nor does it inhibit events (or actions) of the underlying computation. The worst-case message complexity of our algorithm is O(n(m + 1)), where n is the number of processes in the system and m is the number of events executed by the underlying computation. We show that, in practice, its message complexity should be much lower than its worst-case message complexity. The detection latency of our algorithm is O(d) time units, where d is the diameter of communication topology. Our approach also unifies several known algorithms for detecting termination and deadlock. We also show that our algorithm for detecting a locally stable predicate can be used to efficiently detect a stable predicate that is a monotonic function of other locally stable predicates.

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Section: Channel Flushing-based Approachmentioning

confidence: 99%

“…For example, the quiescence detection algorithm in [11] uses the ring-based approach to record a snapshot. Moreover, a snapshot session is aborted as soon as it is detected that the computation has not terminated or the interval is not quiescent.…”

Section: Channel Flushing-based Approachmentioning

confidence: 99%

Efficient detection of a locally stable predicate in a distributed system

Atreya

Mittal

Kshemkalyani

et al. 2007

Journal of Parallel and Distributed Computing

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“…Hence, q~ is not locally stable Po need only find the greatest elements of N with respect to ;>, which can be done in f2(n 2) time. 5 We call this subcut the latest subcut of N. The latest subcut is clearly maximal in size, since all states that are not part of the latest subcut are not consistent with some state in the latest subcut. This gives us the protocol shown in Fig.…”

Section: Basic Protocolmentioning

confidence: 99%

Efficient detection of a class of stable properties

Marzullo

Sabel

1994

Distrib Comput

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“…The first paradigm could be called coordinated detection, a computation which executes a special protocol to detect the desired condition. Termination [15], quiescence [9], and global snapshot algorithms [8] are representative of this paradigm. Algorithms for detecting the termination of a diffusing computation may be adapted to detecting the completion of the region-labeling process.…”

Section: Stable State Detection Metaphorsmentioning

confidence: 99%

Mixed programming metaphors in a shared dataspace model of concurrency

Roman

Cunningham

1990

IIEEE Trans. Software Eng.

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The term shared dataspace refers to the general class of models and languages in which the principal means of communication is a common, content-addressable data structure called a dataspace. Swarm is a simple language we have used as a vehicle for the investigation of the shared dataspace approach to concurrent computation. It is the first shared dataspace language to have an associated assertional-style proof system. An important feature of Swarm is its ability to bring a variety of programming paradigms under a single, unified model. In a series of related examples we explore Swarm's capacity to express shared-variable, messagepassing, and rule-based computations; to specify synchronous and asynchronous processing modes; and to accommodate highly dynamic program and data structures. Several illustrations make use of a programming construct unique to Swarm, the synchrony relation, and explain how this feature can be used to construct dynamically structured, partially synchronous computations. The paper has three parts: an overview the Swarm programming notation, an examination of Swarm programming strategies via a series of related example programs, and a discussion of the distinctive features of the shared dataspace model. A formal operational model for Swarm is presented in an appendix.

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An example of stepwise refinement of distributed programs: quiescence detection

Cited by 72 publications

References 13 publications

Efficient detection of a locally stable predicate in a distributed system

Efficient detection of a locally stable predicate in a distributed system

Efficient detection of a class of stable properties

Mixed programming metaphors in a shared dataspace model of concurrency

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