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Combining Timed Coordination Primitives and Probabilistic Tuple Spaces

Authors: Mario Bravetti; Diego Latella; Michele Loreti; Mieke Massink; Gianluigi Zavattaro;

Combining Timed Coordination Primitives and Probabilistic Tuple Spaces

Abstract

In this paper we present an integration of PLinda, a probabilistic extension of Linda, and StoKLAIM, a stochastic extension of KLAIM. In the resulting language, StoPKLAIM, the execution time of coordination primitives is modeled by means of exponentially distributed random variables, as in StoKLAIM, the choice of the primitive to be executed among conflicting ones is thus resolved by the race condition principle, and the choice of the tuple to be retrieved by a single input/read operation in case of multiple matching tuples is governed by the weight-based probabilistic access policy of PLinda. The language represents a natural development and integration of previous results of the SENSORIA Project in the area of probabilistic and time-stochastic extensions of Tuple Space based coordination languages. The formal operational semantics of StoPKLAIM is presented and an example of modeling is provided.

Keywords

F.4 Mathematical Logic and Formal Languages, Distributed Mobile Computing, 68Q85 Models and methods for concurrent and distributed computing (process algebras, Stochastic Process Languages, Probabilistic Process Languages, transition nets, probabilistic, tuple spaces; coordination primitives; stochastic process algebra, Tuple Space Based Languages, parallel, etc.), bisimulation, 68Q10 Modes of computation (nondeterministic, H.1 Models and Principles, interactive

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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