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Stability of the stochastic matching model

Authors: Mairesse, Jean; Moyal, Pascal;

Stability of the stochastic matching model

Abstract

Abstract We introduce and study a new model that we call the matching model. Items arrive one by one in a buffer and depart from it as soon as possible but by pairs. The items of a departing pair are said to be matched. There is a finite set of classes đť’± for the items, and the allowed matchings depend on the classes, according to a matching graph on đť’±. Upon arrival, an item may find several possible matches in the buffer. This indeterminacy is resolved by a matching policy. When the sequence of classes of the arriving items is independent and identically distributed, the sequence of buffer-content is a Markov chain, whose stability is investigated. In particular, we prove that the model may be stable if and only if the matching graph is nonbipartite.

Country
France
Keywords

graphs, 68M20, [MATH.MATH-PR] Mathematics [math]/Probability [math.PR], matching, Probability (math.PR), graph, stability, Queueing theory (aspects of probability theory), Markov chains (discrete-time Markov processes on discrete state spaces), [INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM], Markovian queueing theory, Edge subsets with special properties (factorization, matching, partitioning, covering and packing, etc.), 60J10, 60K25, 68M20, FOS: Mathematics, 60J10, 05C75, Structural characterization of families of graphs, Queues and service in operations research, Mathematics - Probability, Performance evaluation, queueing, and scheduling in the context of computer systems

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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!
71
Top 1%
Top 10%
Top 10%
Green
bronze