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A Scaling Analysis of a Transient Stochastic Network

Part of:Special processesComputer system organizationLimit theoremsOperations research and management science

Published online by Cambridge University Press:  22 February 2016

Mathieu Feuillet and
Philippe Robert
Mathieu Feuillet*
Affiliation:
INRIA-Rocquencourt
Philippe Robert*
Affiliation:
INRIA-Rocquencourt
*
∗ Postal address: INRIA-Rocquencourt, Domaine de Voluceau, 78153 Le Chesnay, France.
∗ Postal address: INRIA-Rocquencourt, Domaine de Voluceau, 78153 Le Chesnay, France.
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Abstract

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In this paper we use a simple transient Markov process with an absorbing point to investigate the qualitative behavior of a large-scale storage network of nonreliable file servers across which files can be duplicated. When the size of the system goes to ∞, we show that there is a critical value for the maximum number of files per server such that, below this quantity, most files have a maximum number of copies. Above this value, the network loses a significant number of files until some equilibrium is reached. When the network is stable, we show that, with convenient time scales, the evolution of the network towards the absorbing state can be described via a stochastic averaging principle.

Keywords

Distributed system with breakdowntime scaletransient Markov chain with absorbing stateSkorokhod problem

MSC classification

Primary:60K25: Queueing theory60F05: Central limit and other weak theorems
Secondary:68M14: Distributed systems90B05: Inventory, storage, reservoirs

Information

Type
General Applied Probability
Information
Advances in Applied Probability , Volume 46 , Issue 2 , June 2014, pp. 516 - 535
Copyright
© Applied Probability Trust 

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