Probabilistic modelling of replica divergence

A.I.T. Rowstron; N. Lawrence; C.M. Bishop

doi:10.1109/HOTOS.2001.990061

Probabilistic modelling of replica divergence

A.I.T. Rowstron, N. Lawrence, C.M. Bishop

School of Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

It is common in distributed systems to replicate data. In many cases this data evolves in a consistent fashion, and this evolution can be modelled. A probabilistic model of the evolution allows us to estimate the divergence of the replicas and can be used by the application to alter its behaviour, for example to control synchronisation times, to determine the propagation of writes, and to convey to the user information about how much the data may have evolved. In this paper, we describe how the evolution of the data may be modelled and outline how the probabilistic model may be utilised in various applications, concentrating on a news database example.

Original language	English
Title of host publication	Hot Topics in Operating Systems, 2001. Proceedings of the Eighth Workshop on
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	55-60
Number of pages	6
ISBN (Print)	0-7695-1040-X
DOIs	https://doi.org/10.1109/HOTOS.2001.990061
Publication status	Published - 1 May 2001

Keywords

operating systems (computers)
data replication
distributed systems
operating systems
probabilistic model
probabilistic modelling
replica divergence
synchronisation times
Content addressable storage
Data structures
Databases
Knowledge management

Access to Document

10.1109/HOTOS.2001.990061

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=990061

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abstract = "It is common in distributed systems to replicate data. In many cases this data evolves in a consistent fashion, and this evolution can be modelled. A probabilistic model of the evolution allows us to estimate the divergence of the replicas and can be used by the application to alter its behaviour, for example to control synchronisation times, to determine the propagation of writes, and to convey to the user information about how much the data may have evolved. In this paper, we describe how the evolution of the data may be modelled and outline how the probabilistic model may be utilised in various applications, concentrating on a news database example.",

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KW - operating systems

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KW - synchronisation times

KW - Content addressable storage

KW - Data structures

KW - Databases

KW - Knowledge management

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BT - Hot Topics in Operating Systems, 2001. Proceedings of the Eighth Workshop on

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