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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 7/2017

20.04.2017 | Special Issue Paper

A survey of memory deduplication approaches for intelligent urban computing

verfasst von: Hailiang Huang, Chenggang Yan, Bingtao Liu, Licheng Chen

Erschienen in: Machine Vision and Applications | Ausgabe 7/2017

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Abstract

Limited memory size is considered as a major bottleneck in data centers for intelligent urban computing. It is shown that there exist a large number of duplicated pages when various processes working with big data are hosted in data centers. Memory deduplication aims to automatically eliminate duplicate data in memory. It is an efficient technique to reduce memory requirement. In memory deduplication, pages with same content are detected and merged into a single copy. Recently, several system-level techniques have been proposed to address this issue, in which content-based page sharing (CBPS) is most widely used, since CBPS can be performed transparently in the hypervisor layer without any modification to guest operating systems of data center. In this paper, we survey several techniques for memory deduplication. We also classify these techniques based on their characteristics to highlight their similarities and differences. The aim of this paper is to provide insights to researchers into working of memory deduplication techniques and also to motivate them to propose better intelligent urban computing systems.
Vorheriger Artikel Multiple path exploration for graph matching
Nächster Artikel Human body segmentation based on shape constraint

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Metadaten
Titel
A survey of memory deduplication approaches for intelligent urban computing
verfasst von
Hailiang Huang
Chenggang Yan
Bingtao Liu
Licheng Chen
Publikationsdatum
20.04.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 7/2017
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-017-0834-6

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