Distributed Thematic Mapping Performance Optimization in Public Clouds

Authors: Javier Lozano Silva Marco Quartulli Jon Egaña Zubia Igor García Olaizola Ekaitz Zulueta Guerrero

Date: 15.03.2016


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Abstract

Global distributed thematic mapping in public clouds requires optimized data flows. These optimized flows can be the result of the analysis by Machine Learning (ML) of a deeply sensorized mapping system. In this sense, distributed global mapping requires a monitoring system that allows to understand the internal working of the system and enables the implementation of corrective actions to increase system performance. This work presents an implementation of a system monitoring framework and the obtained analysis results.

BIB_text

@Article {
title = {Distributed Thematic Mapping Performance Optimization in Public Clouds},
pages = {99-102},
keywds = {

System monitoring, big data, web mapping


}
abstract = {

Global distributed thematic mapping in public clouds requires optimized data flows. These optimized flows can be the result of the analysis by Machine Learning (ML) of a deeply sensorized mapping system. In this sense, distributed global mapping requires a monitoring system that allows to understand the internal working of the system and enables the implementation of corrective actions to increase system performance. This work presents an implementation of a system monitoring framework and the obtained analysis results.


}
isbn = {978-92-79-56980-7},
doi = {10.2788/854791},
date = {2016-03-15},
year = {2016},
}
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