Distributed Thematic Mapping Performance Optimization in Public Clouds
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
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},
}
