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Wyszukujesz frazę "Apache Spark" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Influence of YARN schedulers on power consumption and processing time for various big data benchmarks
Autorzy:
Drypczewski, Krzysztof
Proficz, Jerzy
Stepnowski, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/1955269.pdf
Data publikacji:
2018
Wydawca:
Politechnika Gdańska
Tematy:
Apache Spark
YARN
big data
green computing
Sentinel
Tera Sort
word count
benchmarks
scheduler
Opis:
Climate change caused by human activities can influence the lives of everybody on the planet. The environmental concerns must be taken into consideration by all fields of study includingICT. Green Computing aims to reduce negative effects of IT on the environment while, at the same time, maintaining all of the possible benefits it provides. Several Big Data platforms like Apache Spark or YARN have become widely used in analytics and High-Performance Computing systems due to the reliability and usability of Map Reduce implementations. The authors research the power consumption and energy efficiency of Hadoop YARN schedulers using Apache Spark under three different workloads. The test cases include: sorting large binary files,counting unique words in large text files and processing satellite imagery from the Sentinel-2mission. The presented results show small (2%–11%) but distinct differences in the power consumption of FIFO and FAIR schedulers.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2018, 22, 4; 303--312
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Processing of satellite data in the cloud
Autorzy:
Proficz, J.
Drypczewski, K.
Powiązania:
https://bibliotekanauki.pl/articles/1940555.pdf
Data publikacji:
2017
Wydawca:
Politechnika Gdańska
Tematy:
Apache Spark
satellite data
Sentinel-2
ESA
big data
cloud
OpenStack
dane satelitarne
duże zbiory danych
chmura
Opis:
The dynamic development of digital technologies, especially those dedicated to devices generating large data streams, such as all kinds of measurement equipment (temperature and humidity sensors, cameras, radio-telescopes and satellites – Internet of Things) enables more in-depth analysis of the surrounding reality, including better understanding of various natural phenomenon, starting from atomic level reactions, through macroscopic processes (e.g. meteorology) to observation of the Earth and the outer space. On the other hand such a large quantitative improvement requires a great number of processing and storage resources, resulting in the recent rapid development of Big Data technologies. Since 2015, the European Space Agency (ESA) has been providing a great amount of data gathered by exploratory equipment: a collection of Sentinel satellites – which perform Earth observation using various measurement techniques. For example Sentinel-2 provides a stream of digital photos, including images of the Baltic Sea and the whole territory of Poland. This data is used in an experimental installation of a Big Data processing system based on the open source software at the Academic Computer Center in Gdansk. The center has one of the most powerful supercomputers in Poland – the Tryton computing cluster, consisting of 1600 nodes interconnected by a fast Infiniband network (56 Gbps) and over 6 PB of storage. Some of these nodes are used as a computational cloud supervised by an OpenStack platform, where the Sentinel-2 data is processed. A subsystem of the automatic, perpetual data download to object storage (based on Swift) is deployed, the required software libraries for the image processing are configured and the Apache Spark cluster has been set up. The above system enables gathering and analysis of the recorded satellite images and the associated metadata, benefiting from the parallel computation mechanisms. This paper describes the above solution including its technical aspects.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2017, 21, 4; 365-377
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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