Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Ilya Verbitskiy"'
Autor:
Odej Kao, Sasho Nedelkoski, César A. F. De Rose, Ilya Verbitskiy, Miguel G. Xavier, Vinicius Meyer, Lauritz Thamsen, Vinh Thuy Tran
Publikováno v:
Euro-Par 2019: Parallel Processing Workshops ISBN: 9783030483395
Distributed data processing systems like MapReduce, Spark, and Flink are popular tools for analysis of large datasets with cluster resources. Yet, users often overprovision resources for their data processing jobs, while the resource usage of these j
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7b890aea164dba5b02848f2941833554
https://doi.org/10.1007/978-3-030-48340-1_40
https://doi.org/10.1007/978-3-030-48340-1_40
Publikováno v:
Services Transactions on Big Data. 4:33-47
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::225bc71a05f1cf88eb2aeaed29bb47b2
https://doi.org/10.29268/stbd.2017.4.1.3
https://doi.org/10.29268/stbd.2017.4.1.3
Publikováno v:
EDGE
The use of machine learning modeling techniques enables smart IoT applications in geo-distributed infrastructures such as in the areas of Industry 4.0, smart cities, autonomous driving, and telemedicine. The data for these models is continuously emit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6c355231394f89ace9df7996cfdad63
https://doi.org/10.1109/edge.2019.00015
https://doi.org/10.1109/edge.2019.00015
Publikováno v:
IEEE BigData
Low-latency processing of data streams from distributed sensors is becoming increasingly important for a growing number of IoT applications. In these environments sensor data collected at the edge of the network is typically transmitted in a number o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5aff266325d001bd9fdc10f7f7ba54d
https://doi.org/10.1109/bigdata.2018.8622651
https://doi.org/10.1109/bigdata.2018.8622651
Publikováno v:
CloudCom
Distributed dataflow systems have been developed to help users analyze and process large datasets. While they make it easier for users to develop massively-parallel programs, users still have to choose the amount of resources for the execution of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e490ecbaa4d701c6c5b716484172cbda
https://doi.org/10.1109/cloudcom2018.2018.00029
https://doi.org/10.1109/cloudcom2018.2018.00029
Publikováno v:
CloudCom
Distributed dataflow systems like MapReduce, Spark, and Flink help users in analyzing large datasets with a set of cluster resources. Performance modeling and runtime prediction is then used for automatically allocating resources for specific perform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::943b69120793e1270ce43db090c5a6e0
https://doi.org/10.1109/cloudcom.2017.37
https://doi.org/10.1109/cloudcom.2017.37
Publikováno v:
IPCCC
Distributed dataflow systems like Spark or Flink enable users to analyze large datasets. Users create programs by providing sequential user-defined functions for a set of well-defined operations, select a set of resources, and the systems automatical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7062cc58d4b1899c592db5940d572b3d
https://doi.org/10.1109/pccc.2016.7820629
https://doi.org/10.1109/pccc.2016.7820629
Publikováno v:
UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld
With the increasing amount of available data, distributed data processing systems like Apache Flink, Apache Spark have emerged that allow to analyze large-scale datasets. However, such engines introduce significant computational overhead compared to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c2da6a2b167eb1a7e82bd26dfe2ee2ff
https://doi.org/10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0114
https://doi.org/10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0114