- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2196-1115
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Estimating runtime of a job in Hadoop MapReduce ; volume:7 ; number:1 ; day:6 ; month:7 ; year:2020 ; pages:1-18 ; date:12.2020
Journal of Big Data ; 7, Heft 1 (6.7.2020), 1-18, 12.2020
- Creator
-
Peyravi, Narges
Moeini, Ali
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1186/s40537-020-00319-4
- URN
-
urn:nbn:de:101:1-2020090520210576523572
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:45 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Peyravi, Narges
- Moeini, Ali
- SpringerLink (Online service)
Other Objects (12)
MapReduce particle filtering with exact resampling and deterministic runtime
MapReduce with Deltas
CLQLMRS: improving cache locality in MapReduce job scheduling using Q-learning
Predictive modelling of MapReduce job performance in cloud environments using machine learning techniques
Pairwise Element Computation with MapReduce
Computing n-Gram Statistics in MapReduce
A gray-box modeling methodology for runtime prediction of Apache Spark jobs
A gray-box modeling methodology for runtime prediction of Apache Spark jobs
Consistency model for runtime objects in the Open Community Runtime
DI-MMAP—a scalable memory-map runtime for out-of-core data-intensive applications
Effiziente MapReduce-Parallelisierung von Entity Resolution-Workflows
Scalable Multi-Agent Simulation based on MapReduce
MapReduce particle filtering with exact resampling and deterministic runtime
MapReduce with Deltas
CLQLMRS: improving cache locality in MapReduce job scheduling using Q-learning
Predictive modelling of MapReduce job performance in cloud environments using machine learning techniques
Pairwise Element Computation with MapReduce
Computing n-Gram Statistics in MapReduce
A gray-box modeling methodology for runtime prediction of Apache Spark jobs
A gray-box modeling methodology for runtime prediction of Apache Spark jobs
Consistency model for runtime objects in the Open Community Runtime
DI-MMAP—a scalable memory-map runtime for out-of-core data-intensive applications
Effiziente MapReduce-Parallelisierung von Entity Resolution-Workflows
Scalable Multi-Agent Simulation based on MapReduce
MapReduce particle filtering with exact resampling and deterministic runtime
MapReduce with Deltas
CLQLMRS: improving cache locality in MapReduce job scheduling using Q-learning
Predictive modelling of MapReduce job performance in cloud environments using machine learning techniques
Pairwise Element Computation with MapReduce
Computing n-Gram Statistics in MapReduce
A gray-box modeling methodology for runtime prediction of Apache Spark jobs
A gray-box modeling methodology for runtime prediction of Apache Spark jobs
Consistency model for runtime objects in the Open Community Runtime
DI-MMAP—a scalable memory-map runtime for out-of-core data-intensive applications
Effiziente MapReduce-Parallelisierung von Entity Resolution-Workflows