Exploiting Machine Learning for Improving In-Memory Execution of Data-Intensive Workflows on Parallel Machines

Exploiting Machine Learning for Improving In-Memory Execution of Data-Intensive Workflows on Parallel Machines

Workflows are largely used to orchestrate complex sets of operations required to handle and process huge amounts of data. Parallel processing is often vital to reduce execution time when complex data-intensive workflows must be run efficiently, and at the same time, in-memory processing can bring im...

Descripción completa

Detalles Bibliográficos
Autores principales: Riccardo Cantini, Fabrizio Marozzo, Alessio Orsino, Domenico Talia, Paolo Trunfio
Formato: Artículo
Lenguaje:English
Publicado: MDPI AG 2021-05-01
Colección:Future Internet
Materias:
workflow
data-intensive
in-memory
machine learning
Apache Spark
scheduling
Acceso en línea:https://www.mdpi.com/1999-5903/13/5/121

Internet

https://www.mdpi.com/1999-5903/13/5/121

Ejemplares similares