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...

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Bibliographic Details
Main Authors: Riccardo Cantini, Fabrizio Marozzo, Alessio Orsino, Domenico Talia, Paolo Trunfio
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Future Internet
Subjects:
workflow
data-intensive
in-memory
machine learning
Apache Spark
scheduling
Online Access:https://www.mdpi.com/1999-5903/13/5/121

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

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