Using Machine Learning Ensemble Methods to Predict Execution Time of e-Science Workflows in Heterogeneous Distributed Systems

Using Machine Learning Ensemble Methods to Predict Execution Time of e-Science Workflows in Heterogeneous Distributed Systems

Effective planning and optimized execution of the e-Science workflows in distributed systems, such as the Grid, need predictions of execution times of the workflows. However, predicting the execution times of e-Science workflows in heterogeneous distributed systems is a challenging job due to the co...

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Bibliographic Details
Main Authors: Farrukh Nadeem, Daniyal Alghazzawi, Abdulfattah Mashat, Khalid Faqeeh, Abdullah Almalaise
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Machine learning ensemble systems
performance modeling of e-science workflows
distributed execution of e-science workflows
execution time prediction of e-science workflows
Online Access:https://ieeexplore.ieee.org/document/8643927/

Internet

https://ieeexplore.ieee.org/document/8643927/

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