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Deep-Q learning-based heterogeneous earliest finish time scheduling algorithm for scientific workflows in cloud

journal contribution

posted on 2020-01-01, 00:00 authored by A Kaur, P Singh, R Singh Batth, C Peng Lim

Deep-Q learning-based heterogeneous earliest finish time scheduling algorithm for scientific workflows in cloud

History

Journal

Software - Practice and Experience

Volume

52

Issue

3

Pagination

689 - 709

Publisher

WILEY

Publisher DOI

https://doi.org/10.1002/spe.2802

ISSN

0038-0644

eISSN

1097-024X

Language

English

Author URL

http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000513228500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a045e4b2bb1f2b747c68c720ec8913b7

Publication classification

C1 Refereed article in a scholarly journal

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Keywords

Science & Technology Technology Computer Science, Software Engineering Computer Science cloud computing deep-Q learning HEFT multiobjective optimization workflow scheduling ENERGY SYSTEMS

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All Rights Reserved

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