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

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Related Materials

1.
DOI - Is version of https://doi.org/10.1002/spe.2802

Location

Chichester, Eng.

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

Notes

Early View Article

Publication classification

C1 Refereed article in a scholarly journal

Journal

Software - Practice and Experience

Volume

52

Pagination

689-709

ISSN

0038-0644

eISSN

1097-024X

Issue

3

Publisher

WILEY

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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 4606 Distributed computing and systems software 4611 Machine learning

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All Rights Reserved

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