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ARED: automata-based runtime estimation for distributed systems using deep learning.

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Title: ARED: automata-based runtime estimation for distributed systems using deep learning.
Authors: Cheon, Hyunjoon1, Ryu, Jinseung2, Ryou, Jaecheol3, Park, Chan Yeol2, Han, Yo-Sub1, emmous@yonsei.ac.kr
Source: Cluster Computing; Oct2023, Vol. 26 Issue 5, p2629-2641, 13p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 170716702
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: ARED: automata-based runtime estimation for distributed systems using deep learning.
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  Data: <searchLink fieldCode="AU" term="%22Cheon%2C+Hyunjoon%22">Cheon, Hyunjoon</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Ryu%2C+Jinseung%22">Ryu, Jinseung</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AU" term="%22Ryou%2C+Jaecheol%22">Ryou, Jaecheol</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AU" term="%22Park%2C+Chan+Yeol%22">Park, Chan Yeol</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AU" term="%22Han%2C+Yo-Sub%22">Han, Yo-Sub</searchLink><relatesTo>1</relatesTo>, <i>emmous@yonsei.ac.kr</i>
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  Data: <searchLink fieldCode="JN" term="%22Cluster+Computing%22">Cluster Computing</searchLink>; Oct2023, Vol. 26 Issue 5, p2629-2641, 13p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=170716702
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s10586-021-03272-w
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 2629
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      – TitleFull: ARED: automata-based runtime estimation for distributed systems using deep learning.
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            NameFull: Cheon, Hyunjoon
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            NameFull: Ryu, Jinseung
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            NameFull: Ryou, Jaecheol
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            NameFull: Park, Chan Yeol
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            – D: 01
              M: 10
              Text: Oct2023
              Type: published
              Y: 2023
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              Value: 26
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