Conference paper Open Access

Learning Workflow Scheduling on Multi-Resource Clusters

Hu, Yang; de Laat, Cees; Zhao, Zhiming


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  <identifier identifierType="URL">https://zenodo.org/record/3466676</identifier>
  <creators>
    <creator>
      <creatorName>Hu, Yang</creatorName>
      <givenName>Yang</givenName>
      <familyName>Hu</familyName>
      <affiliation>University of Amsterdam</affiliation>
    </creator>
    <creator>
      <creatorName>de Laat, Cees</creatorName>
      <givenName>Cees</givenName>
      <familyName>de Laat</familyName>
      <affiliation>University of Amsterdam</affiliation>
    </creator>
    <creator>
      <creatorName>Zhao, Zhiming</creatorName>
      <givenName>Zhiming</givenName>
      <familyName>Zhao</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-6717-9418</nameIdentifier>
      <affiliation>University of Amsterdam</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Learning Workflow Scheduling on Multi-Resource Clusters</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Workflow Scheduling</subject>
    <subject>Multi-resource Clusters</subject>
    <subject>Directed-Acyclic Graph (DAG)</subject>
    <subject>Deep Reinforcement Learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-08-16</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3466676</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/NAS.2019.8834720</relatedIdentifier>
  </relatedIdentifiers>
  <version>Camera ready</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Workflow scheduling is one of the key issues in&lt;/p&gt;

&lt;p&gt;the management of workflow execution. Typically, a workflow&lt;/p&gt;

&lt;p&gt;application can be modeled as a Directed-Acyclic Graph (DAG).&lt;/p&gt;

&lt;p&gt;In this paper, we present GoDAG, an approach that can learn&lt;/p&gt;

&lt;p&gt;to well schedule workflows on multi-resource clusters. GoDAG&lt;/p&gt;

&lt;p&gt;directly learns the scheduling policy from experience through&lt;/p&gt;

&lt;p&gt;deep reinforcement learning. In order to adapt deep reinforcement&lt;/p&gt;

&lt;p&gt;learning methods, we propose a novel state representation,&lt;/p&gt;

&lt;p&gt;a practical action space and a corresponding reward definition&lt;/p&gt;

&lt;p&gt;for workflow scheduling problem. We implement a GoDAG&lt;/p&gt;

&lt;p&gt;prototype and a simulator to simulate task running on multiresource&lt;/p&gt;

&lt;p&gt;clusters. In the evaluation, we compare the GoDAG with&lt;/p&gt;

&lt;p&gt;three state-of-the-art heuristics. The results show that GoDAG&lt;/p&gt;

&lt;p&gt;outperforms the baseline heuristics, leading to less average&lt;/p&gt;

&lt;p&gt;makespan to different workflow structures.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
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      <funderName>European Commission</funderName>
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