Conference paper Open Access

Learning Workflow Scheduling on Multi-Resource Clusters

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


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        <foaf:name>de Laat, Cees</foaf:name>
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        <foaf:name>Zhao, Zhiming</foaf:name>
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    <dct:title>Learning Workflow Scheduling on Multi-Resource Clusters</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued>
    <dcat:keyword>Workflow Scheduling</dcat:keyword>
    <dcat:keyword>Multi-resource Clusters</dcat:keyword>
    <dcat:keyword>Directed-Acyclic Graph (DAG)</dcat:keyword>
    <dcat:keyword>Deep Reinforcement Learning</dcat:keyword>
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    <dct:description>&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;</dct:description>
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