Conference paper Open Access

Learning Workflow Scheduling on Multi-Resource Clusters

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

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Hu, Yang</dc:creator>
  <dc:creator>de Laat, Cees</dc:creator>
  <dc:creator>Zhao, Zhiming</dc:creator>
  <dc:description>Workflow scheduling is one of the key issues in

the management of workflow execution. Typically, a workflow

application can be modeled as a Directed-Acyclic Graph (DAG).

In this paper, we present GoDAG, an approach that can learn

to well schedule workflows on multi-resource clusters. GoDAG

directly learns the scheduling policy from experience through

deep reinforcement learning. In order to adapt deep reinforcement

learning methods, we propose a novel state representation,

a practical action space and a corresponding reward definition

for workflow scheduling problem. We implement a GoDAG

prototype and a simulator to simulate task running on multiresource

clusters. In the evaluation, we compare the GoDAG with

three state-of-the-art heuristics. The results show that GoDAG

outperforms the baseline heuristics, leading to less average

makespan to different workflow structures.</dc:description>
  <dc:subject>Workflow Scheduling</dc:subject>
  <dc:subject>Multi-resource Clusters</dc:subject>
  <dc:subject>Directed-Acyclic Graph (DAG)</dc:subject>
  <dc:subject>Deep Reinforcement Learning</dc:subject>
  <dc:title>Learning Workflow Scheduling on Multi-Resource Clusters</dc:title>
Views 46
Downloads 43
Data volume 16.8 MB
Unique views 43
Unique downloads 40


Cite as