About: Data Centers Job Scheduling with Deep Reinforcement Learning

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Data Centers Job Scheduling with Deep Reinforcement Learning
Creator	Chen, Yong Yang, Zhou Liang, Sisheng
Source	PMC
abstract	Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space. To adapt the complex computing environment in data centers, we proposed an innovative Advantage Actor-Critic (A2C) deep reinforcement learning based approach called A2cScheduler for job scheduling. A2cScheduler consists of two agents, one of which, dubbed the actor, is responsible for learning the scheduling policy automatically and the other one, the critic, reduces the estimation error. Unlike previous policy gradient approaches, A2cScheduler is designed to reduce the gradient estimation variance and to update parameters efficiently. We show that the A2cScheduler can achieve competitive scheduling performance using both simulated workloads and real data collected from an academic data center.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47436-2_68
has license	no-cc
sha1sum (hex)	f1696cf0eb451db24587e096eebab2f4ac4d710e
schema:url	https://doi.org/10.1007/978-3-030-47436-2_68
resource representing a document's title	Data Centers Job Scheduling with Deep Reinforcement Learning
has PubMed Central identifier	PMC7206316
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:f1696cf0eb451db24587e096eebab2f4ac4d710e#body_text
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