Deep and reinforcement learning for automated task scheduling in large‐scale cloud computing systems
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Title
Deep and reinforcement learning for automated task scheduling in large‐scale cloud computing systems
Authors
Keywords
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Journal
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
Volume -, Issue -, Pages -
Publisher
Wiley
Online
2020-07-27
DOI
10.1002/cpe.5919
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- Multi-objective Task Scheduling to Minimize Energy Consumption and Makespan of Cloud Computing Using NSGA-II
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- Host load prediction with long short-term memory in cloud computing
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- (2015) Volodymyr Mnih et al. NATURE
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