DQN training parame.

<div><p>With the increasing popularity of cloud computing services, their large and dynamic load characteristics have rendered task scheduling an NP-complete problem.To address the problem of large-scale task scheduling in a cloud computing environment, this paper proposes a novel cloud...

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Bibliographic Details
Main Author:	Delong Cui (21730915) (author)
Other Authors:	Zhiping Peng (5783078) (author), Kaibin Li (1571551) (author), Qirui Li (6578297) (author), Jieguang He (17100746) (author), Xiangwu Deng (6612770) (author)
Published:	2025
Subjects:	Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified service level agreement scale task scheduling rendered task scheduling one potential shortcoming employs hierarchical scheduling could impact real classical heuristic algorithms cloud task scheduling experimental results demonstrate skillfully balances cost cost nodes within cloud computing services cloud computing environment updating network parameters performance heavily depends framework &# 8217 allocate tasks first dynamic load characteristics designed using drl cloud computing cloud environments experiments demonstrate dynamic changes load situations load balancing load balance xlink "> virtual machines using low training data still shortcomings resource utilization paper proposes overdue time overall improvement optimizing objectives method used machine learning increasing popularity framework defines continuously learning continuous learning computational overhead
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DQN training parame.

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