Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning

Cloud Computing provides an effective platform for executing large-scale and complex workflow applications with a pay-as-you-go model. Nevertheless, various challenges, especially its optimal scheduling for multiple conflicting objectives, are yet to be addressed properly. The existing multi-objecti...

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Vydáno v:	IEEE access Ročník 7; s. 39974 - 39982
Hlavní autoři:	Wang, Yuandou, Liu, Hang, Zheng, Wanbo, Xia, Yunni, Li, Yawen, Chen, Peng, Guo, Kunyin, Xie, Hong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Cloud computing Completion time Computer networks deep-Q-network (DQN) Game theory Games Genetic algorithms Greedy algorithms infrastructure-as-a-service (IaaS) cloud Job shops Learning Markov processes multi-agent reinforcement learning (MARL) Multi-objective workflow scheduling Multiagent systems Multiple objective analysis Optimal scheduling Particle swarm optimization quality-of-service (QoS) Real time Reinforcement learning Scheduling Sorting algorithms Task analysis Virtual environments Workflow software
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
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