A multi-objective trade-off framework for cloud resource scheduling based on the Deep Q-network algorithm
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Link
http://link.springer.com/content/pdf/10.1007/s10586-019-03042-9.pdf
Reference41 articles.
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2. Ramezani, F., Lu, J., Hussain, F.: Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization. In: Service-Oriented Computing. ICSOC 2013. Lecture Notes in Computer Science, vol. 8274, pp. 237–251. Springer (2013)
3. Liu, J., Luo, X.G., Zhang, X.M., et al.: Job scheduling algorithm for cloud computing based on particle swarm optimization. Adv. Mater. Res. 662, 957–960 (2013)
4. Alkayal, E.S., Jennings, N.R., Abulkhair, M.F.: Efficient task scheduling multi-objective particle swarm optimization in cloud computing. In: 2016 IEEE 41st Conference on Local Computer Networks Workshops (LCN Workshops), pp. 17–24 (2016)
5. Verma, A., Kaushal, S.: A hybrid multi-objective particle swarm optimization for scientific workflow scheduling. Parallel Comput. 62, 1–19 (2017)
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