Enhancing virtual machine placement efficiency in cloud data centers: a hybrid approach using multi-objective reinforcement learning and clustering strategies-Reference-Cited by-同舟云学术

Enhancing virtual machine placement efficiency in cloud data centers: a hybrid approach using multi-objective reinforcement learning and clustering strategies

Published:2024-07-02 Issue:9 Volume:106 Page:2897-2922
ISSN:0010-485X
Container-title:Computing
language:en
Short-container-title:Computing

Author:

Ghasemi Arezoo,Toroghi Haghighat Abolfazl,Keshavarzi Amin

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00607-024-01311-z.pdf

Reference32 articles.

1. Aghasi A, Jamshidi K, Bohlooli A (2022) A thermal-aware energy-efficient virtual machine placement algorithm based on fuzzy controlled binary gravitational search algorithm (fc-bgsa). Cluster Comput 1–19

2. Alharbi F, Tian YC, Tang M, Ferdaus MH, Zhang WZ, Yu ZG (2021) Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enterprise data centers. Cluster Comput 24:1255–1275

3. Azizi S, Zandsalimi M, Li D (2020) An energy-efficient algorithm for virtual machine placement optimization in cloud data centers. Cluster Comput 23:3421–3434

4. Feng H, Deng Y, Li J (2021) A global-energy-aware virtual machine placement strategy for cloud data centers. J Syst Architect 116:102048

5. Ghasemi A, Toroghi Haghighat A (2020) A multi-objective load balancing algorithm for virtual machine placement in cloud data centers based on machine learning. Computing 102:2049–2072

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MOHHO: multi-objective Harris hawks optimization algorithm for service placement in fog computing;The Journal of Supercomputing;2024-08-06