Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 6th International Conference on Computing and Data Science
Series Vol. 57 , 17 April 2024
Open
Access | Article
Dynamic resource allocation for virtual machine migration optimization using machine learning
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Abstract
This article delves into the importance of applying machine learning and deep reinforcement learning techniques in cloud resource management and virtual machine migration optimization, highlighting the role of these advanced technologies in dealing with the dynamic changes and complexities of cloud computing environments. Through environment modeling, policy learning, and adaptive enhancement, machine learning methods, especially deep reinforcement learning, provide effective solutions for dynamic resource allocation and virtual intelligence migration. These technologies can help cloud service providers improve resource utilization, reduce energy consumption, and improve service reliability and performance. Effective strategies include simplifying state space and action space, reward shaping, model lightweight and acceleration, and accelerating the learning process through transfer learning and meta-learning techniques. With the continuous progress of machine learning and deep reinforcement learning technologies, combined with the rapid development of cloud computing technology, it is expected that the application of these technologies in cloud resource management and virtual machine migration optimization will be more extensive and in-depth. Researchers will continue to explore more efficient algorithms and models to further improve the accuracy and efficiency of decision making. In addition, with the integration of edge computing, Internet of Things and other technologies, cloud computing resource management will face more new challenges and opportunities, and the application scope and depth of machine learning and deep reinforcement learning technology will also expand, opening new possibilities for building a more intelligent, efficient and reliable cloud computing service system.
Keywords
Cloud computing migration technology, Virtualization, Machine learning-based optimization, Dynamic resource allocation
References
1. Arianyan E, Taheri H, Sharifian S. Novel heuristies for consolidation of virtual machines in cloud datacenters using multi-criteria resource management solutions!J. The Journal of Supercomputing, 2016, 72(2).688-717
2. Zhang X, Zhao Y, Guo S, et al. Performance-Aware Energy-efficient Virtual Machine Placement in CloudData Center[C]. 2017 lEEE International Conference on Communications, 2017: 1-7.
3. Liu, Bo, et al. "Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms." arXiv preprint arXiv:2312.12872 (2023).
4. Ye K, Wu Z, Wang C, et al. Profiling-Based Workload Consolidation and Migration in Virtualized DataCenters[J]. lEEE Transactions on Parallel and Distributed Systems, 2015, 26(3): 878-890.
5. Liu, Bo, et al. "Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms." arXiv preprint arXiv:2312.12872 (2023).
6. Alharbi F, Tain Y C, Tang M, et al. Profile-based static virtual machine placement for energy-efficient datacenter[C]. 2016 lEEE 18th International Conference on High Performance Computing and Communications.2016:1045-1052.
7. Liu, B., Zhao, X., Hu, H., Lin, Q., & Huang, J. (2023). Detection of Esophageal Cancer Lesions Based on CBAM Faster R-CNN. Journal of Theory and Practice of Engineering Science, 3(12), 36–42. https://doi.org/10.53469/jtpes.2023.03(12).06
8. Uddin M, Shah A, Alsagour R, et al. Measuring Efficiency of Tier Level Data Centers to lmplement GreenEnergy Efficient Data Centers[J]. Middle East Journal of Scientific Research, 2013, 15(2): 200-207
9. Hajam Shahid Sultan,and Sofi Shabir Ahmad. "Resource management in fog computing using greedy and semi-greedy spider monkey optimization." Soft Computing 27. 24 (2023):
10. Qilin Zhou Lili Wang,and Shoulin Wu. "Resource management optimisation for federated learning-enabled multi-access edge computing in internet of vehicles." International Journal of Sensor Networks 42. 1 (2023):
11. Chen Yifan, et al. "DRJOA: intelligent resource management optimization through deep reinforcement learning approach in edge computing." Cluster Computing 26. 5 (2022):
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 6th International Conference on Computing and Data Science
- ISBN (Print)
- 978-1-83558-393-7
- ISBN (Online)
- 978-1-83558-394-4
- Published Date
- 17 April 2024
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/57/20241348
- Copyright
- 17 April 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited