The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
A Reinforcement Learning-Based Virtual Machine Placement Strategy in Cloud Data Centers
Abstract
With the widespread use of cloud computing, energy consumption of cloud data centers is increasing which mainly comes from IT equipment and cooling equipment. This paper argues that once the number of virtual machines on the physical machines reaches a certain level, resource competition occurs, resulting in a performance loss of the virtual machines. Unlike most papers, we do not impose placement constraints on virtual machines by giving a CPU cap to achieve the purpose of energy savings in cloud data centers. Instead, we use the measure of performance loss to weigh. We propose a reinforcement learning-based virtual machine placement strategy(RLVMP) for energy savings in cloud data centers. The strategy considers the weight of virtual machine performance loss and energy consumption, which is finally solved with the greedy strategy. Simulation experiments show that our strategy has a certain improvement in energy savings compared with the other algorithms.
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Machine-learning approaches can be used for VM placement, including Reinforcement Learning [130], Artificial Neural Network [131], and Fuzzy reinforcement learning [132].
“For the suitable placement, machine-learning approaches can be used for the placement of virtual machines. Reinforcement Learning [130], Artificial Neural Network [131], and Fuzzy reinforcement learning [132] are some of them.”
From Energy efficiency in cloud computing data centers: a survey on software technologies (2023) · cites this paper as [130]
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Energy efficiency in cloud computing data centers: a survey on software technologies
Katal, A., Dahiya, S., & Choudhury, T. (2023) · Cluster Computing · doi:10.1007/s10586-022-03713-0 · Cited by 516
“Reinforcement Learning [130], Artificial Neural Network [131], and Fuzzy reinforcement learning [132] are some of them.”
Metadata
- Authors
- Long, S., Li, Z., Xing, Y., Tian, S., Li, D., & Yu, R.
- Affiliations
- Xiangtan University (CN)
- National University of Defense Technology (CN)
- Guangdong University of Technology (CN)
- Venue
- 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
- Publisher
- IEEE
- Published
- 2020-12
- Pages
- 223-230
- OpenAlex
- W3159136201
- Cited by
- 14
- References
- 33
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 2 · 2022: 3
- FWCI
- 0.95
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (publishedVersion)
- Funders
- Research and Development
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- virtual machine placement, cloud data centers, energy consumption, reinforcement learning, performance degradation, resource competition, greedy algorithm, energy conservation
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