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Smart city article details
| Title | A Reinforcement Learning-Based Virtual Machine Placement Strategy In Cloud Data Centers |
|---|---|
| ID_Doc | 4043 |
| Authors | Long S.; Li Z.; Xing Y.; Tian S.; Li D.; Yu R. |
| Year | 2020 |
| Published | Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 |
| DOI | http://dx.doi.org/10.1109/HPCC-SmartCity-DSS50907.2020.00028 |
| 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. © 2020 IEEE. |
| Author Keywords | Cloud Data Centers; Energy Savings.; Reinforcement Learning; Virtual Machine Placement |
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