| Title | A Reinforcement Learning-Based Virtual Machine Placement Strategy in Cloud Data Centers |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Long, Saiqin, Li, Zhetao, Xing, Yun, Tian, Shujuan, Li, Dongsheng, Yu, Rong |
| Date Published | Dec. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-7649-9 |
| Keywords | cloud computing, cloud data centers, composability, cryptography, Cyber physical system, data centers, energy consumption, Energy Savings., High performance computing, NP-hard problem, pubcrawl, reinforcement learning, resilience, Resiliency, security, virtual machine, virtual machine placement, virtual machine security, Virtual machining, Weight measurement |
| 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. |
| URL | https://ieeexplore.ieee.org/document/9407943 |
| DOI | 10.1109/HPCC-SmartCity-DSS50907.2020.00028 |
| Citation Key | long_reinforcement_2020 |
A Reinforcement Learning-Based Virtual Machine Placement Strategy in Cloud Data Centers