논문 인용하기
각 논문마다 생성되어 있는 BibTeX를 사용하시면 자신이 원하는 스타일의 인용 문구를 생성할 수 있습니다.
생성된 BibTeX 코드를 복사하여 BibTeX Parser를 사용해 일반 문자열로 바꾸십시오. 아래의 사이트와 같이 웹에서 변환할 수도 있습니다.
bibtex.online2021
1.
Lee, Seungjun; Yoon, Daegun; Oh, Sangyoon
Imitation learning for VM placement problem using demonstration data generated by heuristics🌏 InternationalConference 📃 In press
17th Int. Conference on Data Science (ICDATA’21), 2021.
Abstract | Links | BibTeX | 태그: consolidation, imitation learning, reinforcement learning
@conference{lee2021imitation,
title = {Imitation learning for VM placement problem using demonstration data generated by heuristics},
author = {Seungjun Lee and Daegun Yoon and Sangyoon Oh},
url = {https://youtu.be/CmG3E1rWroQ},
year = {2021},
date = {2021-07-26},
urldate = {2021-07-26},
booktitle = {17th Int. Conference on Data Science (ICDATA’21)},
abstract = {Data centers are key components of cloud computing to run virtual machines. For saving the cost to operate data centers, it is important to decide how to allocate each virtual machine to a certain physical machine. Because the virtual machine placement problem is NP-Hard, there are many heuristics to obtain near-optimal solutions as quickly as possible. The reinforcement learning technique can be applied for virtual machine placement problem. However, if the problem size gets bigger, the convergence speed of reinforcement learning gets slower. The possible solution is that the agent imitates the behavior of given demonstration, called imitation learning. In this paper, we propose a method combining reinforcement learning with imitation learning. In our proposed approach, demonstration data is generated by simple heuristics not human experts.},
keywords = {consolidation, imitation learning, reinforcement learning},
pubstate = {published},
tppubtype = {conference}
}
Data centers are key components of cloud computing to run virtual machines. For saving the cost to operate data centers, it is important to decide how to allocate each virtual machine to a certain physical machine. Because the virtual machine placement problem is NP-Hard, there are many heuristics to obtain near-optimal solutions as quickly as possible. The reinforcement learning technique can be applied for virtual machine placement problem. However, if the problem size gets bigger, the convergence speed of reinforcement learning gets slower. The possible solution is that the agent imitates the behavior of given demonstration, called imitation learning. In this paper, we propose a method combining reinforcement learning with imitation learning. In our proposed approach, demonstration data is generated by simple heuristics not human experts.