참고문헌 (25)

1. H. C. Yang, “Contents Industry Trend of USA,” Korea Creative Content Agency, Vol. 21, November 2019. 

2. 염경훈, 김기형, 김강석. AWS 클라우드 기반 장애극복 부하분산 메커니즘 및 가용성 평가. 정보과학회. 컴퓨팅의 실제 논문지 = KIISE transactions on computing practices, vol.24, no.5, 211-219.

   상세보기

3. Yang, Chao-Tung, Chen, Shuo-Tsung, Chan, Yu-Wei, Shen, Yu-Chuan. On construction of a cloud storage system with heterogeneous software-defined storage technologies. Human-centric computing and information sciences, vol.9, no.1, 12-.

   상세보기

4. Liu, Wenqiang, Wang, Pengwei, Meng, Ying, Zhao, Caihui, Zhang, Zhaohui. Cloud spot instance price prediction using kNN regression. Human-centric computing and information sciences, vol.10, no.1, 34-.

   상세보기

5. M. S. Park, Y. S. Lee, S. B. Oh, M. K. Jo, H. R. Jang, Y. I. Yoon and T. W. Um, “A Study on Cloud Resource Allocation for Efficient Online Video Service Based on Reinforcement Learning,” Proceedings of The 12th International Conference on Computer Science and its Applications(CSA 2020), pp. 14-16, December 2020. 

6. 10.1109/HPCC-SmartCity-DSS.2016.0162 

   

7. 10.1109/SmartCity.2015.209 

   

8. 강동기, 김성환, 윤찬현. 가격 효율적인 클라우드 가상 자원 중개 기법에 대한 연구. 정보처리학회논문지. KIPS transactions on computer and communication systems 컴퓨터 및 통신 시스템, vol.3, no.7, 219-230.

   원문보기 상세보기

9. Zhang, Yu, Yao, Jianguo, Guan, Haibing. Intelligent Cloud Resource Management with Deep Reinforcement Learning. IEEE cloud computing, vol.4, no.6, 60-69.

   상세보기

10. 10.1109/FiCloud.2018.00020 

    

11. K. W. Au, “Optimizing Auto-scaling Virtual Machines for a Cloud-based VOD Data Center,” Master Degree Dissertation, Graduate School, Hong Kong University, 2016. 

12. Nguyen Cong Luong, Ping Wang, Niyato, Dusit, Yonggang Wen, Zhu Han. Resource Management in Cloud Networking Using Economic Analysis and Pricing Models: A Survey. IEEE Communications surveys and tutorials, vol.19, no.2, 954-1001.

    상세보기

13. 최영호, 임유진, 박재성. 클라우드 컴퓨팅 환경에서 강화학습기반 자원할당 기법. 한국통신학회논문지 = The Journal of Korean Institute of Communications and Information Sciences, vol.40, no.4, 653-658.

    원문보기 상세보기

14. de Assuncao, M.D., Cardonha, C.H., Netto, M.A.S., Cunha, R.L.F.. Impact of user patience on auto-scaling resource capacity for cloud services. Future generation computer systems : FGCS, vol.55, 41-50.

    상세보기

15. T. Ruether, “2019 Video Streaming Latency Report,” Wowza Media Systems. Available: https://www.wowza.com/wp-content/uploads/Streaming-Video-Latency-Report-Interactive-2020.pdf, January 2021. 

16. 10.1109/CSCWD.2018.8465244 

    

17. S. C. Woo and Y. S. Sung, “Dynamic Action Space Handling Method for Reinforcement Learning Models,” Journal of Information Processing Systems, Vol. 16, No. 5, pp. 1223-1230, October 2020. 

18. J. S. Park and J. H. Park, “Enhanced Machine Learning Algorithms: Deep Learning, Reinforcement Learning, and Q-Learning,” Journal of Information Processing Systems, Vol. 16, No. 5, pp. 1001-1007, October 2020. 

19. 10.11591/ijece.v6i5.10639 B. Asgari, M. G. Arani and S. Jabbehdari, “An Effiecient Approach for Resource Auto-Scaling in Cloud Environments,” International Journal of Electrical and Computer Engineering, Vol. 6, No. 5, pp. 2415-2424, October 2016. 

    

20. S. H. Cho, “Rate adaptation with Q-learning in CSMA/CA wireless networks,” Journal of Information Processing Systems, Vol. 16, No. 5, pp. 1048-1063, October 2020. 

21. M. K. Lee, “Reinforcement Learning based Service Deployment for Optimal Resource Allocation in 5G Network Slicing,” Master Degree Dissertation, Graduate School, Kyunghee University, 2019. 

22. K. H. Kim, I. Y. Jung, H. M. Kim, Y. J. Kim and C. S. Jeong, “SLA-based Auto-Scaling Engine for Cloud based Collaboration Platform,” 2014 Korea Electronic Engineering Association Summer General Academic Conference, Vol. 37, No. 1, pp. 1518-1521, June 2014. 

23. Kim, Youngwoo, No, Jaechum. Resource Allocation based on Virtual Machine Resource Requirement Estimation in Cloud Computing Environment. Journal of the Institute of Electronics and Information Engineers = 전자공학회논문지, vol.54, no.11, 113-121.

    상세보기

24. 10.7583/JKGS.2016.16.2.61 M. J. Choe, H. R. Yong and H. S. Hwang, “A Study of Analyzing Live Streaming OTT Service Data : Focused on Youtube Game Broadcasting,” Journal of Korea Game Society, Vol. 16, No. 2, pp. 61-73, April 2016. 

    원문보기 상세보기

25. Quantylab, “RLtrader open source,” Available: https://github.com/quantylab