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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.7
- 수록면
- 29 - 37 (9page)
- DOI
- https://doi.org/10.15207/JKCS.2017.8.7.029
이용수
초록· 키워드
오류제보하기
Compared to the commercial networks, R&E networks have the strength such as flexible network engineering and design. Based on those features of R&E networks, we propose our storage cloud service model which supports general-purpose network users in a central region and experimental network users in distributed regions simultaneously. We prototype our service model utilizing multiple proxy controllers of OpenStack Swift service in order to deploy several regions via experimental backbone networks. Our experiments on the influence of the network latency and the size of data to be transmitted show that the bigger size of data is preferable to the smaller size of data in an experimental backbone network where the network latency increases within 10ms because the rate of throughput decline in the bigger object is comparatively small. It means that our service model is appropriate for experimental network users who directly access the service in order to move intermittently high volume of data as well as normal users in the central region who access the service frequently.
목차
등록된 정보가 없습니다.
참고문헌 (20)
참고문헌 신청
J. Balewski / 2012 / Offloading Peak Processing to Virtual Farm by STAR Experiment at RHIC / Journal of Physics: Conference Series 368(2012):012011
R. P Taylor / 2015 / The Evolution of Cloud Computing in ATLAS / Journal of Physics:Conference Series 664(2015):022038
M. Parashar / 2013 / Cloud Paradigms and Practices for Computational and Data-Enabled Science and Engineering / Computing in Science & Eng 15(4):10~18
K. Keahey / 2014 / Enabling On-Demand Science via Cloud Computing / IEEE Cloud Computing :21~27
D. Yuan / 2014 / Cloud Data Management for Scientific Workflows: Research Issues, Methodologies, and State-of-the-Art / IEEE International Conference on Semantics, Knowledge and Grids
R. P Taylor / 2015 / The Evolution of Cloud Computing in ATLAS / Journal of Physics:Conference Series 664(2015):022038
M. Parashar / 2013 / Cloud Paradigms and Practices for Computational and Data-Enabled Science and Engineering / Computing in Science & Eng 15(4):10~18
K. Keahey / 2014 / Enabling On-Demand Science via Cloud Computing / IEEE Cloud Computing :21~27
D. Yuan / 2014 / Cloud Data Management for Scientific Workflows: Research Issues, Methodologies, and State-of-the-Art / IEEE International Conference on Semantics, Knowledge and Grids
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