DOI QR코드

DOI QR Code

그리드 컴퓨팅 환경에서 기상업무에 적합한 접근 제어 시스템 구현

Implementation of Access Control System Suitable for Meteorological Tasks in Grid Computing Environment

  • 나승권 (한국폴리텍대학 강릉캠퍼스 전자통신학과) ;
  • 주재한 (송호대학교 보건의료전자과)
  • Na, Seung-kwon (Department of Electronics and Communication, Korea Polytechnic College Gangneung Campus) ;
  • Ju, Jae-han (Department of Medical Electronics, Songho College)
  • 투고 : 2017.03.17
  • 심사 : 2017.04.21
  • 발행 : 2017.04.30

초록

최근 컴퓨팅 기기를 하나의 네트워크로 연결하여, 극대화한 차세대 디지털 신경망서비스를 제공하는 그리드 컴퓨팅은 PC나 서버, PDA 등 모든 컴퓨터를 네트워크로 연결해 하나의 거대한 가상 컴퓨터를 만든다는 것이다. 따라서 기상업무 분야에 적용될 그리드 컴퓨팅 구현 모델을 다음과 같이 제안한다. 첫째, 그리드 컴퓨팅을 이용하게 될 대상 작업은 중규모 이하의 수치 모델 개발 또는 테스트 운영에 필요한 작업들과 기상용 슈퍼컴퓨터의 최종 백업이다. 둘째, 그리드 컴퓨팅을 구성하게 될 자원은 운영 효율을 고려하여 본청에서 운영 중인 업무용 PC와 리눅스 서버들로 한정한다. 셋째, 네트워크는 LAN 구간으로 제한하는 것으로 고성능 컴퓨팅을 구현하는 방안을 제시하였다.

Recently computing devices by connecting to a network, grid computing, the next generation of digital neural networks that provide maximum service will connect all of the computer such as a PC or server, PDA into one giant network makes the virtual machine. Therefore, we propose the grid computing implementation model to be applied to meteorological business field as follows. First, grid computing will be used for tasks such as the development of numerical models below the mid-scale or test operations, and the final backup of the weather supercomputer. Second, the resources that will constitute grid computing are limited to business PCs and Linux servers operated by the central government considering operational efficiency. Third, the network is restricted to the LAN section, which suggests the implementation of high performance computing.

키워드

참고문헌

  1. Q. Zheng, "On the design of mutually aware optimal pricing and load balancing Strategiesfor grid computing systems computers," IEEE Transactions on Computers, Vol. 63, No. 7, pp. 1802-1811, 2013. https://doi.org/10.1109/TC.2013.56
  2. M. Smith, F. Schwarzer, M. Harbach, T. Noll, and B. Freisleben, "A streaming intrusion detection system for grid computing environments," in The 11th IEEE International Conference on High Performance Computing and Communications 2009. HPCC'09. Seoul: Korea, pp. 44-51, 2009.
  3. Y. Zhang, L. Wang, W. Sun, R. C. Green, and M. Alam, "Distributed intrusion detection system in a multi-layer network architecture of smart grids," IEEE Transactions on Smart Grid, Vol. 2, No. 4, pp. 796-808, 2011. https://doi.org/10.1109/TSG.2011.2159818
  4. R. S. Chang, C. Y. Lin, and C. F. Lin, "An adaptive scoring job scheduling algorithm for grid computing," Information Sciences, Vol. 207, pp. 79-89, 2012. https://doi.org/10.1016/j.ins.2012.04.019
  5. P. Vivekananth, "An overview of trust models and proposal of new model based on teputation for resource selection in grid computing," International Journal of Engineering and Technology, Vol. 2, No. 4, pp. 387-389, 2010. https://doi.org/10.7763/IJET.2010.V2.152
  6. C. Li, "Joint application-fabric layer optimization in grid Computing," in 2008 11th IEEE International Conference on Computational Science and Engineering, Sao Paulo: Brazil, pp. 141-146, 2008.
  7. O. Lysne, J. M. Montaana, J. Flich, J. Duato, T. M. Pinkston, and T. Skeie, "An efficient and deadlock-free network reconfiguration protocol," IEEE Transactions on Computers, Vol. 57, No. 6, pp. 762-779, 2008. https://doi.org/10.1109/TC.2008.31
  8. I. Gorton, Z. Huang, Y. Chen, B. Kalahar, S. Jin, D. Baxter, and J. Feo, "A high-performance hybrid computing approach to massive contingency analysis in the power grid," in The 5th IEEE International Conference on e-Science, Oxford: United Kingdom, pp. 277-283, 2009.