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A study of Modeling and Simulation for the Availability Optimization of Cloud Computing Service

클라우드 컴퓨팅 서비스의 가용성 최적화를 위한 모델링 및 시뮬레이션

  • 장은영 (서울여자대학교 컴퓨터학과) ;
  • 박춘식 (서울여자대학교 정보보호학과 클라우드컴퓨팅연구센터)
  • Received : 2010.04.05
  • Accepted : 2011.01.21
  • Published : 2011.03.31

Abstract

Cloud computing emerges as a new paradigm for deploying, managing and offering IT resources as a service anytime, anywhere on any devices. Cloud computing data center stores many IT resources through resource integration. So cloud computing system has to be designed by technology and policy to make effective use of IT resources. In other words, cloud vendor has to provide high quality services to all user and mitigate the dissipation of IT resources. However, vendors need to predict the performance of cloud services and the use of IT resources before releasing cloud service. For solving the problem, this research presents cloud service modeling on network environment and evaluation index for availability optimization of cloud service. We also study how to optimize an amount of requested cloud service and performance of datacenter using CloudSim toolkit.

클라우드 컴퓨팅은 장소나 장비에 제한 없이 네트워크를 통해 IT자원을 서비스 형태로 제공받을 수 있는 새로운 패러다임이다. 클라우드 컴퓨팅 환경은 데이터센터에 많은 IT자원이 집약된 형태로 효율적인 인프라구조를 위한 기술과 정책을 적용하여 시스템을 설계해야 한다. 즉, 클라우드 서비스를 효율적으로 제공하여 사용자의 요구를 만족시켜야 하며, 사업자는 불필요하게 낭비되는 자원으로 인한 손해가 없어야 한다. 그러나 최적 시스템을 구축하기 위해서는 서비스를 배포하기 전에 서비스 제공 성능과 자원 사용의 효율성을 예측할 수 있어야 한다. 본 논문에서는 이러한 클라우드 컴퓨팅 시스템 설계과정의 문제를 해결하기 위해 네트워크 환경에서의 클라우드 서비스 모델을 모델링하고 클라우드 서비스의 가용성 최적화를 위해 가용성 평가 지표를 산출하였다. 또한 클라우드 환경이 적용된 CloudSim 시뮬레이터를 이용해 클라우드 컴퓨팅 서비스 요구와 데이터센터 성능에 대한 가용성을 최적화하는 방법을 모색하였다.

Keywords

References

  1. Cloud Security Alliance, "Security Guidance for Critical Areas of Focus in Cloud Computing," 2009.
  2. B. Hayes, "Cloud computing," Communications of the ACM, vol. 51, Issue 7, pp. 9-11, 2008. https://doi.org/10.1145/1364782.1364786
  3. P. Mell and T. Grance, "The NIST Definition of Cloud Computing," National Institute of Standards and Technology, Information Technology Laboratory, Version 15, 2009.
  4. R.N.Calheiros, R.Ranjan, De Rose, and R.Buyya, "CloudSim: A novel framework for modeling and simulation of cloud computing infrastructures and services," Technical Report, Grid Computing and Distributed Systems (GRIDS) Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, 2009.
  5. R. Buyya, R.Ranjan, and R.N, Calheiros, "Modeling and simulation of scalable cloud computing environments and the cloudsim toolkit: challenges and opportunities," Proceedings of the 7th High Performance Computing and Simulation Conference, Leipzig, Germany, 2009.
  6. I. Adan, and J.Resing, "Queueing Theory," Department of Mathematics and Computing Science Eindhove University of Technology, P.O.Box 513, 5600 MB Eindhoven, 2002.
  7. B.P.Zeigler, H.Praehofer, and T.G. Kim, "Theory of Modeling and Simulation," 2th Ed., Academic Press, Jan 2000.
  8. M. Li, "An approach to reliably identifying signs of DDOS flood attacks based on LRD traffic pattern recognition," Computer & Security, vol. 23, pp. 549-558, 2004. https://doi.org/10.1016/j.cose.2004.04.005
  9. G.Macia-Fernandez, J.E.Diaz-Verdejo, and P. Garcia- Teodoro, "Evaluation of a low-rate DoS attack against application server," COMPUTER & SECURITY, vol. 27, pp. 1013-1030, 2008.
  10. T.Tidwell, "Modeling internet attacks," Proceedings of the 2001 IEEE Workshop on Information Assurance and Security United States Military Academy, pp. 54-59, 2001.
  11. J.Mirkovic, J.Martin, and T. Reiher, "A taxonomy of DDoS attacks and DDoS defense mechanisms," ACM SIGCOMM Computer Communication Review, vol. 34, Issue 2, pp. 39-53, 2004. https://doi.org/10.1145/997150.997156
  12. F. Cohen, "Simulating cyber attacks, defences, and consequences," Computer & Security, vol. 18, pp. 479-518, 1999. https://doi.org/10.1016/S0167-4048(99)80115-1