KIISE Transactions on Computing Practices (정보과학회 컴퓨팅의 실제 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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A Resource Clustering Method Considering Weight of Application Characteristic in Hybrid Cloud Environment

하이브리드 클라우드 환경에서의 응용 특성 가중치를 고려한 자원 군집화 기법

  • 오유리 (숙명여자대학교 컴퓨터과학과) ;
  • 김윤희 (숙명여자대학교 컴퓨터과학과)
  • Received : 2017.02.17
  • Accepted : 2017.05.25
  • Published : 2017.08.15
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Abstract

There are many scientists who want to perform experiments in a cloud environment, and pay-per-use services allow scientists to pay only for cloud services that they need. However, it is difficult for scientists to select a suitable set of resources since those resources are comprised of various characteristics. Therefore, classification is needed to support the effective utilization of cloud resources. Thus, a dynamic resource clustering method is needed to reflect the characteristics of the application that scientists want to execute. This paper proposes a resource clustering analysis method that takes into account the characteristics of an application in a hybrid cloud environment. The resource clustering analysis applies a Self-Organizing Map and K-means algorithm to dynamically cluster similar resources. The results of the experiment indicate that the proposed method can classify a similar resource cluster by reflecting the application characteristics.

클라우드의 원하는 자원을 필요한 만큼만 사용하고 지불하는(Pay-per-use) 방식을 이용하여 과학 응용을 수행하고자 하는 과학자들이 늘어나는 추세이다. 그러나 다양한 특성으로 구성된 클라우드 자원으로 과학자들은 적절한 자원을 선택하는데 어려움을 겪는다. 이에 따라 자원의 효율적인 활용을 위하여 과학자가 실험하고자하는 응용의 특성에 따라 동적으로 자원을 분류하는 것이 필요하다. 본 연구에서는 하이브리드 클라우드 환경에서 응용의 특성을 반영한 자원 군집 분석 기법을 제안한다. 자원 군집 분석은 자기조직화지도 및 K-평균 알고리즘을 적용하여 유사한 자원을 군집화한다. 제안한 알고리즘을 통해 과학응용의 특성을 반영한 유사 자원 군집을 형성하였음을 증명한다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. Amazon EC2, [Online]. Available: http://aws.amazon.com/
  2. Microsoft Window Azure, [Online]. Available: http://www.windowsazure. com/
  3. KT Ucloud, [Online]. Available: http://ucloudbiz.olleh.com/
  4. Google Cloud Engine, [Online]. Available: https://cloud.google.com/
  5. NHN Entertainment, [Online]. Available: http://cloud.toast.com/
  6. Chavan, Vinay, and Parag Ravikant Kaveri, "Clustered virtual machines for higher availability of resources with improved scalability in cloud computing," Networks & Soft Computing (ICNSC), 2014 First International Conference on. IEEE, 2014.
  7. Wu, Qingtao, et al., "A cloud service resource classification strategy based on feature similarity," Journal of Networks 9.11, 2987-2993, 2014.
  8. Ahn, Younsun, and Yoonhee Kim, "Semantic Cloud Resource Recommendation Using Cluster Analysis in Hybrid Cloud Computing Environment," KIPS Transactions on Computer and Communication Systems, Vol. 4, No. 9, 283-288, 2015. https://doi.org/10.3745/KTCCS.2015.4.9.283
  9. Self-Organizing Map, Kohonen, Teuvo, and Timo Honkela, "Kohonen network," Scholarpedia 2.1 1568, 2007. https://doi.org/10.4249/scholarpedia.1568
  10. Elbow method, [Online]. Available: https://en.wikipedia.org/wiki/Elbow_method_(clustering)
  11. K-means clustering, MacQueen, James, "Some methods for classification and analysis of multivariate observations," Proc. of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1, No. 14, 1967.
  12. OpenStack, [Online]. Available: https://www.openstack.org/.
  13. CFD(Computational fluid dynamics), Seoul National University, Aerodynamic Simulation & Design Lab.
  14. Montage GALFA, [Online]. Available: http://montage.ipac.caltech.edu/docs/cubemosaicstutorial.html