DOI QR코드

DOI QR Code

Resource Clustering Simulator for Desktop Virtualization Based on Intra Cloud

인트라 클라우드 기반 데스크탑 가상화를 위한 리소스 클러스터링 시뮬레이터

  • 김현우 (동국대학교 멀티미디어공학과)
  • Received : 2018.09.27
  • Accepted : 2018.10.10
  • Published : 2019.01.31

Abstract

With the gradual advancement of IT, passive work processes are automated and the overall quality of life has greatly improved. This is made possible by the formation of an organic topology between a wide variety of real-life smart devices. To serve these diverse smart devices, businesses or users are using the cloud. The services in the cloud are divided into Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). SaaS runs on PaaS, and PaaS runs on IaaS. Since IaaS is the basis of all services, an algorithm is required to operate virtualization resources efficiently. Among them, desktop resource virtualization is used for resource high availability of unused state time of existing desktop PC. Clustering of hierarchical structures is important for high availability of these resources. In addition, it is very important to select a suitable algorithm because many clustering algorithms are mainly used depending on the distribution ratio and environment of the desktop PC. If various attempts are made to find an algorithm suitable for desktop resource virtualization in an operating environment, a great deal of power, time, and manpower will be incurred. Therefore, this paper proposes a resource clustering simulator for cluster selection of desktop virtualization. This provides a clustering simulation to properly select clustering algorithms and apply elements in different environments of desktop PCs.

IT의 점진적 진보에 따라 수동적인 작업 처리가 자동화되고 이를 통해 전반적인 삶의 질이 대폭 발전되었다. 이는 실생활에 접목된 다양하고 많은 스마트 디바이스간 유기적인 토폴로지가 형성됨으로써 가능하다. 이러한 다양한 스마트 디바이스에 서비스를 제공하기 위해서 기업 또는 사용자들은 클라우드를 이용하고 있다. 클라우드에서의 서비스는 크게 Infrastructure as a Service(IaaS), Platform as a Service(PaaS), Software as a Service(SaaS)로 나뉜다. SaaS는 PaaS 위에서 동작되고, PaaS는 IaaS 위에서 동작한다. 이와 같이 IaaS는 모든 서비스의 기반이기 때문에 가상화하는 자원을 효율적으로 운용하기 위한 알고리즘이 요구된다. 이 중에 데스크탑 자원 가상화는 기존 데스크탑 PC의 비가용 상태 시간의 자원 고가용성을 위해 사용된다. 이러한 자원의 고가용성을 위해서는 계층적 구조에 대한 클러스터링이 중요시된다. 또한 많은 클러스터링 알고리즘 중에서 데스크탑 PC의 분포율 및 환경에 따라 주로 사용되는 자원 비중이 다르기 때문에 적합한 알고리즘을 선정하는 것이 매우 중요하다. 만일 동작 환경의 데스크탑 자원 가상화에 적합한 알고리즘을 찾기 위해 다양한 시도를 한다면 이에 대한 전력적, 시간적, 인력에 대한 막대한 비용이 초래된다. 따라서 본 논문에서는 데스크탑 가상화의 클러스터 선정을 위한 리소스 클러스터링 시뮬레이터인 RCS를 제안한다. RCS에 클러스터 수, 호스트 수를 증가하여 동작하는 과정의 시각화 및 수행 시간을 비교 분석한다. 이를 통하여 데스크탑 PC들의 서로 다른 환경에서 클러스터링 알고리즘 선정 및 요소를 올바르게 적용할 수 있도록 클러스터링 시뮬레이션을 제공한다.

Keywords

JBCRJM_2019_v8n1_45_f0001.png 이미지

Fig. 1. XML Scheme of Host in RCS

JBCRJM_2019_v8n1_45_f0002.png 이미지

Fig. 2. RCS Architecture

JBCRJM_2019_v8n1_45_f0003.png 이미지

Fig. 3. Initial Configuration View of RCS

JBCRJM_2019_v8n1_45_f0004.png 이미지

Fig. 4. 2D and 3D Simulation of RCS

JBCRJM_2019_v8n1_45_f0005.png 이미지

Fig. 5. Host Clustering and Monitoring through the RCS

JBCRJM_2019_v8n1_45_f0006.png 이미지

Fig. 6. RCS Architecture

Table 1. RCS Clustering Elements

JBCRJM_2019_v8n1_45_t0001.png 이미지

References

  1. Nitesh Shrivastava, and Ganesh Kumar, "A survey on cost effective multi-cloud storage in cloud computing," International Journal of Advanced Research in Computer Engineering and Technology, Vol. 2, Issue. 4, Apr. 2013.
  2. So-Yeon Kim, Hong-Chan Roh, Chi-Hyun Park, and Sang-Hyun Park, "Analysis of Metadata Server on Clustered File Systems," in Proceedings of the Korea Computer Congress 2009, KCC, Vol. 36, No. 1, Jul. 2009.
  3. Pradnya Eknath Gaonkar, Sachin Bojewar, and Jayesh Ajit Das, “A Survey: Data Storage Technologies,” International Journal of Engineering Science and Innovative Technology, Vol. 2, No. 2, pp. 547-554, Mar. 2013.
  4. Garth A. Gibson, and Rodney Van Meter, “Network attached storage architecture,” Communications of the ACM, Vol. 43, No. 11, pp. 37-45, Nov. 2000. https://doi.org/10.1145/353360.353362
  5. B. Dong, Q. Zheng, F. Tian, K. Chao, R. Ma, and R. Anane, “An optimized approach for storing and accessing small files on cloud storage,” Journal of Network and Computer Applications, Vol. 35, No. 6, pp. 1847-1862, Nov. 2012. https://doi.org/10.1016/j.jnca.2012.07.009
  6. Zhanquan Sun, Geoffrey Fox, Weidong Gu, and Zhao Li, “A parallel clustering method combined information bottleneck theory and centroid-based clustering,” Journal of Supercomputing, Vol. 69, No. 1, pp. 452-467, Jul. 2014. https://doi.org/10.1007/s11227-014-1174-1
  7. Sarah P. Preheim, Allison R. Perrotta, Antonio M. Martin-Platero, Anika Gupta, and Eric J. Alm, “Distribution-Based Clustering: Using Ecology To Refine the Operational Taxonomic Unit,” Applied and Environmental Microbiology, Vol. 79, No. 21, pp. 6593-6603, Nov. 2013. https://doi.org/10.1128/AEM.00342-13
  8. Hans-Peter Kriegel, Peer Kroger, Jorg Sander, Arthur Zimek, "Density-based clustering," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 1, No. 3, pp. 231-240, May/Jun. 2011. https://doi.org/10.1002/widm.30
  9. Hartuv Erez, and Shamir Ron, “A clustering algorithm based on graph connectivity,” Information Processing Letters, Vol. 76, No. 4, pp. 175-181, Dec. 2000. https://doi.org/10.1016/S0020-0190(00)00142-3
  10. Manojit Chattopadhyay, Pranab K. Dan, and Sitanath Mazumdar, "Comparison of visualization of optimal clustering using self-organizing map and growing hierarchical selforganizing map in cellular manufacturing system," Applied Soft Computing, Vol. 22, pp. 528-543, Sep. 2014. https://doi.org/10.1016/j.asoc.2014.04.027
  11. Naidila Sadashiv, and S. M Dilip Kumar, "Cluster, Grid and Cloud Computing: A Detailed Comparison," in Proceedings of the 6th International Conference on Computer Science and Education, ICCSE 2011, pp. 477-482, Aug. 2011.
  12. Anthony Sulistio, Uros Cibej, Srikumar Venugopal, Borut Robic, Rajkumar Buyya, “A toolkit for modeling and simulating data Grids: an extension to GridSim,” Concurrency and Computation: Practice and Experience, Vol. 20, No. 13, pp. 1591-1609, Sep. 2008. https://doi.org/10.1002/cpe.1307
  13. Rajkumar Buyya, and Manzur Murshed, “GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing,” Concurrency and Computation: Practice and Experience, Vol. 14, No. 13-15, pp. 1175-1220, Nov. 2002. https://doi.org/10.1002/cpe.710
  14. Luis F. W. Goes, Luiz E. S. Ramos, and Carlos A. P. S. Martins, "ClusterSim: A Java-Based Parallel Discrete-Event Simulation Tool for Cluster Computing," in Proceedings of the 2004 IEEE International Conference on Cluster Computing, pp. 401-410, Sep. 2004.
  15. Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose, and Rajkumar Buyya, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation or resource provisioning algorithms,” Software: Practice and Experience, Vol. 41, No. 1, pp. 23-50, Jan. 2011. https://doi.org/10.1002/spe.995