한국통신학회논문지 (The Journal of Korean Institute of Communications and Information Sciences)

  • 제37권8C호
  • /
  • Pages.670-679
  • /
  • 2012
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  • 1226-4717(pISSN)
  • /
  • 2287-3880(eISSN)
한국통신학회 (The Korean Institute of Commucations and Information Sciences)
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클라우드 스토리지 최적화를 위한 고속 캐싱 및 대용량 파일 전송 기법

A Scheme on High-Performance Caching and High-Capacity File Transmission for Cloud Storage Optimization

  • 김태훈 (성균관대학교 전자전기컴퓨터공학과) ;
  • 김정한 (성균관대학교 전자전기컴퓨터공학과) ;
  • 엄영익 (성균관대학교 정보통신대학 컴퓨터공학과)
  • 투고 : 2012.04.14
  • 심사 : 2012.08.06
  • 발행 : 2012.08.31
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초록

최근 클라우드 컴퓨팅 환경의 보급과 함께 스토리지의 데이터양이 급증함에 따라 그에 따른 스토리지 저장 비용이 빠르게 증가하고 있다. 더불어, 사용자들의 다양한 서비스 및 데이터 요청으로 클라우드 스토리지의 부하 또한 급증하고 있다. 이러한 문제를 해결하기 위해 분산 파일 시스템을 통한 저비용 고성능 스토리지 환경을 제공하고자 하는 기존의 연구가 있었으나, 이에는 데이터 병렬처리, 임의위치 접근처리, 빈번한 작은 워크로드 접근처리 등의 취약점이 존재한다. 최근에는 캐싱 기술을 이용하여 이를 개선하려는 연구가 주목받고 있다. 본 논문에서는 분산 파일 시스템 환경에서 병렬 캐싱, 분산 캐싱과 공유 자원을 고려한 데이터 병렬 전송방법을 제공하는 CHPC(Cloud storage High-Performance Caching) 구조를 제안하며, 또한 이를 기존의 방법들과 비교 평가하여 스토리지 부하를 최적화하는 방법을 제시한다. 더불어, 제안 기법이 기존 클라우드 시스템에 비하여 스토리지 서버의 디스크 입출력 감소, 서버로 데이터의 요청이 집중되어 발생하는 병목현상 방지, 각 클라이언트의 중복되는 페이지 캐시 제거, 데이터 전송률 향상의 장점을 가짐을 보인다.

The recent dissemination of cloud computing makes the amount of data storage to be increased and the cost of storing the data grow rapidly. Accordingly, data and service requests from users also increases the load on the cloud storage. There have been many works that tries to provide low-cost and high-performance schemes on distributed file systems. However, most of them have some weaknesses on performing parallel and random data accesses as well as data accesses of frequent small workloads. Recently, improving the performance of distributed file system based on caching technology is getting much attention. In this paper, we propose a CHPC(Cloud storage High-Performance Caching) framework, providing parallel caching, distributed caching, and proxy caching in distributed file systems. This study compares the proposed framework with existing cloud systems in regard to the reduction of the server's disk I/O, prevention of the server-side bottleneck, deduplication of the page caches in each client, and improvement of overall IOPS. As a results, we show some optimization possibilities on the cloud storage systems based on some evaluations and comparisons with other conventional methods.

키워드

참고문헌

  1. J. Gantz and D. Reinsel, IDC's Digital Universe Stud y, sponsored by EMC, June 2011, http://idcdocserv.com/1142
  2. J. Lin, A. Bahety, S. Konda and S. Mahindrakar, "Low-Latency, High-Throughput Access to Static G lobal Resources within the Hadoop Framework," Technical Report HCIL, 2009.
  3. L. Lin, and Z. Jia, "Research on Performance Opti mization for Grid Application Using Distributed Fil e System," Frontier of Computer Science and Technology FCST, 2010
  4. A. Amin, B. Bockelman, J. Letts, "High Throughpu t WAN Data Transfer with Hadoop-based Storage," Journal of Physics: Conference Series Computing F abrics and Networking Technologies, 2011.
  5. P. Arjan, K. Christiaan, S. Rogier and K. Paul, "Survey of Technologies for Wide Area Distributed Storage," surfnet.nl, 2010
  6. M. Young-su, Jin. Ki-Sung, K. Hong-yun, "Distributed technology trends for cloud computing" Electronics and Telecommunications Trends analysis, 2009.
  7. P. Dongchul and H.C. David, "BlueSky: A Cloud- Backed File System for the Enterprise," USENIX T HE ADVANCED COMPUTING SYSTEMS ASSOCI ATION FAST, 2012.
  8. GridFTP, http://en.wikipedia.org/wiki/GridFTP
  9. Distributed File System, http://en.wikipedia.org/wiki/Distributed_filesystem
  10. B. Fitzpatrick, "Distributed caching with memcached," Linux Journal volum 2004, Iuusue 124, 2004.
  11. I. Florin, M. Guido, O. Vlad, S. Gabor and T. Walt er, "Integrating collective I/O and cooperative caching into the clusterfile parallel file system," the 18th annual international conference on Supercomputing, 2004.
  12. C. Qingkui and L. Lichun, "Parallel Cache Model Based on GridMemor," IEEExplore IFIP International Conference on Network and Parallel Computing, 2007.
  13. Relational-database, http://www.readwriteweb.com/enterprise/2009/02/is-the-relational-database-doomed.php
  14. I.Heizer, P. Leach, and D. Perry, "Common Internet File System Protocol (CIFS/1.0)," http://tools.ietf.org/html/draft-heizer-cifs-v1-spec-00.
  15. D. Hitz, J. Lau, and M. Malcolm, "File System Design for an NFS File Server Appliance," the Winter USENIX Technical Conference, 1994.
  16. R. Sandberg, D. Goldberg, S. Kleirnan, D. Walsh, and B. Lyon, "Design and Implementation of the Sun Network Filesystem," the Summer USENIX Technical Conference, 1985.
  17. J. Howard, M. Kazar, S. Nichols, D. Nichols, M. Satyanarayanan, R. Sidebotham, and M. West. "Scale and Performance in a Distributed File System," ACM Transactionson Computer Systems (TOCS), 1988. https://doi.org/10.1145/35037.35059
  18. BigData, http://en.wikipedia.org/wiki/Big_data
  19. R. Steven, and e. Soltis, "The Global File system," Mass Storage Systems and Technologies, 1996.
  20. XtreemFS official site, http://www.xtreemfs.org/
  21. Ceph official site, http://ceph.newdream.net/
  22. GlusterFS official site, http://www.gluster.org/
  23. MooseFS official site, http://www.moosefs.org/
  24. S Ghemawat and H Gobioff, "The Google file system," ACM SIGOPS Operating Systems, 2003
  25. A. Bessani, M. Correia, B. Quaresma, F. Andre, an d P. Sousa. "DepSky: Dependable and Secure Stora ge in a Cloud-of-Clouds," In EuroSys 2011, 2011.
  26. Nasuni, http://www.nasuni.com
  27. SCTP, http://en.wikipedia.org/wiki/Stream_Control_Transmission_Protocol
  28. SCTP Standards and Technology Analysis and Forecast, http://ettrends.etri.re.kr/PDFData/18-3_011_020.pdf
  29. NFS & CINF, http://www.differencebetween.net/technology/difference-between-nfs-and-cifs/
  30. P. Dongchul, and H.C David, "Hot Data Identification for Flash Memory Using Multiple Bloom Filters," USENIX THE ADVANCED COMPUTING SYST EMS ASSOCIATION FAST, 2011.
  31. Bloom Filter, http://en.wikipedia.org/wiki/Bloom_filter
  32. Touch Count LRU, http://wiki.ex-em.com/index.php/Latch:_cache_buffers_chains
  33. RamCloud, https://ramcloud.stanford.edu/wiki/display/ramcloud/Home
  34. Values for each formula, http://www.computer-definition.com/access-time.php
  35. Values for each formula, http://www.content-networking.com/papers/web-caching-zipf.pdf