KIPS Transactions on Computer and Communication Systems (정보처리학회논문지:컴퓨터 및 통신 시스템)

Korea Information Processing Society (한국정보처리학회)
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Performance Analysis and Improvement of WANProxy

WANProxy의 성능 분석 및 개선

  • 김하늘 (숭실대학교 스마트시스템소프트웨어학과) ;
  • 지승규 (숭실대학교 스마트시스템소프트웨어학과) ;
  • 정규식 (숭실대학교 스마트시스템소프트웨어학과)
  • Received : 2019.07.17
  • Accepted : 2019.11.27
  • Published : 2020.03.31
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Abstract

In the current trend of increasing network traffic due to the popularization of cloud service and mobile devices, WAN bandwidth is very low compared to LAN bandwidth. In a WAN environment, a WAN optimizer is needed to overcome performance problems caused by transmission protocol, packet loss, and network bandwidth limitations. In this paper, we analyze the data deduplication algorithm of WANProxy, an open source WAN optimizer, and evaluate its performance in terms of network latency and WAN bandwidth. Also, we evaluate the performance of the two-stage compression method of WANProxy and Zstandard. We propose a new method to improve the performance of WANProxy by revising its data deduplication algorithm and evaluate its performance improvement. We perform experiments using 12 data files of Silesia with a data segment size of 2048 bytes. Experimental results show that the average compression rate by WANProxy is 150.6, and the average network latency reduction rates by WANProxy are 95.2% for a 10 Mbps WAN environment and 60.7% for a 100 Mbps WAN environment, respectively. Compared with WANProxy, the two-stage compression of WANProxy and Zstandard increases the average compression rate by 33%. However, it increases the average network latency by 2.1% for a 10 Mbps WAN environment and 5.27% for a 100 Mbps WAN environment, respectively. Compared with WANProxy, our proposed method increases the average compression rate by 34.8% and reduces the average network latency by 13.8% for a 10 Mbps WAN and 12.9% for a 100 Mbps WAN, respectively. Performance analysis results of WANProxy show that its performance improvement in terms of network latency and WAN bandwidth is excellent in a 10Mbps or less WAN environment while superior in a 100 Mbps WAN environment.

클라우드 서비스와 모바일 기기의 대중화로 네트워크 트래픽이 계속 증가하고 있는 현재 추세에 LAN 대역폭에 비해 WAN 대역폭이 아주 낮다. WAN 환경에서는 전송 프로토콜, 패킷 손실, 네트워크 대역폭 한계 때문에 생기는 성능 문제를 극복하는 WAN 최적화기가 필요하다. 본 논문에서는 오픈소스 WAN 최적화기인 WANProxy의 데이터 중복제거 알고리즘을 분석하고 성능을 네트워크 대기시간 및 WAN 대역폭 관점에서 평가한다. 또한, WANProxy에 추가로 zstd를 적용하는 2단계 압축을 적용할 경우의 성능을 평가한다. 또한, WANProxy의 데이터 중복 제거 방법을 개선한 새로운 방법을 제안하고 성능 개선 효과를 평가한다. 데이터 세그먼트 크기를 2048바이트로 하고 Silesia의 12개 데이터 파일을 이용한 성능 실험을 수행한다. 실험 결과에 의하면, WANProxy에 의한 평균 압축률이 150.6이고 네트워크 대기시간 평균 감소율은 10 Mbps WAN 환경에서는 95.2%, 100 Mbps WAN 환경에서는 60.7%가 된다. WANProxy에 추가로 zstd를 적용하는 방법은 WANProxy를 적용하는 경우와 비교할 때 압축률이 평균 33% 증가하지만 네트워크 대기시간이 10 Mbps WAN 환경에서는 평균 2.1%, 100 Mbps WAN 환경에서는 평균 5.2% 각각 증가한다. 본 논문에서 제안한 개선 방법을 WANProxy에 적용한 경우는 기존의 WANProxy와 비교할 때 압축률이 평균 34.8% 증가하고 네트워크 대기시간이 10 Mbps WAN 환경에서는 평균 13.8%, 100 Mbps WAN 환경에서는 평균 12.9% 각각 감소한다. 성능 분석 결과에 의하면, WAN 대역폭이 10 Mbps 이하인 환경에서 WANProxy를 적용할 경우 네트워크 대기시간과 WAN 대역폭 관점에서 성능 개선 효과가 아주 우수하고 WAN 대역폭이 100 Mbps 환경에서도 우수하다.

Keywords

References

  1. Ted Grevers, Jr. and Joel Christner, "Application Acceleration and WAN Optimization Fundamentals," 2012, Cisco Press.
  2. Y. Deng and S. Manoharan, "A review of network latency optimization techniques," 2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), Aug. 2013.
  3. Y. Zhang, N. Ansari, M. Wu, and H. Yu, "On Wide Area Network Optimization," IEEE Communications Surveys and Tutorials, Vol.14, No.4, pp.1090-1113, Fourth Quarter 2012. https://doi.org/10.1109/SURV.2011.092311.00071
  4. T. Han, N. Ansari, M. Wu, and H. Yu, "On Accelerating Content Delivery in Mobile Networks," IEEE Communications Surveys & Tutorials, Vol.15, No.3, Third Quarter 2013.
  5. M. B. Nirmala, "Wan optimization tools, techniques and research issues for cloud-based big data analytics," 2014 World Congress on Computing and Communication Technologies.
  6. M. Nelson and J. Gailly, "The Data Compression Book," 2nd edition, M&T Books, 1996.
  7. DEFLATE [Internet], https://en.wikipedia.org/wiki/DEFLATE
  8. Zlib [Internet], https://en.wikipedia.org/wiki/Zlib
  9. Zstandard [Internet], https://en.wikipedia.org/wiki/Zstandard
  10. Zstd [Internet], https://facebook.github.io/zstd/
  11. Y. Zhang and N. Ansari, "On Protocol-Independent Data Redundancy Elimination," IEEE Communications Surveys and Tutorials, Vol.16, No.1, pp.455-472, First Quarter, 2014. https://doi.org/10.1109/SURV.2013.052213.00186
  12. N. Spring and D. Wetherall, "A protocol-independent Technique for Eliminating Redundant Network Traffic," ACM SIGCOMM, 2000.
  13. A. Anand, C. Muthukrishnan, A. Akella, and R. Ramjee, "Redundancy in network traffic: findings and implications," SIGMETRICS '09 Proceedings of the Eleventh International Joint Conference on Measurement and Modeling of Computer Systems.
  14. Q. He, Z. Li, and X. Zhang, "Data Deduplication Techniques," in Proc. of International Conference on Future Information Technology and Management Engineering (FITME), Oct. 2010, pp.430-433.
  15. L. Huang, H. Feng, Y. Le, and C. Shen, "Redundancy Elimination on Unidirectional Lossy Links," 2018 27th International Conference on Computer Communication and Networks (ICCCN).
  16. F. Le, M. Srivatsa, and A. K. Lyengar, "Byte caching in wireless networks," IEEE 32nd International Conference on Distributed Computing Systems (ICDCS), 2012, pp. 265-274.
  17. I. Demirkan, "Improved Byte Caching Techniques," IEEE International Black Sea Conference on Communications and Networking, 2015, pp.127-131.
  18. R. Kaur, I. Chana, and J. Bhattacharya, "Data deduplication techniques for efficient cloud storage management: a systematic review," The Journal of Supercomputing, Vol.74, No.5, pp.2035-2085, May 2018. https://doi.org/10.1007/s11227-017-2210-8
  19. Z. Yan, L. Zhang, W. Ding, and Q. Zheng, "Heterogeneous Data Storage Management with Deduplication in Cloud Computing," IEEE Transactions on Big Data, July 2019.
  20. Wanproxy [Internet], http://wanproxy.org/
  21. Librsync [Internet], https://librsync.github.io
  22. Solowan [Internet], https://github.com/solowan/solowan
  23. Silesia Compression Corpus [Internet], http://sun.aei.polsl.pl/-sdeor/index.php?page=silesia
  24. Zstd Compression Format [Internet], https://github.com/valyala/gozstd/blob/master/zstd/doc/zstd_compression_format.md