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

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Enhancing RCC(Recyclable Counter With Confinement) with Cuckoo Hashing

Cuckoo Hashing을 이용한 RCC에 대한 성능향상

  • Received : 2016.04.29
  • Accepted : 2016.06.02
  • Published : 2016.06.30
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Abstract

According to rapidly increasing of network traffics, necessity of high-speed router also increased. For various purposes, like traffic statistic and security, traffic measurement function should performed by router. However, because of the nature of high-speed router, memory resource of router was limited. RCC proposed a way to measure traffics with high speed and accuracy. Additional quadratic probing hashing table used for accumulating elephant flows in RCC. However, in our experiment, quadratic probing performed many overheads when allocated small memory space or load factor was high. Especially, quadratic requested many calculations in update and lookup. To face this kind of problem, we use a cuckoo hashing which performed a good performance in update and loop for enhancing the RCC. As results, RCC with cuckoo hashing performed high accuracy and speed even when load factor of memory was high.

인터넷 트래픽양의 급증에 따라 고속 라우터의 수요가 많아졌다. 트래픽 통계 또는 보안 등의 목적으로 라우터에서 패킷을 측정해야 하는데 고속 라우터의 특성상 메모리공간이 제한적이다. RCC는 적은 메모리로 트래픽을 정확하고 효율적으로 측정하는 방법을 제시했다. RCC에서는 트래픽을 측정하는데 큰 Flow를 추가적인 Quadratic Probing 기반 해시 테이블에 누적하는 방법 사용한다. 그런데 Quadratic Probing은 적은 메모리 또는 메모리 사용률이 많은 상황에서 연산량이 많으며, 특히 갱신 또는 실시간 조회가 자주 발생하는 시스템에서 오버헤드가 크다. 이 논문에서는 RCC의 특성을 분석하고 실험을 통해 Quadratic Probing의 문제점을 증명하며 갱신 또는 조회에 효율적인 Cuckoo Hashing을 사용하여 RCC의 성능을 개선한다. 실험 결과에 따르면 RCC에서 Cuckoo Hashing을 사용할 때 메모리 사용률이 높은 상황에서도 높은 정확도를 보여주었고, 효율적으로 트래픽을 측정할 수 있었다.

Keywords

References

  1. Cisco, Cisco Visual Networking Index: Forecast and methodology, 2012-2017, Retrieved May, 29, 2013, from http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-481360_ns827_Networking_Solutions_White_Paper.html.
  2. Cisco, NetFlow systems, from http://www.cisco.com/en/US/products/ps6601/products_ios_protocol_group_home.html.
  3. InMon Corp, sFlow accuracy & billing, Retrieved 2003, from http://www.sflow.org/sFlowOverview.pdf.
  4. P. Lieven and B. Scheuermann, "High-speed per-flow traffic measurement with probabilistic multiplicity counting," in Proc. IEEE INFOCOM, pp. 1-9, San Diego, USA, Apr. 2010.
  5. A. Kumar, J. JimXu, and J. Wang, "Space-code bloom filter for efficient per-flow traffic measurement," in Proc. IEEE INFOCOM, pp. 1762-1773, Hong Kong, China, Mar. 2004.
  6. Y. Lu, A. Montanari, B. Prabhakar, S. Dharmapurikar, and A. Kabbani, "Counter braids: A novel counter architecture for per-flow measurement," in Proc. ACM SIGMETRICS, pp. 121-132, Annapolis, USA, Jun. 2008.
  7. Y. Lu and B. Prabhakar, "Robust counting via counter braids: An error-resilient network measurement architecture," in Proc. IEEE INFOCOM, pp. 522-530, Rio de Janeiro, Brazil, Apr. 2009.
  8. T. Li, S. Chen, and Y. Ling, "Fast and compact per-flow traffic measurement through randomized counter sharing," in Proc. IEEE INFOCOM, pp. 1799-1807, Shang Hai, China, Apr. 2011.
  9. K. Y. Whang, B. Vander-Zanden, and H. Taylor, "A linear-time probabilistic counting algorithm for database applications," ACM Trans. Database Syst., vol. 15, no. 2, pp. 208-229, Jun. 1990. https://doi.org/10.1145/78922.78925
  10. M. Yoon, T. Li, S. Chen, and J. K. Peir, "Fit a compact spread estimator in small high-speed memory," IEEE/ACM Trans. Netw., vol. 19, no. 5, pp. 1253-1264, Oct. 2011. https://doi.org/10.1109/TNET.2010.2080285
  11. D. H. Nyang and D. O. Shin. "Recyclable counter with confinement for real-time per-flow measurement," IEEE/ACM Trans. Netw., Jan. 2016.
  12. R. Pagh and F. F. Rodler. "Cuckoo hashing" J. Algorithms, vol. 51, no. 2, pp. 122-144, May 2004. https://doi.org/10.1016/j.jalgor.2003.12.002
  13. C. Estan and G. Varghese, "New directions in traffic measurement and accounting," in Proc. ACM SIGCOMM, pp. 323-336, Pittsburgh, USA, Aug. 2002.
  14. R. Karp, S. Shenker, and C. Papadimitriou, "A simple algorithm for finding frequent elements in streams and bags," ACM Trans. Database Syst., vol. 28, no. 1, pp. 51-55, Mar. 2003. https://doi.org/10.1145/762471.762473
  15. N. Kamiyama and T. Mori, "Simple and accurate identification of high rate flows by packet sampling," in Proc. IEEE INFOCOM, pp. 1-13, Barcelona, Spain, Apr. 2006.
  16. X. Dimitropoulos, P. Hurley, and A. Kind, "Probabilistic lossy counting: An efficient algorithm for finding heavy hitters," Comput. Commun. Rev., vol. 38, no. 1, pp. 7-16, Jan. 2008.
  17. S. Cohen and Y. Matias, "Spectral bloom filters," in Proc. ACM SIGMOD, pp. 241-252, San Diego, USA, Jun. 2003.
  18. P. Flajolet and G. Nigel Martin, "Probabilistic counting algorithms for database applications," J. Comput. Syst. Sci., vol. 31, pp. 182-209, Apr. 1985. https://doi.org/10.1016/0022-0000(85)90041-8
  19. The Cooperative Association for Internet Data Analysis, Equinix Chicago data center, Retrieved Nov. 21, 2013, from http://www.caida.org.

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