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

  • 제33권8B호
  • /
  • Pages.667-680
  • /
  • 2008
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  • 1226-4717(pISSN)
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  • 2287-3880(eISSN)
한국통신학회 (The Korean Institute of Commucations and Information Sciences)
권호 표지

무선 랜 트래픽의 분석과 모델링

Modeling and Analysis of Wireless Lan Traffic

  • 대쉬도즈얌힌 (한양대학교 전자컴퓨터통신공학과 분산멀티미디어 연구실) ;
  • 이성진 (한양대학교 전자컴퓨터통신공학과 분산멀티미디어 연구실) ;
  • 원유집 (한양대학교 전자컴퓨터통신공학과 분산멀티미디어 연구실)
  • 발행 : 2008.08.31
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초록

본 논문에서는 802.11 무선 랜 네트워크 트래픽의 실제 관측 자료에 대한 연구 결과를 보인다. 패킷 트레이스는 대학 캠퍼스의 무선 랜 시설에서 얻은 자료로서 총합된 트래픽(aggregate traffic), 업스트림 트래픽(upstream traffic), 다운스트림 트래픽(downstream traffic), 그리고 TCP 패킷으로만 구성된 통합된 트래픽으로 이 4개의 트래픽 데이터를 수집하였다. 수집한 데이터에서 byte count 프로세스와 packet count 프로세스로 구성된 트래픽의 시계열과 시계열의 주변분포, 그리고 패킷 크기 분포에 대한 분석을 한다. 4개의 모든 데이터의 byte count 프로세스와 packet count 프로세스에서 장기 의존성 성질이 나타났다. 사용자가 인터넷으로 접속하는 없트�� 트래픽의 평균 패킷 크기는 151.7 byte였는데 다른 데이터의 평균 패킷 크기는 모드 260 byte 이상이었다. 최대 크기를 갖는 패이로드(payload)는 업스트림에서 3%, 그리고 나온트림에서 10%로 나타났다. 이런 분명한 패킷 크기 분포의 차이에도 불구하고 모든 4개의 데이터에서는 허스트(Hurst) 값이 모두 유사하게 나왔다. 허스트 값만으로는 트래픽의 확률적 특성을 충분히 설명할 수가 없다. 트래픽의 특성을 fractional-ARIMA(FARINA) 그리고 fractional Gaussian noise(FGN)으로 모델링을 한다. FGN은 연산을 하는데 있어서는 더 효율적이었고, FARINA는 트래픽 특성을 정확하게 모델링하는데 더 좋은 결과를 얻었다.

In this work, we present the results of our empirical study on 802.11 wireless LAN network traffic. We collect the packet trace from existing campus wireless LAN infra-structure. We analyzed four different data sets: aggregate traffic, upstream traffic, downstream traffic, tcp only packet trace from aggregate traffic. We analyze the time series aspect of underlying traffic (byte count process and packet count process), marginal distribution of time series, and packet size distribution. We found that in all four data sets there exist long-range dependent property in byte count and packet count process. Inter-arrival distribution is well fitted with Pareto distribution. Upstream traffic, i.e. from the user to Internet, exhibits significant difference in its packet size distribution from the rests. Average packet size of upstream traffic is 151.7 byte while average packet size of the rest of the data sets are all greater than 260 bytes. Packets with full data payloads constitutes 3% and 10% in upstream traffic and the downstream traffic, respectively. Despite the significant difference in packet size distribution, all four data sets have similar Hurst values. The Hurst alone does not properly explain the stochastic characteristics of the underlying traffic. We model the underlying traffic using fractional-ARIMA (FARIMA) and fractional Gaussian Noise (FGN). While the fractional Gaussian Noise based method is computationally more efficient, FARIMA exhibits superior performance in accurately modeling the underlying traffic.

키워드

참고문헌

  1. A. Balachandran, G. Voelker, P. Bahl, and P. Rangan. "Characterizing user behavior and network performance in a public wireless lan," In Proc. ACM SIGEMTRICS''02, June 2002. NY, USA
  2. Jan Beran. "Statistics for long-memory processes," Jeffrey Glosup Technometrics, 39(1):105-106, 2007
  3. J.J. Collins and C.J De Luca. "Upright, correlated random walks: a statistical-biomechanics approach to the human postural control system,"Chaos, 5(1):57-63, 1994 https://doi.org/10.1063/1.166086
  4. Cox D. "Computer Experiments with Fractional Gaussian Noises," A Review, In Statistics and Appraisal Iowa State Statistical Library, 1984
  5. R. F. Felter. "Processes stochastiques fractals avec applications These de doctorat." PhD thesis, Univ.Paris,1998
  6. U. Frisch. "Turbulence: the legacy of a.n. kolmogorov," Cambridge University Press, 1995
  7. Matthias Grossglauser and Jean- Chrysostome Bolot. "On the relevance of long-range dependence in network traffic," IEEE/ACM Transactions on Networking, 7(5):629-640, 1999. https://doi.org/10.1109/90.803379
  8. Hosking J. "Fractional differencing," Biometrika, 68(1):165-176, 1981 https://doi.org/10.1093/biomet/68.1.165
  9. Hosking J. "Modeling persistence in hydrological time series using fractional differencing," Water Resoures Research, 20(12):1898-1908, 1984 https://doi.org/10.1029/WR020i012p01898
  10. M.S. Taqqu J. Beran, R. Sherman and W. Willinger. "Long-range dependence in variable- bit-rate video traffic," IEEE TRans. on Communications, 5(43):1566-1579, 1995
  11. M.S. Taqqu J. Beran, R. Sherman and W. Willinger. "Long-range dependence in variable- bit-rate video traffic," IEEE TRans. on Communications, 5(43):1566-1579, 1995
  12. W. Leland M. Taqqu, W. Willinger and D. Wilson. "On the self-similar nature of ethernet trafficc (extended version)," IEEE/ACM Transactions on Networking, Vol.2, pp.1-15. 1994 https://doi.org/10.1109/90.282603
  13. Bruce A. Mah. "An empirical model of http network traffic," In Proceedings of the INFOCOM '97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution, page 592, Washington, DC, USA, 1997
  14. M. McNett and G. Voelker. "Access and mobility of wireless pda users," Technical Report CS''04, 2004
  15. W. Willinger M.S. Taqqu, V. Teverovsky. "Estimators for long-range dependence: an empirical study, fractals," In Fractal Geometry and Analysis, World Scientific, Vol.3, pp.785-798, 1996. Singapore
  16. Tudjarov A. Temkov D. Janevski T. Firfov. O. "Empirical modeling of internet traffic at middlelevel burstiness," In Proceedings of the 12th IEEE Mediterranean, Vol.2, pp.535-538, 2004
  17. O.Shiluhin, S.Smolskiy, and A.Ocin. "Fractial process in the telecommunication," Radio tehnika, 2003 in Russian
  18. O.Shiluhin, S.Smolskiy, and A.Ocin. "Modeling for information system," Radio Technika, Moscow, 2005 in Russian
  19. Kihong Park, Gitae Kim, and Mark Crovella. "On the relationship between file sizes, transport protocols, and self-similar network traffic," Technical Report 1996-016, 30, 1996
  20. Vern Paxson. "Fast, approximate synthesis of fractional gaussian noise for generating self-similar network traffic," SIGCOMM Comput. Commun. Rev., 27(5):5-18, 1997. NY, USA
  21. J M Pitts and J A Schormans. "Introduction IP and ATM Design and Performance With Applications Analysis Software". British Library Cataloguing in Publication Data, 2000
  22. Vinay J. Ribeiro, Rudolf H. Riedi, and Richard G. Baraniuk. "Multiscale queuing analysis of longrange-dependent network traffic," IEEE/ACM Trans. Netw., 14(5):1005-1018, 2000. Piscataway, NJ, USA. https://doi.org/10.1109/TNET.2006.882987
  23. Jong-Suk R.Lee and Hyoung-Woo Park (Korea) Hae-Duck J.Jeong (New Zealand), editors. "Simulation Run-lengths of Self-similar Queueing Processes," In Proceeding of IEEE, Vol.1. 2003
  24. William Stallings. "High-Speed Networks and Internets: Performance and Quality of Service," Prentice Hall Upper Saddle River, New Jersey, 2-th edition, 2002
  25. Shahram Teymori and Weihua Zhuang. "Queue analysis and multiplexing of heavy-tailed traffic in wireless packet data networks," Mob. Netw. Appl., 12(1):31-41, 2007. Hingham, MA, USA https://doi.org/10.1007/s11036-006-0004-y
  26. Boris Tsybakov and Nicoals D. Georganas. "On self-similar traffic in atm queues: definitions, overflow probability bound, and cell delay distribution." IEEE/ACM Trans. Netw., 5(3):397-409, 1997. Piscataway, NJ, USA https://doi.org/10.1109/90.611104
  27. Vikram M.Gadre Urali Krishna P and Uday B. Desai. "Multifractal based network traffic modeling." kluwer academic publishers, Boston/Dordrecht/London, 2003
  28. P. Abry and D. Veitch. "Wavelet analysis of long-range-dependent traffic." IEEE Transactions on Information Theory, Vol. 44. No.1, pp.2-15, 1998 https://doi.org/10.1109/18.650984
  29. M. Gospodinov, E. Gospodinova "The graphical methods for estimating hurst parameter of self-similar network traffic," Proceedings of the 2005 International Conference on Computer Systems and Technologies, 2005