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Realistic and responsive network traffic generation

Published:11 August 2006Publication History
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Abstract

This paper presents Swing, a closed-loop, network-responsive traffic generator that accurately captures the packet interactions of a range of applications using a simple structural model. Starting from observed traffic at a single point in the network, Swing automatically extracts distributions for user, application, and network behavior. It then generates live traffic corresponding to the underlying models in a network emulation environment running commodity network protocol stacks. We find that the generated traces are statistically similar to the original traces. Further, to the best of our knowledge, we are the first to reproduce burstiness in traffic across a range of timescales using a model applicable to a variety of network settings. An initial sensitivity analysis reveals the importance of capturing and recreating user, application, and network characteristics to accurately reproduce such burstiness. Finally, we explore Swing's ability to vary user characteristics, application properties, and wide-area network conditions to project traffic characteristics into alternate scenarios.

References

  1. ABRY, P., AND VEITCH, D. Wavelet analysis of long-range-dependent traffic. IEEE Transactions on Information Theory 44, 1 (1998), 2--15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Auckland-VI trace archive, University of Auckland, New Zealand. http://pma.nlanr.net/Traces/long/auck6.html.Google ScholarGoogle Scholar
  3. BARFORD, P., AND CROVELLA, M. Generating representative web workloads for network and server performance evaluation. In MMCS (1998), pp. 151--160. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. BARFORD, P., AND CROVELLA, M. Critical path analysis of TCP transactions. In ACM SIGCOMM (2000). Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. BENKO, P., AND VERES, A. A passive method for estimating end-to-end tcp packet loss. In IEEE Globecom (2002).Google ScholarGoogle ScholarCross RefCross Ref
  6. CAIDA. http://www.caida.org.Google ScholarGoogle Scholar
  7. CAO, J., CLEVELAND, W., GAO, Y., JEFFAY, K., SMITH, F. D., AND WEIGLE, M. Stochastic models for generating synthetic http source traffic. In IEEE INFOCOMM (2004).Google ScholarGoogle Scholar
  8. CHAN LAN, K., AND HEIDEMANN, J. A tool for rapid model parameterization and its applications. In MoMeTools Workshop (2003). Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. CHENG, Y. -C., HOELZLE, U., CARDWELL, N., SAVAGE, S., AND VOELKER, G. M. Monkey see, monkey do: A tool for tcp tracing and replaying. In USENIX Technical Conference (2004). Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. DANZIG, P. B., AND JAMIN, S. tcplib: A library of TCP/IP traffic characteristics. USC Networking and Distributed Systems Laboratory TR CS-SYS-91-01 (October, 1991).Google ScholarGoogle Scholar
  11. DOVROLIS, C., RAMANATHAN, P., AND MOORE, D. Packet dispersion techniques and capacity estimation. In IEEE/ACM Transactions in Networking, Dec (2004). Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. FELDMANN, A., GILBERT, A. C., HUANG, P., AND WILLINGER, W. Dynamics of IP traffic: A study of the role of variability and the impact of control. In ACM SIGCOMM (1999). Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. FLOYD, S., AND PAXSON, V. Difficulties in simulating the internet. In IEEE/ACM Transactions on Networking (2001). Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. GUMMADI, K. P., DUNN, R. J., SAROIU, S., GRIBBLE, S. D., LEVY, H. M., AND ZAHORJAN, J. Measurement, modeling, and analysis of a peer-to-peer file-sharing workload. In Symposium on Operating Sytems Principles (SOSP) (2003). Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. HARFOUSH, K., BESTAVROS, A., AND BYERS, J. Measuring bottleneck bandwidth of targeted path. In IEEE INFOCOM (2003).Google ScholarGoogle ScholarCross RefCross Ref
  16. HERNANDEZ-CAMPOS, F., SMITH, F. D., AND JEFFAY, K. Generating realistic tcp workloads. In CMG2004 Conference (2004).Google ScholarGoogle Scholar
  17. HUANG, P., FELDMANN, A., AND WILLINGER, W. A non-intrusive, wavelet-based approach to detecting network performance problems. In Internet Measurement Workshop (2001). Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. JAIN, M., AND DOVROLIS, C. End-to-end available bandwidth: Measurement methodology, dynamics, and relation with tcp throughput. In ACM SIGCOMM (2002). Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. JAIN, M., AND DOVROLIS, C. Ten fallacies and pitfalls in end-to-end available bandwidth estimation. In Internet Measurement Conference (2004). Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. JAISWAL, S., IANNACONE, G., DIOT, C., KUROSE, J., AND TOWSLEY, D. Inferring tcp connection characteristics through passive measurements. In IEEE INFOCOM (2004).Google ScholarGoogle ScholarCross RefCross Ref
  21. JIANG, H., AND DOVROLIS, C. Why is the internet traffic bursty in short (sub-rtt) time scales? In SIGMETRICS (2005). Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. KARAGIANNIS, T., PAPAGIANNAKI, K., AND FALOUTSOS, M. Blinc: Multilevel traffic classification in the dark. In ACM SIGCOMM (2005). Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. LE, L., AIKAT, J., JEFFAY, K., AND SMITH, F. D. The Effects of Active Queue Management on Web Performance. In ACM SIGCOMM (2003). Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. LEE, B. O., FROST, V. S., AND JONKMAN, R. Netspec 3. 0 source models for telnet, ftp, voice, video and WWW traffic. In Technical Report ITTC-TR-10980-19, University of Kansas (1997).Google ScholarGoogle Scholar
  25. MAH, B. A. An empirical model of HTTP network traffic. In IEEE INFOCOM (2) (1997). Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Mawi working group traffic archive. http://tracer.csl.sony.co.jp/mawi/.Google ScholarGoogle Scholar
  27. MEDINA, A., TAFT, N., SALAMATIAN, K., BHATTACHARYYA, S., AND DIOT, C. Traffic Matrix Estimation: Existing Techniques and New Directions. In ACM SIGCOMM (2002). Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. MOORE, A., AND ZUEV, D. Internet traffic classification using bayesian analysis techniques. In ACM SIGMETRICS (2005). Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. The national laboratory for applied network research (nlanr) http://www.nlanr.net.Google ScholarGoogle Scholar
  30. The network simulator ns-2. http://www.isi.edu/nsnam/ns.Google ScholarGoogle Scholar
  31. PAXSON, V. Empirically derived analytic models of wide-area TCP coections. IEEE/ACM Transactions on Networking 2, 4 (1994), 316--336. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. PAXSON, V. End-to-end internet packet dynamics. In IEEE/ACM Transactions on Networking, Vol. 7, No. 3 (June, 1999), pp. 277--292. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. RUPP, A., DREGER, H., FELDMANN, A., AND SOMMER, R. Packet trace manipulation framework for test labs. In Internet Measurement Conference (2004). Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. SEN, S., AND WANG, J. Analyzing peer-to-peer traffic across large networks. In ACM SIGCOMM Internet measurement workshop (2002). Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. SMITH, F. D., HERNANDEZ-CAMPOS, F., JEFFAY, K., AND OTT, D. What TCP/IP protocol headers can tell us about the web. In SIGMETRICS/Performance (2001), pp. 245--256. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. SOMMERS, J., AND BARFORD, P. Self-configuring network traffic generation. In Internet Measurement Conference (2004). Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. STANIFORD, S., PAXSON, V., AND WEAVER, N. How to 0wn the Internet in Your Spare Time. In USENIX Security Symposium (2002). Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. TANG, W., FU, Y., CHERKASOVA, L., AND VAHDAT, A. Medisyn: a synthetic streaming media service workload generator. In 13th International workshop on NOSSDAV (2003). Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. VAHDAT, A., YOCUM, K., WALSH, K., MAHADEVAN, P., KOSTIC, D., CHASE, J., AND BECKER, D. Scalability and accuracy in a large-scale network emulator. In Operating Systems Design and Implementation (OSDI) (2002). Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. WHITE, B., LEPREAU, J., STOLLER, L., RICCI, R., GURUPRASAD, S., NEWBOLD, M., HIBLER, M., BARB, C., AND JOGLEKAR, A. An Integrated Experimental Environment for Distributed Systems and Networks. In Operating Sytems Design and Implementation (OSDI) (2002). Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. WILLINGER, W., PAXSON, V., AND TAQQU, M. S. Self-similarity and Heavy Tails: Structural Modeling of Network Traffic. In A Practical Guide to Heavy Tails: Statistical Techniques and Applications (1998). Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. XU, K., ZHANG, Z.-L., AND BHATTACHARYA, S. Profiling internet backbone traffic: Behavior models and applications. In ACM SIGCOMM (2005). Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. YOCUM, K., EADE, E., DEGESYS, J., BECKER, D., CHASE, J., AND VAHDAT, A. Toward scaling network emulation using topology partitioning. In Eleventh IEEE/ACM International Symposium on Modeling, Analysis, and Simulation of Computer and Telecounication Systems (MASCOTS) (2003).Google ScholarGoogle ScholarCross RefCross Ref
  44. ZHANG, Y., BRESLAU, L., PAXSON, V., AND SHENKER, S. Onthe characteristics and origins of internet flow rates. In ACM SIGCOMM (2002). Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. ZHANG, Y., PAXSON, V., AND SHENKER, S. The stationarity of internet path properties: Routing, loss, and throughput. ACIRI Technical Report (2000).Google ScholarGoogle Scholar

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    • Published in

      cover image ACM SIGCOMM Computer Communication Review
      ACM SIGCOMM Computer Communication Review  Volume 36, Issue 4
      Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
      October 2006
      445 pages
      ISSN:0146-4833
      DOI:10.1145/1151659
      Issue’s Table of Contents
      • cover image ACM Conferences
        SIGCOMM '06: Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
        September 2006
        458 pages
        ISBN:1595933085
        DOI:10.1145/1159913

      Copyright © 2006 ACM

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      • Published: 11 August 2006

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