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Cluster Computing

, Volume 16, Issue 4, pp 989–1003 | Cite as

Enabling richer statistical MANET simulations through cluster computing

  • Deepali Arora
  • Eamon Millman
  • Stephen W. Neville
Article

Abstract

The wide-scale adoption of modern smart phones and other multi-radio mobile devices, has begun to provide pragmatic deployment environments for non-cellular mobile ad hoc network (MANET) services (i.e., for disaster recovery scenarios, peered mobile games, social networking applications, etc.). User perceptions of the quality of such MANET services will be driven, in part, by standard network-level quality of service (QoS) metrics such as delay, jitter, throughput, etc. Much of the existing MANET literature has explored these issues, as well as MANET routing protocol design, through single computer Monte Carlo simulations (e.g., via ns-2, ns-3, OMNeT++, or OpNet). Results are then reported as the averages of these Monte Carlo runs. As is well known from probability and statistics, such averaging is only meaningful when applied across statistically ergodic data (i.e., data drawn from the same underlying distribution). But, assessing the validity of this underlying ergodic assumption requires transitioning to more rigorous cluster-based MANET simulation frameworks. This work highlights the theoretical rationale for such ergodicity testing, the developments of a cluster-based framework, the STARs framework, to support such testing, and the results and insights obtained by using this framework to evaluate the popular DYMO and OLSR MANET routing protocols. This work also discusses why the insights ergodic testing provides are of interest to potential real-world MANET deployments.

Keywords

MANET Cluster computing Statistical analysis 

References

  1. 1.
    Rahman, M., Al Muktadir, A.: The impact of data send rate, node velocity and transmission range on QoS parameters of OLSR and DYMO MANET routing protocols. In: 10th International Conference on Computer and Information Technology (ICCIT 2007), pp. 1–6 (2007) Google Scholar
  2. 2.
    Krasnovsky, M., Wieser, V.: A performance of wireless ad-hoc network routing protocol. In: 17th International Conference Radioelektronika 2007, pp. 1–3 (2007) CrossRefGoogle Scholar
  3. 3.
    Benkovic, J., Kotuliak, I., Truchly, P.: Comparison of DSR and DYMO routing protocols in building environments. In: International Symposium ELMAR ’09, pp. 109–112 (2009) Google Scholar
  4. 4.
    Koltsidas, G., Karapantazis, S., Theodoridis, G., Pavlidou, F.: A detailed study of dynamic MANET on-demand multipath routing for mobile ad hoc networks. In: International Conference on Wireless and Optical Communications Networks (IFIP 2007), pp. 1–5 (2007) CrossRefGoogle Scholar
  5. 5.
    Kum, D.W., Park, J.S., Cho, Y.Z., Cheon, B.Y.: Performance evaluation of AODV and DYMO routing protocols in MANET. In: 7th IEEE Consumer Communications and Networking Conference (CCNC), pp. 1–2 (2010) Google Scholar
  6. 6.
    Koltsidas, G., Pavlidou, F.N., Kuladinithi, K., Timm-Giel, A., Gorg, C.: Investigating the performance of a multipath DYMO protocol for ad-hoc networks. In: IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2007), pp. 1–5 (2007) CrossRefGoogle Scholar
  7. 7.
    Malany, A., Chandrasekaran, R.: Mobility impact, timing analysis and repeatability issues of DYMO protocol in a precise mobile ad hoc network. Int. J. Comput. Sci. Netw. Secur. 9(6), 107–112 (2005) Google Scholar
  8. 8.
  9. 9.
  10. 10.
    The network simulator—ns2. http://www.isi.edu/nsnam/ns/
  11. 11.
    The network simulator—ns3. http://www.isi.edu/nsnam/ns/
  12. 12.
    Yoon, J., Liu, M., Noble, B.: Random waypoint considered harmful. In: Twenty-Second Annual Joint Conference of the IEEE Computer and Communications (INFOCOM 2003), vol. 2, pp. 1312–1321 (2003) Google Scholar
  13. 13.
    McGuire, M.: Stationary distributions of random walk mobility models for wireless ad hoc networks. In: Proceedings of the 6th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc ’05), pp. 90–98 (2005) CrossRefGoogle Scholar
  14. 14.
    Millman, E., Arora, D., Neville, S.: STARS: A framework for statistically rigorous simulation-based network research. In: IEEE Workshops of International Conference on Advanced Information Networking and Applications (WAINA 2011), pp. 733–739 (2011) CrossRefGoogle Scholar
  15. 15.
    Varga, A., Sekercioglu, A.: The OMNeT++ discrete event simulation system. In: Proceedings of the European Simulation Multiconference (2001) Google Scholar
  16. 16.
    Dreibholz, T.: In: SimProcTC: a powerful tool-chain for setup, distributed processing and analysis of OMNeT++ simulations (2009). http://www.iem.uni-due.de/~dreibh/omnetpp/ Google Scholar
  17. 17.
    Sroka, S., Karl, H.: Using Akaroa2 with OMNeT++. In: Proceedings of the Second International OMNeT++ Workshop, pp. 43–50 (2002) Google Scholar
  18. 18.
    Seggelmann, R., Rungeler, I., Tuxen, M., Rathgeb, E.P.: Parallelizing OMNeT++ simulations using Xgrid. In: Proceedings of the 2nd International Conference on Simulation Tools and Techniques, pp. 1–8 (2009) Google Scholar
  19. 19.
    Dreibholz, T.: Applicability of reliable server pooling for real-time distributed computing. Internet-draft (2008) Google Scholar
  20. 20.
  21. 21.
    Papoulis, A., Pillai, S.: Probability, Random Variables and Stochastic Processes. McGraw Hill, New York (2002) Google Scholar
  22. 22.
    Barnes, J., Koss, L.: The ergodic theory carnival. Math. Mag. 83(3), 180–190 (2010) MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Millman, E., Neville, S.: STARS: a framework for statistically rigorous simulation-based network research (2011). https://github.com/emillman/STARS
  24. 24.
    Bendat, J.S., Piersol, A.G.: Random Data: Analysis and Measurement Procedures, 3rd edn. Wiley-Interscience, New York (2000) MATHGoogle Scholar
  25. 25.
    Lemeshko, B., Lemeshko, S., Postovalov, S.: Comparative analysis of the power of goodness-of-fit tests for near competing hypotheses. 1. The verification of simple hypotheses. J. Appl. Ind. Math. 3(4), 462–475 (2009) MathSciNetCrossRefGoogle Scholar
  26. 26.
    Bendat, J.S., Piersol, A.G.: Random Data: Analysis and Measurement Procedures, 3rd edn. Wiley-Interscience, New York (2000) MATHGoogle Scholar
  27. 27.
    Paxson, V.: Fast approximation of self-similar network traffic. Lawrence Berkeley Laboratory (1995). http://books.google.co.in/books?id=7C1JHAAACAAJ
  28. 28.
    Ikeda, M., DeMarco, G., Yang, T., Barolli, L.: Performance analysis of an adhoc network for emergency and collaborative environments. Telecommun. Syst. 38, 133–146 (2008) CrossRefGoogle Scholar
  29. 29.
    Al-Maashri, A., Ould-Khaoua, M.: Performance analysis of MANET routing protocols in the presence of self-similar traffic. In: IEEE 31st Conference on Local Computer Networks Proceedings, pp. 801–807 (2006) Google Scholar
  30. 30.
    AhleHagh, H., Michalson, W., Finkel, D.: Statistical characteristics of wireless network traffic and its impact on ad hoc network performance. In: Applied Telecommunication Symposium Proceedings (2003) Google Scholar
  31. 31.
    Chakeres, I.D., Perkins, C.E.: Dynamic MANET on-demand routing protocol. ETF internet draft. draft-ietf-manet-dymo-12.txt (2008) Google Scholar
  32. 32.
    Millman, E.: Analyzing MANET jamming strategies (2011). http://dspace.library.uvic.ca:8080/handle/1828/3745
  33. 33.
    Koltsidas, G., Karapantazis, S., Theodoridis, G., Pavlidou, F.: A detailed study of dynamic manet on-demand multipath routing for mobile ad hoc networks. In: International Conference on Wireless and Optical Communications Networks (IFIP 2007), pp. 1–5 (2007) CrossRefGoogle Scholar
  34. 34.
    Koltsidas, G., Pavlidou, F.N., Kuladinithi, K., Tinini-Giel, A., Gorg, C.: Investigating the performance of a multipath DYMO protocol for ad-hoc networks. In: Proceedings of IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1–5 (2007) Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Deepali Arora
    • 1
  • Eamon Millman
    • 1
  • Stephen W. Neville
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of VictoriaVictoriaCanada

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