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On the parallel simulation of scale-free networks

Published:19 May 2013Publication History

ABSTRACT

Scale-free networks have received much attention in recent years due to their prevalence in many important applications such as social networks, biological systems, and the Internet. We consider the use of conservative parallel discrete event simulation techniques in network simulation applications involving scale-free networks. An analytical model is developed to study the parallelism available in simulations using a conservative time window synchronization algorithm. The performance of scale-free network simulations using two variants of the Chandy/Misra/Bryant synchronization algorithm are evaluated. These results demonstrate the importance of topology in the performance of synchronization protocols when developing parallel discrete event simulations involving scale-free networks, and highlight important challenges such as performance bottlenecks that must be addressed to achieve efficient parallel execution. These results suggest that new approaches to parallel simulation of scale-free networks may offer significant benefit.

References

  1. I. F. Akyildiz, L. Chen, S. R. Das, R. M. Fujimoto, and R. Serfozo. The effect of memory capacity on time warp performance. International Journal of Parallel and Distributed Computing, 18(4):411--422, August 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. B. C. Arnold. Pareto and generalized pareto distributions. 5:119--145, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  3. R. Ayani. A Parallel Simulation Scheme Based on Distances Between Objects. Technical report (Kungl. Tekniska högskolan. Dept. of Telecommunication Systems-Computer Systems). Royal Institute of Technology, Department of Telecommunication Systems-Computer Systems, 1988.Google ScholarGoogle Scholar
  4. A. L. Barabasi and R. Albert. Emergence of scaling in random networks. Science (New York, N.Y.), 286:509--512, Oct. 1999.Google ScholarGoogle ScholarCross RefCross Ref
  5. D. Bauer, G. Yaun, C. Carothers, M. Yuksel, and S. Kalyanaraman. Ross.net: optimistic parallel simulation framework for large-scale internet models. In Simulation Conference, 2003. Proceedings of the 2003 Winter, volume 1, pages 703 -- 711 Vol.1, dec. 2003.Google ScholarGoogle ScholarCross RefCross Ref
  6. K. Chandy and J. Misra. Distributed simulation: A case study in design and verification of distributed programs. Software Engineering, IEEE Transactions on, (5):440 -- 452, sept. 1979. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. Clauset, C. R. Shalizi, and M. E. J. Newman. Power-law distributions in empirical data. SIAM Rev., 51(4):661--703, Nov. 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. R. Cohen and S. Havlin. Scale-free networks are ultrasmall. Phys. Rev. Lett., 90, 2003.Google ScholarGoogle Scholar
  9. G. D'Angelo and S. Ferretti. Simulation of scale-free networks. In Proceedings of the 2nd International Conference on Simulation Tools and Techniques, Simutools '09, pages 20:1--20:10, ICST, Brussels, Belgium, Belgium, 2009. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering). Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. P. M. Dickens, D. M. Nicol, P. F. Reynolds, Jr., and J. M. Duva. Analysis of bounded time warp and comparison with yawns. ACM Trans. Model. Comput. Simul., 6(4):297--320, Oct. 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. Faloutsos, P. Faloutsos, and C. Faloutsos. On power-law relationships of the internet topology. SIGCOMM Comput. Commun. Rev., 29(4):251--262, Aug. 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. R. M. Fujimoto. Parallel discrete event simulation. Commun. ACM, 33(10):30--53, Oct. 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. R. Govindan and H. Tangmunarunkit. Heuristics for internet map discovery, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  14. A. Gupta, I. F. Akyildiz, and R. M. Fujimoto. Performance analysis of time warp with multiple homogeneous processors. IEEE Trans. Softw. Eng., 17(10):1013--1027, Oct. 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. D. Jefferson and H. Sowizral. Fast concurrent simulation using the time warp mechanism. 1982.Google ScholarGoogle Scholar
  16. H. Kitano. Systems biology: A brief overview. Science, 295(5560):1662--1664, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  17. D. Kumar. Simulating feedforward systems using a network of processors. In Proceedings of the 19th annual symposium on Simulation, ANSS '86, pages 127--144, Los Alamitos, CA, USA, 1986. IEEE Computer Society Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Y.-B. Lin. Parallelism analyzers for parallel discrete event simulation. ACM Trans. Model. Comput. Simul., 2(3):239--264, July 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. X. Liu and A. A. Chien. Realistic large-scale online network simulation, 2004.Google ScholarGoogle Scholar
  20. B. D. Lubachevsky. Efficient distributed event-driven simulations of multiple-loop networks. Commun. ACM, 32(1):111--123, Jan. 1989. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. M. E. J. Newman. Power laws, pareto distributions and zipf's law. Contemporary Physics, pages 323--351, 2005.Google ScholarGoogle Scholar
  22. D. M. Nicol. The cost of conservative synchronization in parallel discrete event simulations. J. ACM, 40(2):304--333, Apr. 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. D. Rao and A. Chernyakhovsky. Parallel simulation of the global epidemiology of avian influenza. In Simulation Conference, 2008. WSC 2008. Winter, pages 1583 --1591, dec. 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. G. F. Riley, M. H. Ammar, R. M. Fujimoto, A. Park, K. Perumalla, and D. Xu. A federated approach to distributed network simulation. ACM Trans. Model. Comput. Simul., 14(2):116--148, Apr. 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. G. Siganos, M. Faloutsos, P. Faloutsos, and C. Faloutsos. Power laws and the as-level internet topology. IEEE/ACM Transactions on Networking, 11(4):514--524, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. W. Small and J. D. Singer. Resort to arms: International and civil wars, 1816--1980. Notices of the American Mathematical Society, 1982.Google ScholarGoogle Scholar
  27. K. Soramaki, M. L. Bech, J. Arnold, R. J. Glass, and W. E. Beyeler. The topology of interbank payment flows. Physica A: Statistical Mechanics and its Applications, 379(1):317 -- 333, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  28. W. Su and C. L. Seitz. Variants of the chandy-misra-bryant distributed discrete-event simulation algorithm. Technical report, Pasadena, CA, USA, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. S. Thulasidasan and S. Eidenbenz. Accelerating traffic microsimulations: A parallel discrete-event queue-based approach for speed and scale. In Simulation Conference (WSC), Proceedings of the 2009 Winter, pages 2457--2466, dec. 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. X. F. Wang and G. Chen. Complex networks: small-world, scale-free and beyond. Circuits and Systems Magazine, IEEE, 3(1):6 -- 20, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  31. F. Wieland. Practical parallel simulation applied to aviation modeling. In Proceedings of the fifteenth workshop on Parallel and distributed simulation, PADS '01, pages 109--116, Washington, DC, USA, 2001. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. W. Willinger, D. Alderson, and J. C. Doyle. Mathematics and the internet: A source of enormous confusion and great potential, 2009.Google ScholarGoogle Scholar
  33. L. Zhang, X. Deng, J. Yu, and X. Wu. The degree and connectivity of internet's scale-free topology. CoRR, abs/1101.4285, 2011.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Conferences
      SIGSIM PADS '13: Proceedings of the 1st ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
      May 2013
      426 pages
      ISBN:9781450319201
      DOI:10.1145/2486092

      Copyright © 2013 ACM

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      Publication History

      • Published: 19 May 2013

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      SIGSIM PADS '13 Paper Acceptance Rate29of75submissions,39%Overall Acceptance Rate398of779submissions,51%

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