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.
- 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 ScholarDigital Library
- B. C. Arnold. Pareto and generalized pareto distributions. 5:119--145, 2008.Google ScholarCross Ref
- 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 Scholar
- A. L. Barabasi and R. Albert. Emergence of scaling in random networks. Science (New York, N.Y.), 286:509--512, Oct. 1999.Google ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- R. Cohen and S. Havlin. Scale-free networks are ultrasmall. Phys. Rev. Lett., 90, 2003.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- R. M. Fujimoto. Parallel discrete event simulation. Commun. ACM, 33(10):30--53, Oct. 1990. Google ScholarDigital Library
- R. Govindan and H. Tangmunarunkit. Heuristics for internet map discovery, 2000.Google ScholarCross Ref
- 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 ScholarDigital Library
- D. Jefferson and H. Sowizral. Fast concurrent simulation using the time warp mechanism. 1982.Google Scholar
- H. Kitano. Systems biology: A brief overview. Science, 295(5560):1662--1664, 2002.Google ScholarCross Ref
- 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 ScholarDigital Library
- Y.-B. Lin. Parallelism analyzers for parallel discrete event simulation. ACM Trans. Model. Comput. Simul., 2(3):239--264, July 1992. Google ScholarDigital Library
- X. Liu and A. A. Chien. Realistic large-scale online network simulation, 2004.Google Scholar
- B. D. Lubachevsky. Efficient distributed event-driven simulations of multiple-loop networks. Commun. ACM, 32(1):111--123, Jan. 1989. Google ScholarDigital Library
- M. E. J. Newman. Power laws, pareto distributions and zipf's law. Contemporary Physics, pages 323--351, 2005.Google Scholar
- D. M. Nicol. The cost of conservative synchronization in parallel discrete event simulations. J. ACM, 40(2):304--333, Apr. 1993. Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- W. Small and J. D. Singer. Resort to arms: International and civil wars, 1816--1980. Notices of the American Mathematical Society, 1982.Google Scholar
- 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 ScholarCross Ref
- W. Su and C. L. Seitz. Variants of the chandy-misra-bryant distributed discrete-event simulation algorithm. Technical report, Pasadena, CA, USA, 1988. Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- W. Willinger, D. Alderson, and J. C. Doyle. Mathematics and the internet: A source of enormous confusion and great potential, 2009.Google Scholar
- 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 Scholar
Index Terms
- On the parallel simulation of scale-free networks
Recommendations
Link Partitioning in Parallel Simulation of Scale-Free Networks
DS-RT '16: Proceedings of the 20th International Symposium on Distributed Simulation and Real-Time ApplicationsIt has been observed that many networks arising in practice have skewed node degree distributions. Scale-free networks are one well-known class of such networks. Achieving efficient parallel simulation of scale-free networks is challenging because large-...
Parallelism in sequential multiprocessor simulation models: a case study
The design and analysis of multiprocessor simulation models represents a complex and computationally demanding application that is a candidate for parallel simulation. This paper examines the application of conservative parallel discrete event ...
Parallel shared-memory simulator performance for large ATM networks
A performance comparison between an optimistic and a conservative parallel simulation kernel is presented. Performance of the parallel kernels is also compared to a central-event-list sequential kernel. A spectrum of ATM network and traffic scenarios ...
Comments