Georg Kunz
Klaus Wehrle
James Gross
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Abstract
The technical evolution of wireless communication technology and the need for accurately modeling these increasingly complex systems causes a steady growth in the complexity of simulation models. At the same time, multi-core systems have become the de facto standard hardware platform. Unfortunately, wireless systems pose a particular challenge for parallel execution due to a tight coupling of network entities in space and time. Moreover, model developers are often domain experts with no in-depth understanding of parallel and distributed simulation. In combination, both aspects severely limit the performance and the efficiency of existing parallelization techniques. We address these challenges by presenting parallel expanded event simulation, a novel modeling paradigm that extends discrete events with durations that span a period in simulated time. The resulting expanded events form the basis for a conservative synchronization scheme that considers overlapping expanded events eligible for parallel processing. We then put these concepts into practice by implementing Horizon, a parallel expanded event simulation framework specifically tailored to the characteristics of multi-core systems. Our evaluation shows that Horizon achieves considerable speedups in synthetic as well as real-world simulation models and considerably outperforms the current state-of-the-art in distributed simulation.
- R. L. Bagrodia and M. Takai. 2002. Performance evaluation of conservative algorithms in parallel simulation languages. IEEE Transactions on Parallel and Distributed Systems 11, 4 (2002), 395--411. Google Scholar
Digital Library
- P. D. Barnes, J. M. Brase, T. W. Canale, M. M. Damante, M. A. Horsley, D. R. Jefferson, and R. A. Soltz. 2012. A benchmark model for parallel ns-3. In Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques. Google ScholarDigital Library
- R. Barr, H. Zygmunt, and R. van Renesse. 2004. JiST: Embedding simulation time into a virtual machine. In Proceedings of EuroSim Congress on Modelling and Simulation.Google Scholar
- L. Bononi, M. Di Felice, M. Bertini, and E. Croci. 2006. Parallel and distributed simulation of wireless vehicular ad hoc networks. In Proceedings of the 9th International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. Google ScholarDigital Library
- V. Cadambe and S. Jafar. 2008. Interference alignment and degrees of freedom of the k-user interference channel. IEEE Transactions on Information Theory 54, 8 (2008), 3425. Google ScholarDigital Library
- K. M. Chandy and J. Misra. 1979. Distributed simulation: A case study in design and verification of distributed programs. IEEE Transactions on Software Engineering SE-5, 5 (September 1979), 440--452. Google ScholarDigital Library
- G. Chen and B. K. Szymanski. 2005. DSIM: Scaling time warp to 1,033 processors. In Proceedings of the 37th Winter Simulation Conference. Google ScholarDigital Library
- L. Chen, Y. Lu, Y. Yao, S. Peng, and L. Wu. 2011. A well-balanced time warp system on multi-core environments. In Proceedings of IEEE Workshop on Principles of Advanced and Distributed Simulation (PADS). Google ScholarDigital Library
- J. Cowie, A. Ogielski, and D. M. Nicol. 2002. The SSFNet Network Simulator. Software on-line: http://www.ssfnet.org/homePage.html. (2002).Google Scholar
- J. H. Cowie, D. M. Nicol, and A. T. Ogielski. 1999. Modeling the global internet. Computing in Science & Engineering 1, 1 (Jan. 1999), 42--50. DOI:http://dx.doi.org/10.1109/5992.743621 Google ScholarDigital Library
- G. D’Angelo, S. Ferretti, and M. Marzolla. 2012. Time warp on the go. In Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques. Google ScholarDigital Library
- R. M. Fujimoto. 1990a. Parallel discrete event simulation. Communications of the ACM 33, 10 (1990), 30--53. Google ScholarDigital Library
- R. M. Fujimoto. 1990b. Performance of time warp under synthetic workloads. In Proceedings of the SCS Multiconference on Distributed Simulation.Google Scholar
- R. M. Fujimoto. 1999. Exploiting temporal uncertainty in parallel and distributed simulations. In Proceedings of the 13th Workshop on Parallel and Distributed Simulation. Google ScholarDigital Library
- R. M. Fujimoto, K. S. Perumalla, A. Park, H. Wu, M. H. Ammar, and G. F. Riley. 2003. Large-scale network simulation: How big? How fast? In Proceedings of 11th International IEEE Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems.Google Scholar
- D. Gesbert, M. Shafi, D. Shiu, P. J. Smith, and A. Naguib. 2003. From theory to practice: An overview of mimo space-time coded wireless systems. IEEE Journal on Selected Areas in Communications 21, 3 (April 2003), 281--302. Google ScholarDigital Library
- D. Halperin, T. Anderson, and D. Wetherall. 2008. Taking the sting out of carrier sense: Interference cancellation for wireless LANs. In Proceedings of the 14th ACM Internatinoal Conference on Mobile Computing and Networking. Google ScholarDigital Library
- T. R. Henderson, S. Roy, S. Floyd, and G. F. Riley. 2006. ns-3 project goals. In Proceedings of the 2006 Workshop on ns-2: The IP Network Simulator. Google ScholarDigital Library
- M. Hybinette and R. M. Fujimoto. 1997. Cloning: A novel method for interactive parallel simulation. In Proceedings of the 29th Winter Simulation Conference. Google ScholarDigital Library
- M. Hybinette and R. M. Fujimoto. 2001. Cloning parallel simulations. ACM Transactions on Modeling and Computer Simulation 11, 4 (Oct. 2001), 378--407. Google ScholarDigital Library
- D. Jagtap, N. Abu-Ghazaleh, and D. Ponomarev. 2012. Optimization of parallel discrete event simulator for multi-core systems. In Proceedings of the IEEE 26th Internatiaonal Parallel and Distributed Processing Symposium. Google ScholarDigital Library
- I. Koffman, V. Roman, and R. Technol. 2002. Broadband wireless access solutions based on OFDM access in IEEE 802.16. IEEE Communications Magazine 40, 4 (2002), 96--103. Google ScholarDigital Library
- G. Kunz. 2013. Exploiting Multi-core Systems for Parallel Network Simulation. Shaker Verlag. PhD Dissertation.Google Scholar
- G. Kunz, O. Landsiedel, J. Gross, S. Götz, F. Naghibi, and K. Wehrle. 2010. Expanding the event horizon in parallelized network simulations. In Proceedings of the 18th International IEEE Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems. Google ScholarDigital Library
- G. Kunz, O. Landsiedel, and K. Wehrle. 2009. Poster abstract: Horizon - exploiting timing information for parallel network simulation. In Proceedings of the 17th International IEEE Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems.Google Scholar
- G. Kunz, M. Stoffers, J. Gross, and K. Wehrle. 2011. Runtime efficient event scheduling in muti-threaded network simulation. In Proceedings of the 4th International Workshop on OMNeT++. Google ScholarDigital Library
- G. Kunz, M. Stoffers, J. Gross, and K. Wehrle. 2012. Know thy simulation model: Analyzing event interactions for probabilistic synchronization in parallel simulations. In Proceedings of the 5th International ICST Conf. on Simulation Tools and Techniques. Google ScholarDigital Library
- Z. Li, X. Li, L. Wang, and W. Cai. 2014. Hierarchical resource management for enhancing performance of large-scale simulations on data centers. In Proceedings of the 2nd Conference on Principles of Advanced Discrete Simulation. Google ScholarDigital Library
- J. Liu. 2009. Parallel Discrete-Event Simulation. John Wiley & Sons.Google Scholar
- J. Liu, Y. Li, and Y. He. 2009. A large-scale real-time network simulation study using PRIME. In Proceedings of the 2009 Winter Simulation Conference. Google ScholarDigital Library
- J. Liu and D. M. Nicol. 2001. Learning not to share. In Proceedings 15th Workshop on Parallel and Distributed Simulation. Google ScholarDigital Library
- J. Liu and D. M. Nicol. 2002. Lookahead revisited in wireless network simulations. In Proceedings of the 16th Workshop on Parallel and Distributed Simulation. Google ScholarDigital Library
- M. Loper and R. M. Fujimoto. 2000. Pre-sampling as an approach for exploiting temporal uncertainty. In Proceedings of the 14th Workshop on Parallel and Distributed Simulation. Google ScholarDigital Library
- M. Loper and R. M. Fujimoto. 2004. A case study in exploiting temporal uncertainty in parallel simulations. In Proceedings of the 2004 International Conference on Parallel Processing. Google ScholarDigital Library
- B. D. Lubachevsky. 1988. Efficient distributed event driven simulations of multiple-loop networks. In Proceedings of the ACM SIGMETRICS Conferenece on Measurement and Modeling of Computer Systems. Google ScholarDigital Library
- A. Markopoulou, F. Tobagi, and M. Karam. 2006. Loss and delay measurements of internet backbones. Computer Communications 29, 10 (June 2006), 1590--1604. Google ScholarDigital Library
- R. A. Meyer and R. L. Bagrodia. 1998. Improving lookahead in parallel wireless network simulation. In Proceedings of the 6th International IEEE Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems. Google ScholarDigital Library
- R. A. Meyer and R. L. Bagrodia. 1999. Path lookahead: A data flow view of PDES models. In Proceedings of the 13th Workshop on Parallel and Distributed Simulation. Google ScholarDigital Library
- F. Naghibi and J. Gross. 2010. How bad is interference in IEEE 802.16e systems? In Proceedings of the 16th European Wireless Conference.Google Scholar
- D. M. Nicol. 1996. Principles of conservative parallel simulation. In Proceedings of the 28th Winter Simulation Conference. Google ScholarDigital Library
- D. M. Nicol. 2003. Darpa Network Modeling and Simulation (NMS) Baseline Network Topology. Last accessed November 28, 2014. (2003). http://www.ssfnet.org/Exchange/gallery/baseline/index.html.Google Scholar
- D. M. Nicol and J. Liu. 2002. Composite synchronization in parallel discrete-event simulation. IEEE Transactions on Parallel Distributed Systems 13, 5 (May 2002), 433--446. Google ScholarDigital Library
- K. S. Perumalla. 2006. Parallel and distributed simulation: Traditional techniques and recent advances. In Proceedings of the 38th Winter Simulation Conference. Google ScholarDigital Library
- P. Peschlow, T. Honecker, and P. Martini. 2007. A flexible dynamic partitioning algorithm for optimistic distributed simulation. In Proceedings of the 21st International Workshop on Principles of Advanced and Distributed Simulation. Google ScholarDigital Library
- P. Peschlow, P. Martini, and J. Liu. 2008. Interval branching. In Proc. of the 22nd Workshop on Principles of Advanced and Distributed Simulation. Google ScholarDigital Library
- P. Peschlow, A. Voss, and P. Martini. 2009. Good news for parallel wireless network simulations. In Proceedings of the 12th International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. Google ScholarDigital Library
- O. Punal, H. Escudero, and J. Gross. 2011. Performance comparison of loading algorithms for 80 MHz IEEE 802.11 WLANs. In Proceedings of the 73rd IEEE Vehicular Technology Conference.Google Scholar
- G. F. Riley. 2003. The georgia tech network simulator. In Proceedings of the ACM SIGCOMM Workshop on Models, Methods and Tools for Reproducible Network Research. Google ScholarDigital Library
- G. Seguin. 2009. Multi-core Parallelism for ns-3 Simulator. Technical Report. INRIA Sophia-Antipolis.Google Scholar
- A. Sekercioglu, A. Varga, and G. Egan. 2003. Parallel simulation made easy with OMNeT++. In Proceedings of the European Simulation Symposium.Google Scholar
- M. Stoffers, R. Bettermann, J. Gross, and K. Wehrle. 2014a. Enabling distributed simulation of OMNeT++ INET models. In Proceedings of the 1st OMNeT++ Community Summit.Google Scholar
- M. Stoffers, S. Schmerling, G. Kunz, J. Gross, and K. Wehrle. 2014b. Large-scale network simulation: Leveraging the strengths of modern smp-based compute clusters. In Proceedings of the 7th Internatiaonal ICST Conference on Simulation Tools and Techniques (SIMUTools’14). Google ScholarDigital Library
- A. Varga. 2001. The OMNeT++ discrete event simulation system. In Proceedings of the 15th European Simulation Multiconference.Google Scholar
- A. Varga. 2014. OMNeT++ Website. Retrieved November 28, 2014 from http://www.omnetpp.org.Google Scholar
- R. Vitali, A. Pellegrini, and F. Quaglia. 2012. Towards symmetric multi-threaded optimistic simulation kernels. In Proceedings of IEEE Workshop on Principles of Advanced and Distributed Simulation (PADS). Google ScholarDigital Library
- C.-X. Wang, M. Pätzold, and Q. Yao. 2007. Stochastic modeling and simulation of frequency-correlated wideband fading channels. IEEE Transactions on Vehicular Technology 56, 3 (2007).Google ScholarCross Ref
- S. B. Yoginath and K. S. Perumalla. 2013. Optimized hypervisor scheduler for parallel discrete event simulations on virtual machine platforms. In Proceedings of the 6th International ICST Conf. on Simulation Tools and Techniques. Google ScholarDigital Library
Index Terms
- Parallel Expanded Event Simulation of Tightly Coupled Systems
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