DEXSim: an experimental environment for distributed execution of replicated simulators using a concept of single simulation multiple scenarios

This paper presents an efficient and scalable experimental environment for distributed execution of replicated simulators. By taking a performance-centered approach, the proposed technique makes the best use of distributed hardware resources for faster data collection. Accordingly, the primary contribution of this work is to describe how the environment improves scalability and utilizes distributed hardware resources efficiently. To do this, we suggest a new concept of single simulation multiple scenarios and propose a distributed execution simulation framework regarding the following three aspects: (1) layered architecture model design; (2) protocol definitions interacting with them; and (3) framework implementation. The proposed model architecture and protocol definitions guarantee a straightforward structural scalability and an efficient load-balanced utilization between hardware resources. Moreover, the framework operates simulation execution automatically without users’ extra work. In order to prove the efficiency of the proposed framework, we performed three extensive experiments with different models, that is, different systems. The experimental results show that simulation performance increases proportionally with the number of hardware resources, minimizing the overhead of the proposed framework’s utilization.

[1]  Massimiliano Rak,et al.  mJADES: Concurrent Simulation in the Cloud , 2012, 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems.

[2]  Mehrdad Ehsani,et al.  A Matlab-based modeling and simulation package for electric and hybrid electric vehicle design , 1999 .

[3]  Russ Miles,et al.  Learning UML 2.0 , 2006 .

[4]  Pawel Gepner,et al.  Multi-Core Processors: New Way to Achieve High System Performance , 2006, PARELEC.

[5]  Massimiliano Rak,et al.  Process-oriented Discrete-event Simulation in Java with Continuations - Quantitative Performance Evaluation , 2012, SIMULTECH.

[6]  Dana Petcu,et al.  Towards a Cross Platform Cloud API - Components for Cloud Federation , 2011, CLOSER.

[7]  George Kurian,et al.  Graphite: A distributed parallel simulator for multicores , 2010, HPCA - 16 2010 The Sixteenth International Symposium on High-Performance Computer Architecture.

[8]  M.C. Yuang,et al.  Survey of protocol verification techniques based on finite state machine models , 1988, [1988] Proceedings. Computer Networking Symposium.

[9]  Aamer Jaleel,et al.  DRAMsim: a memory system simulator , 2005, CARN.

[10]  Kyung-Min Seo,et al.  Measurement of Effectiveness for an Anti-torpedo Combat System Using a Discrete Event Systems Specification-based Underwater Warfare Simulator , 2011 .

[11]  Elizabeth Chang,et al.  Goal-Directed Grid-Enabled Computing for Legacy Simulations , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

[12]  Adelinde M. Uhrmacher,et al.  Experiments with Single Core, Multi-core, and GPU Based Computation of Cellular Automata , 2009, 2009 First International Conference on Advances in System Simulation.

[13]  Gabriel A. Wainer,et al.  Multicore acceleration of Discrete Event System Specification systems , 2012, Simul..

[14]  Carl A. Sunshine,et al.  Survey of protocol definition and verification techniques , 1978, CCRV.

[15]  Catherine C. McGeoch Analyzing algorithms by simulation: variance reduction techniques and simulation speedups , 1992, CSUR.

[16]  Reuven Y. Rubinstein,et al.  Modern simulation and modeling , 1998 .

[17]  Cho-Yu Jason Chiang,et al.  On Optimal Deadlock Detection Scheduling , 2006, IEEE Transactions on Computers.

[18]  Philip Heidelberger Statistical analysis of parallel simulations , 1986, WSC '86.

[19]  Jack P. C. Kleijnen,et al.  A Java-based simulation manager for Web-based simulation , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[20]  Christian Lebiere,et al.  Modeling Synthetic Opponents in MOUT Training Simulations Using the ACT-R Cognitive Architecture , 2002 .

[21]  Tag Gon Kim,et al.  New insight into doctrine via simulation interoperation of heterogeneous levels of models in battle experimentation , 2012, Simul..

[22]  Gérard Lachapelle,et al.  Performance analysis of a stand-alone high-sensitivity receiver , 2002 .

[23]  Simon J. E. Taylor,et al.  Sakergrid: Simulation experimentation using grid enabled simulation software , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[24]  Stephen John Turner,et al.  Distributed computing and modeling & simulation: Speeding up simulations and creating large models , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[25]  Hans van Vliet,et al.  Software engineering - principles and practice , 1993 .

[26]  Adelinde M. Uhrmacher,et al.  A flexible and scalable experimentation layer for JAMES II , 2008, Online World Conference on Soft Computing in Industrial Applications.

[27]  Stephen John Turner,et al.  Flexible State Update Mechanism for Large-Scale Distributed Wargame Simulations , 2007, Simul..

[28]  Morris R. Driels,et al.  Weaponeering: Conventional Weapon System Effectiveness , 2013 .

[29]  Tina M. Higgins,et al.  Simulation-Based Undersea Warfare Assessment , 2002 .

[30]  Bernd Page,et al.  A framework for distributed simulation optimization , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[31]  Ihsan Sabuncuoglu,et al.  Experimental investigation of iterative simulation-based scheduling in a dynamic and stochastic job shop , 2001 .

[32]  Luciano Bononi,et al.  Concurrent replication of parallel and distributed simulations , 2005, Workshop on Principles of Advanced and Distributed Simulation (PADS'05).

[33]  Tag Gon Kim,et al.  Battle Experiments of Naval Air Defense with Discrete Event System-based Mission-level Modeling and Simulations , 2011 .

[34]  Su-Youn Hong,et al.  DEVSim++ Toolset for Defense Modeling and Simulation and Interoperation , 2011 .

[35]  John W. Fowler,et al.  Grand Challenges in Modeling and Simulation of Complex Manufacturing Systems , 2004, Simul..

[36]  Barry L. Nelson,et al.  Efficient generation of cycle time‐throughput curves through simulation and metamodeling , 2005, WSC '05.

[37]  Adelinde M. Uhrmacher,et al.  A flexible and scalable experimentation layer , 2008, 2008 Winter Simulation Conference.

[38]  R. Michael Hord Parallel supercomputing in SIMD architectures , 1990 .

[39]  Luciano Bononi,et al.  ARTÌS: A Parallel and Distributed Simulation Middleware for Performance Evaluation , 2004, ISCIS.

[40]  Lalit K Piplani Systems Acquisition Manager's Guide for the Use of Models and Simulations , 1994 .

[41]  H. Van Dyke Parunak,et al.  Representing Agent Interaction Protocols in UML , 2000, AOSE.