{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T20:19:19Z","timestamp":1769285959217,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In this research study, we investigate the ability of deep learning neural networks to provide a mapping between features of a parallel distributed discrete-event simulation (PDDES) system (software and hardware) to a time synchronization scheme to optimize speedup performance. We use deep belief networks (DBNs). DBNs, which due to their multiple layers with feature detectors at the lower layers and a supervised scheme at the higher layers, can provide nonlinear mappings. The mapping mechanism works by considering simulation constructs, hardware, and software intricacies such as simulation objects, concurrency, iterations, routines, and messaging rates with a particular importance level based on a cognitive approach. The result of the mapping is a synchronization scheme such as breathing time buckets, breathing time warp, and time warp to optimize speedup. The simulation-optimization technique outlined in this research study is unique. This new methodology could be realized within the current parallel and distributed simulation modeling systems to enhance performance.<\/jats:p>","DOI":"10.3390\/info11100467","type":"journal-article","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T09:04:12Z","timestamp":1601543052000},"page":"467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Design of Distributed Discrete-Event Simulation Systems Using Deep Belief Networks"],"prefix":"10.3390","volume":"11","author":[{"given":"Edwin","family":"Cortes","sequence":"first","affiliation":[{"name":"Institute of Simulation and Training, Orlando, FL 32816, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis","family":"Rabelo","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alfonso T.","family":"Sarmiento","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Universidad de La Sabana, Chia 250001, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8128-5356","authenticated-orcid":false,"given":"Edgar","family":"Gutierrez","sequence":"additional","affiliation":[{"name":"Center for Latin America Logistics Innovation, Bogota 110111, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Borshchev, A. (2013). The Big Book of Simulation Modeling: Multimethod Modeling with AnyLogic 6, AnyLogic North America.","DOI":"10.1002\/9781118762745.ch12"},{"key":"ref_2","unstructured":"Fujimoto, R. (2000). Parallel and Distributed Simulation, John Wiley & Sons. [1st ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/353735.353736","article-title":"Web-based Simulation: Revolution or Evolution?","volume":"10","author":"Page","year":"2000","journal-title":"ACM Trans. Modeling Comput. Simul. (TOMACS)"},{"key":"ref_4","first-page":"1","article-title":"Parallel and distributed Simulation in the cloud","volume":"3","author":"Fujimoto","year":"2010","journal-title":"SCS Modeling Simul. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1177\/0037549712450359","article-title":"Simulation on the Web with distributed models and intelligent agents","volume":"88","author":"Fur","year":"2012","journal-title":"Simulation"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.jpdc.2013.02.008","article-title":"Efficient autonomic cloud computing using online discrete event simulation","volume":"73","author":"Amoretti","year":"2013","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.simpat.2012.08.003","article-title":"Synchronization methods in Parallel and distributed discrete-event Simulation","volume":"30","author":"Jafer","year":"2013","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Padilla, J., Diallo, S., Barraco, A., Lynch, C., and Kavak, H. (2014, January 7\u201310). Cloud-based simulators: Making simulations accessible to non-experts and experts alike. Proceedings of the 2014 Winter Simulation Conference 2014, Savannah, GA, USA.","DOI":"10.1109\/WSC.2014.7020192"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2746232","article-title":"Efficient Parallel Discrete Event Simulation on cloud\/virtual machine platforms","volume":"26","author":"Yoginath","year":"2015","journal-title":"ACM Trans. Modeling Comput. Simul. (TOMACS)"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Padilla, J., Lynch, C., Diallo, S., Gore, R., Barraco, A., Kavak, H., and Jenkins, B. (2016, January 11\u201314). Using simulation games for teaching and learning discrete-event simulation. Proceedings of the 2016 Winter Simulation Conference (WSC), Washington, DC, USA.","DOI":"10.1109\/WSC.2016.7822368"},{"key":"ref_11","unstructured":"Liu, D., De Grande, R., and Boukerche, A. (2017, January 23\u221226). Towards the Design of an Interoperable Multi-cloud Distributed Simulation System. Proceedings of the 2017 Spring Simulation Multi-Conference\u2014Annual Simulation Symposium, Virginia Beach, VA, USA."},{"key":"ref_12","first-page":"159","article-title":"Towards a World Wide Web of Simulation","volume":"14","author":"Diallo","year":"2017","journal-title":"J. Def. Modeling Simul. Appl. Methodol. Technol."},{"key":"ref_13","unstructured":"Shchur, L., and Shchur, L. (2015, January 13\u221216). Parallel Discrete Event Simulation as a Paradigm for Large Scale Modeling Experiments. Proceedings of the XVII International Conference \u201cData Analytics and Management in Data Intensive Domains\u201d (DAMDID\/RCDL\u20192015), Obninsk, Russia."},{"key":"ref_14","unstructured":"Tang, Y., Perumalla, K., Fujimoto, R., Karimabadi, H., Driscoll, J., and Omelchenko, Y. (2005, January 1\u22123). Optimistic parallel discrete event simulations of physical systems using reverse computation. Proceedings of the Workshop on Principles of Advanced and Distributed Simulation (PADS\u201905), Monterey, CA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ziganurova, L., Novotny, M., and Shchur, L. (2015, January 6\u201310). Model for the evolution of the time profile in optimistic parallel discrete event simulations. Proceedings of the International Conference on Computer Simulation in Physics and Beyond, Moscow, Russia.","DOI":"10.1088\/1742-6596\/681\/1\/012047"},{"key":"ref_16","unstructured":"Steinman, J. (2005, January 1\u20133). The WarpIV Simulation Kernel. Proceedings of the Workshop on Principles of Advanced and Distributed Simulation (PADS 2005), Monterey, CA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Steinman, J. (1993, January 16\u201319). Breathing Time Warp. Proceedings of the 7th Workshop on Parallel and Distributed Simulation (PADS93), San Diego, CA, USA.","DOI":"10.1145\/158459.158473"},{"key":"ref_18","unstructured":"Rabelo, L., Bhide, S., and Gutierrez, E. (2018). Using Deep Learning to Configure Parallel Distributed Discrete-Event Simulators. Artificial Intelligence: Advances in Research and Applications, Nova Science Publishers. [1st ed.]."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/195291.182490","article-title":"Discrete-Event Simulation, and the Event Horizon","volume":"24","author":"Steinman","year":"1994","journal-title":"ACM SIGSIM Simul. Dig."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1145\/238793.238841","article-title":"Discrete-Event Simulation and the Event Horizon Part 2: Event List Management","volume":"26","author":"Steinman","year":"1996","journal-title":"ACM SIGSIM Simul. Dig."},{"key":"ref_21","unstructured":"Steinman, J., Nicol, D., Wilson, L., and Lee, C. (1995, January 14\u201316). Global Virtual Time and Distributed Synchronization. Proceedings of the 1995 Parallel and Distributed Simulation Conference, Lake Placid, NY, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_23","first-page":"1799","article-title":"Deep learning: Yesterday, today, and tomorrow","volume":"50","author":"Yu","year":"2013","journal-title":"J. Comput. Res. Dev."},{"key":"ref_24","unstructured":"Jiang, L., Zhou, Z., Leung, T., Li, T., and Fei-Fei, L. (2018, January 10\u201315). Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In Proceeding of the Thirty-Fifth International Conference on Machine Learning, Stockholmsm\u00e4ssan, Stockholm, Sweden."},{"key":"ref_25","first-page":"926","article-title":"A practical guide to training restricted Boltzmann machines","volume":"9","author":"Hinton","year":"2010","journal-title":"Momentum"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Sainath, T., Dahl, G., Ramabhadran, B., Hinton, G., and Picheny, M. (2011, January 22\u201327). Deep belief networks using discriminative features for phone recognition. Proceedings of the Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference, Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947494"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/TASL.2011.2109382","article-title":"Acoustic modeling using deep belief networks","volume":"20","author":"Mohamed","year":"2012","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1109\/TITS.2014.2311123","article-title":"Deep architecture for traffic flow prediction: Deep belief networks with multitask learning","volume":"15","author":"Huang","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/TASLP.2014.2303296","article-title":"Application of deep belief networks for natural language understanding","volume":"22","author":"Sarikaya","year":"2014","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1109\/JBHI.2017.2727218","article-title":"Deep belief networks for electroencephalography: A review of recent contributions and future outlooks","volume":"22","author":"Movahedi","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/978-3-642-21735-7_2","article-title":"Improved learning of Gaussian-Bernoulli restricted Boltzmann machines","volume":"Volume 6791","author":"Cho","year":"2011","journal-title":"Artificial Neural Networks and Machine Learning\u2014ICANN 2011"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_34","unstructured":"LeCun, Y., and Corinna, C. (2020, September 22). THE MNIST DATABASE of Handwritten Digits. Available online: http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"ref_35","unstructured":"Wu, M., and Chen, L. (2015, January 27\u201329). Image Recognition Based on Deep Learning. Proceedings of the 2015 Chinese Automation Congress (CAC), Wuhan, China."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"81","DOI":"10.4271\/2013-01-2090","article-title":"An architecture for monitoring and anomaly detection for space systems","volume":"6","author":"Cortes","year":"2013","journal-title":"SAE Int. J. Aerosp."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1016\/S0743-7315(02)00004-7","article-title":"ROSS: A high-performance, low memory modular time warp system","volume":"62","author":"Carothers","year":"2002","journal-title":"J. of Parallel Distrib. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mubarak, M., Carothers, C., Ross, R., and Carns, P. (2014, January 7\u201310). Using massively parallel Simulation for MPI collective communication modeling in extreme-scale networks. Proceedings of the 2014 Winter Simulation Conference, Savannah, GA, USA.","DOI":"10.1109\/WSC.2014.7020148"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Steinman, J., Lammers, C., and Valinski, M. (2009, January 13\u201316). A Proposed Open Cognitive Architecture Framework (OpenCAF). Proceedings of the 2009 Winter Simulation Conference, Austin, TX, USA.","DOI":"10.1109\/WSC.2009.5429287"},{"key":"ref_40","unstructured":"Steinman, J., Lammers, C., Valinski, M., and Steinman, W. (2020, September 30). External Modeling Framework and the OpenUTF. Report of WarpIV Technologies. Available online: http:\/\/www.warpiv.com\/Documents\/Papers\/EMF.pdf."},{"key":"ref_41","unstructured":"Plauger, P., Stepanov, A., Lee, M., and Musser, D. (2001). The C++ Standard Template Library, Prentice-Hall PTR, Prentice-Hall Inc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/CJECE.2003.1532511","article-title":"A new measure of software complexity based on cognitive weights","volume":"28","author":"Shao","year":"2003","journal-title":"Can. J. Electr. Comput. Eng."},{"key":"ref_43","first-page":"1","article-title":"A Complexity Measure based on Cognitive Weights","volume":"1","author":"Misra","year":"2006","journal-title":"Int. J. Theor. Appl. Comput. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kent, E., Hoops, S., and Mendes, P. (2012). Condor-COPASI: High-throughput computing for biochemical networks. BMC Syst. Biol., 6.","DOI":"10.1186\/1752-0509-6-91"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1175\/JTECH-D-12-00165.1","article-title":"A Hybrid MPI\u2013OpenMP Parallel Algorithm and Performance Analysis for an Ensemble Square Root Filter Designed for Multiscale Observations","volume":"30","author":"Wang","year":"2013","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1007\/s10898-016-0449-x","article-title":"Balancing global and local search in parallel efficient global optimization algorithms","volume":"67","author":"Zhan","year":"2017","journal-title":"J. Glob. Optim."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1007\/s10694-016-0645-8","article-title":"Increasing the Simulation Performance of Large-Scale Evacuations Using Parallel Computing Techniques Based on Domain Decomposition","volume":"53","author":"Grandison","year":"2017","journal-title":"Fire Technol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1007\/s13042-020-01096-5","article-title":"Recent advances in deep learning","volume":"11","author":"Wang","year":"2020","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.eng.2019.12.012","article-title":"Adversarial Attacks and Defenses in Deep Learning","volume":"6","author":"Ren","year":"2020","journal-title":"Engineering"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/10\/467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:15:46Z","timestamp":1760177746000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/10\/467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,1]]},"references-count":50,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["info11100467"],"URL":"https:\/\/doi.org\/10.3390\/info11100467","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,1]]}}}