Next Article in Journal
Prevention of Unintended Appearance in Photos Based on Human Behavior Analysis
Next Article in Special Issue
Multiple Resolution Modeling: A Particular Case of Distributed Simulation
Previous Article in Journal
A Systematic Review of the Application of Maturity Models in Universities
Previous Article in Special Issue
Distributed Simulation Using Agents for the Internet of Things and the Factory of the Future
Open AccessArticle

Design of Distributed Discrete-Event Simulation Systems Using Deep Belief Networks

1
Institute of Simulation and Training, Orlando, FL 32816, USA
2
Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
3
Faculty of Engineering, Universidad de La Sabana, Chia 250001, Colombia
4
Center for Latin America Logistics Innovation, Bogota 110111, Colombia
*
Author to whom correspondence should be addressed.
Information 2020, 11(10), 467; https://doi.org/10.3390/info11100467
Received: 2 September 2020 / Revised: 26 September 2020 / Accepted: 29 September 2020 / Published: 1 October 2020
(This article belongs to the Special Issue Distributed Simulation 2020)
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. View Full-Text
Keywords: parallel distributed discrete-event simulation; deep learning; deep belief networks; breathing time buckets; breathing time warp; time warp parallel distributed discrete-event simulation; deep learning; deep belief networks; breathing time buckets; breathing time warp; time warp
Show Figures

Figure 1

MDPI and ACS Style

Cortes, E.; Rabelo, L.; Sarmiento, A.T.; Gutierrez, E. Design of Distributed Discrete-Event Simulation Systems Using Deep Belief Networks. Information 2020, 11, 467.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop