Special Issue "Distributed Simulation 2020"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (31 August 2020).

Special Issue Editor

Dr. Luis Rabelo
Website
Guest Editor
Department of Industrial Engineering & Management Systems, University of Central Florida, USA
Interests: distributed simulation, artificial intelligence, optimization
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Distributed simulation is the execution of simulation models on distributed computer systems, and can also be just a single simulation model (e.g., just one model with an irregular and data-dependent nature). There are several advantages of distributed simulations:

  1. Increased speed (i.e., reduced execution time) due to the potential parallelism;
  2. Increased size of the simulation program and/or data generation;
  3. Heterogeneous computing;
  4. Fault tolerance;
  5. Usage of unique resources and multi-enterprise/geographical distributed locations; and
  6. Multi-enterprise simulation/intellectual property.

Several computer systems can handle distributed simulations. These computer systems can be tightly coupled or loosely coupled. The tightly coupled system is a multiprocessor computer system that communicates through shared memory modules. A loosely coupled system is a multiprocessor computer system where each processor has its own local memory and the processors communicate via messages. One important point is the merge of the paradigms of high performance computing and high-throughput computing and the resulting increasingly sophisticated distributed simulations.

This Special Issue particularly welcomes, but is not limited to, contributions that explore advanced computer architecture for distributed simulation. In addition, the Issue will include sophisticated applications in the areas of big data, the Internet of Things, artificial intelligence, and massive agent-based systems. Contributors that are interested in presenting their work are invited to submit an extended abstract to the Guest Editor ([email protected]).

Dr. Luis Rabelo

Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Parallel Distributed Simulation
  • High Performance Computing
  • Distributed Systems Simulation
  • Agent-based Simulation
  • Deep Learning Implementations
  • Time Management
  • Live, Virtual, Constructive
  • Big Data
  • Internet of Things
  • High-Level Architecture (HLA)

Published Papers (4 papers)

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Research

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Open AccessArticle
Design of Distributed Discrete-Event Simulation Systems Using Deep Belief Networks
Information 2020, 11(10), 467; https://doi.org/10.3390/info11100467 - 01 Oct 2020
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Distributed Simulation 2020)
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Open AccessArticle
Distributed Simulation Using Agents for the Internet of Things and the Factory of the Future
Information 2020, 11(10), 458; https://doi.org/10.3390/info11100458 - 24 Sep 2020
Abstract
The adoption of the Internet of Things (IoT) and its related technologies has transformed the manufacturing industry and has significantly changed the traditional linear manufacturing supply chains into dynamic and interconnected systems. However, the lack of an approach to assess the economic feasibility [...] Read more.
The adoption of the Internet of Things (IoT) and its related technologies has transformed the manufacturing industry and has significantly changed the traditional linear manufacturing supply chains into dynamic and interconnected systems. However, the lack of an approach to assess the economic feasibility and return uncertainties of an IoT system implementation, is blamed as the culprit for hindering its adoption rate. Using two distinctive case studies, this research investigates the use of distributed simulation of agent-based model (ABM) to address such gap in the literature. The first involves the economic feasibility of an IoT implementation in a very large retail warehouse facility, while the second case study proposes a framework able to generate and assess ideal or near-ideal manufacturing configurations and capabilities, and in establishing appropriate information messaging protocols between the various system components by using ABM in distributed simulation. Full article
(This article belongs to the Special Issue Distributed Simulation 2020)
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Review

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Open AccessReview
A Systematic Review of the Multi-Resolution Modeling (MRM) for Integration of Live, Virtual, and Constructive Systems
Information 2020, 11(10), 480; https://doi.org/10.3390/info11100480 - 14 Oct 2020
Abstract
Multi-Resolution Modeling (MRM) is a modeling technology that creates a model that expresses the same phenomenon at more than two different resolutions. Since the advent of distributed simulation systems, the MRM study began in the military field, where the modeling and simulation (M&S) [...] Read more.
Multi-Resolution Modeling (MRM) is a modeling technology that creates a model that expresses the same phenomenon at more than two different resolutions. Since the advent of distributed simulation systems, the MRM study began in the military field, where the modeling and simulation (M&S) was most actively developed and was recognized as an essential area in the integrated system of live, virtual and constructive (LVC) simulations. Models of the various resolutions had already been built based on the characteristics and training purposes of each weapon system, and the interoperability of these models was a primary task in the M&S community. In this study, we report the results from a systematic review of the MRM to address two questions: (1) What research has been done towards the MRM for integrating LVC system? (2) What are the research and technology challenges for the MRM implementation in the future? In total, 22 papers have been identified and studied in this review by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The structures of the significant 20 MRM implementation experiments in those papers are analyzed based on the relationship between the MRM and integrating the LVC system being implemented in the military. We explored the various issues related to the MRM. Then, we discussed the direction in which the MRM should move forward, comparing civilian modeling techniques with those being used in the military. Full article
(This article belongs to the Special Issue Distributed Simulation 2020)
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Open AccessReview
Multiple Resolution Modeling: A Particular Case of Distributed Simulation
Information 2020, 11(10), 469; https://doi.org/10.3390/info11100469 - 02 Oct 2020
Abstract
Multiple resolution modeling (MRM) is the future of distributed simulation. This article describes different definitions and notions related to MRM. MRM is a relatively new research area, and there is a demand for simulator integration from a modeling complexity point of view. This [...] Read more.
Multiple resolution modeling (MRM) is the future of distributed simulation. This article describes different definitions and notions related to MRM. MRM is a relatively new research area, and there is a demand for simulator integration from a modeling complexity point of view. This article also analyzes a taxonomy based on the experience of the researchers in detail. Finally, an example that uses the high-level architecture (HLA) is explained to illustrate the above definitions and, in particular, to look at the problems that are common to these distributed simulation configurations. The steps required to build an MRM distributed simulation system are introduced. The conclusions describe the lessons learned for this unique form of distributed simulation. Full article
(This article belongs to the Special Issue Distributed Simulation 2020)
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