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Thematische Bibliographien / Distributed systems simulation / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Distributed systems simulation“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 2. Februar 2022

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1

Karatza, Helen D., und Georgios K. Theodoropoulos. „Distributed Systems Simulation“. Simulation Modelling Practice and Theory 14, Nr. 6 (August 2006): 677–78. http://dx.doi.org/10.1016/j.simpat.2005.10.001.

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2

Vasileva, Svetlana, und Aleksandar Milev. „Simulation Studies of Distributed Two-phase Locking in Distributed Database Management Systems“. Information Technologies and Control 13, Nr. 1-2 (01.06.2015): 46–55. http://dx.doi.org/10.1515/itc-2016-0010.

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Abstract This paper considers algorithms simulating the implementation of distributed two-phase locking (2PL) protocols in distributed database systems and simulation results. It describes specifically the simulations of two-version 2PL and 2PL with integrated timestamp ordering mechanism. Integrated modelling algorithms for deadlock avoiding are suggested in the paper: twoversion architecture of database and timestamp ordering strategy “wait-die”. The results of the simulations of these two variants of the 2PL method at different scales of the networks for data transmission and at different intensities of inflow transactions are also presented. Modelling algorithms are developed by means of the system for simulation modelling GPSS World Personal Version.

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3

Reed, Morton W. „Distributed simulation using distributed control systems“. ACM SIGSIM Simulation Digest 20, Nr. 4 (April 1990): 143–51. http://dx.doi.org/10.1145/99637.99656.

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4

Logan, B., und G. Theodoropoulos. „The distributed simulation of multiagent systems“. Proceedings of the IEEE 89, Nr. 2 (2001): 174–85. http://dx.doi.org/10.1109/5.910853.

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5

Bley, Helmut, Claas Christian Wuttke und Wolfgang Massberg. „Distributed Simulation Applied to Production Systems“. CIRP Annals 46, Nr. 1 (1997): 361–64. http://dx.doi.org/10.1016/s0007-8506(07)60843-9.

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6

Righter, R., und J. C. Walrand. „Distributed simulation of discrete event systems“. Proceedings of the IEEE 77, Nr. 1 (1989): 99–113. http://dx.doi.org/10.1109/5.21073.

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7

Kumar, D. „Systems with low distributed simulation overhead“. IEEE Transactions on Parallel and Distributed Systems 3, Nr. 2 (März 1992): 155–65. http://dx.doi.org/10.1109/71.127257.

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8

Kumar, Devendra. „Efficient distributed simulation of acyclic systems“. Information Sciences 66, Nr. 1-2 (Dezember 1992): 167–90. http://dx.doi.org/10.1016/0020-0255(92)90092-m.

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9

INUKAI, Toshihiro, Hironori HIBINO und Yoshiro FUKUDA. „Efficient Design and Evaluation for Manufacturing Systems Using Distributed Real Simulation(Manufacturing systems and Scheduling)“. Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2005.2 (2005): 397–402. http://dx.doi.org/10.1299/jsmelem.2005.2.397.

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10

Çakmak, Hüseyin, Anselm Erdmann, Michael Kyesswa, Uwe Kühnapfel und Veit Hagenmeyer. „A new distributed co-simulation architecture for multi-physics based energy systems integration“. at - Automatisierungstechnik 67, Nr. 11 (26.11.2019): 972–83. http://dx.doi.org/10.1515/auto-2019-0081.

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Abstract Simulating energy systems integration scenarios enables a comprehensive consideration of interdependencies between multimodal energy grids. It is an important part of the planning for the redesign of the current energy system infrastructure, which is essential for the foreseen drastic reduction of carbon emissions. In contrast to the complex implementation of monolithic simulation architectures, emerging distributed co-simulation technologies enable the combination of several existing single-domain simulations into one large energy systems integration simulation. Accompanying disadvantages of coupling simulators have to be minimized by an appropriate co-simulation architecture. Hence, in the present paper, a new simulation architecture for energy systems integration co-simulation is introduced, which enables an easy and fast handling of the therefore required simulation setup. The performance of the new distributed co-simulation architecture for energy systems integration is shown by a campus grid scenario with a focus on the effects of power to gas and the reversal process onto the electricity grid. The implemented control strategy enables a successful co-simulation of electrolysis coupled with photovoltaics, a hydrogen storage with a combined heat and power plant and a variable power consumption.

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11

Krus, Petter. „Distributed Modelling for Simulation of Pneumatic Systems“. Proceedings of the JFPS International Symposium on Fluid Power 1999, Nr. 4 (1999): 443–52. http://dx.doi.org/10.5739/isfp.1999.443.

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12

Kato, K., und H. R. Fudeh. „Performance simulation of distributed energy management systems“. IEEE Transactions on Power Systems 7, Nr. 2 (Mai 1992): 820–27. http://dx.doi.org/10.1109/59.141791.

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13

Graham, James H., Adel S. Elmaghraby, Irfan Karachiwala und Hussam Soliman. „A visual environment for distributed simulation systems“. ACM SIGSIM Simulation Digest 25, Nr. 3 (Januar 1996): 13–22. http://dx.doi.org/10.1145/242745.1108993.

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14

Sergienko, I. V., A. V. Gladkii und V. V. Skopetskii. „Simulation of distributed systems with nonselfadjoint operator“. Cybernetics and Systems Analysis 30, Nr. 6 (November 1994): 830–38. http://dx.doi.org/10.1007/bf02366441.

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15

Rizvi, S. S. „A Logical Process Simulation Model for Conservative Distributed Simulation Systems“. International Journal of Simulation Modelling 12, Nr. 2 (15.06.2013): 69–81. http://dx.doi.org/10.2507/ijsimm12(2)1.224.

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16

Sikora, Andrzej, und Ewa Niewiadomska-Szynkiewicz. „A Federated Approach to Parallel and Distributed Simulation of Complex Systems“. International Journal of Applied Mathematics and Computer Science 17, Nr. 1 (01.03.2007): 99–106. http://dx.doi.org/10.2478/v10006-007-0009-0.

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A Federated Approach to Parallel and Distributed Simulation of Complex SystemsThe paper describes a Java-based framework called ASimJava that can be used to develop parallel and distributed simulators of complex real-life systems. Some important issues associated with the implementation of parallel and distributed simulations are discussed. Two principal paradigms for constructing simulations today are considered. Particular attention is paid to an approach for federating parallel and distributed simulators. We describe the design, performance and applications of the ASimJava framework. Two practical examples, namely, a simple manufacturing system and computer network simulations are provided to illustrate the effectiveness and range of applications of the presented software tool.

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17

Nylund, Hasse, und Paul H. Andersson. „Simulation of service-oriented and distributed manufacturing systems“. Robotics and Computer-Integrated Manufacturing 26, Nr. 6 (Dezember 2010): 622–28. http://dx.doi.org/10.1016/j.rcim.2010.07.009.

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18

Niewiadomska-Szynkiewicz, E., M. Warchoł und M. Żmuda. „Software Environment for Distributed Simulation of Complex Systems“. IFAC Proceedings Volumes 31, Nr. 20 (Juli 1998): 811–16. http://dx.doi.org/10.1016/s1474-6670(17)41897-0.

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19

Karatza, Helen D. „Modeling and simulation of distributed systems and networks“. Simulation Modelling Practice and Theory 12, Nr. 3-4 (Juli 2004): 183–85. http://dx.doi.org/10.1016/j.simpat.2004.04.001.

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20

Suryanarayanan, Vinoth, Georgios Theodoropoulos und Michael Lees. „PDES-MAS: Distributed Simulation of Multi-agent Systems“. Procedia Computer Science 18 (2013): 671–81. http://dx.doi.org/10.1016/j.procs.2013.05.231.

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21

Smid, G. E., Ka C. Cheok und J. L. Overholt. „Distributed Network Environment for Virtual Vehicle Systems Simulation“. IFAC Proceedings Volumes 33, Nr. 26 (September 2000): 63–69. http://dx.doi.org/10.1016/s1474-6670(17)39122-x.

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22

Dobre, Ciprian, Florin Pop und Valentin Cristea. „New Trends in Large Scale Distributed Systems Simulation“. Journal of Algorithms & Computational Technology 5, Nr. 2 (Juni 2011): 221–57. http://dx.doi.org/10.1260/1748-3018.5.2.221.

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23

Lees, Michael, Brian Logan und Georgios Theodoropoulos. „Distributed simulation of agent-based systems with HLA“. ACM Transactions on Modeling and Computer Simulation 17, Nr. 3 (Juli 2007): 11. http://dx.doi.org/10.1145/1243991.1243992.

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24

Bagrodia, R. L., und C. C. Shen. „MIDAS: integrated design and simulation of distributed systems“. IEEE Transactions on Software Engineering 17, Nr. 10 (1991): 1042–58. http://dx.doi.org/10.1109/32.99192.

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25

Teo, Yong Meng, und Seng Chuan Tay. „Performance analysis of parallel simulation on distributed systems“. Distributed Systems Engineering 3, Nr. 1 (März 1996): 20–31. http://dx.doi.org/10.1088/0967-1846/3/1/004.

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26

Rabenstein, R. „Simulation of linear continuous systems with distributed parameters“. Simulation Practice and Theory 1, Nr. 3 (Dezember 1993): 93–107. http://dx.doi.org/10.1016/0928-4869(93)90001-7.

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27

Chen, Jinchao, Chenglie Du, Pengcheng Han und Xiaoyan Du. „Real-time digital simulator for distributed systems“. SIMULATION 97, Nr. 5 (22.01.2021): 299–309. http://dx.doi.org/10.1177/0037549720986865.

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Simulation has been widely adopted as a support tool for the validation and experimentation of distributed systems. It allows different devices and applications to be evaluated and analyzed without requiring the actual presence of those machines. Although the simulation plays an important role in investigating and evaluating the behaviors of devices, it results in a serious simulator building problem as the distributed systems become more and more complicated and dynamically data driven. Most of the existing simulators are designed and developed to target a specific type of application, lacking the capabilities to be a configurable and standardized tool for researchers. To solve the adaptability and reusability problems of simulators, this paper proposes a new approach to design and implement a configurable real-time digital simulator for hardware devices that are connected via data buses in distributed systems. First, the proposed simulator uses a logic automaton to simulate the activities of a real device, and generates the incentive data for tested equipment according to the predefined XML-based files. Then with a virtual bus, the simulator can receive, handle, and send data in various network environments, improving the flexibility and adaptability of a simulator design. Experimental results show that the proposed simulator has a high real-time performance, and can meet the increasing requirements of modern simulations of distributed systems.

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28

Majumdar, Shikharesh, und Azzedine Boukerche. „Distributed systems performance“. Performance Evaluation 58, Nr. 2-3 (November 2004): 87–88. http://dx.doi.org/10.1016/j.peva.2004.08.003.

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29

Sapaty, P. S. „Symbiosis of Distributed Simulation and Control under Spatial Grasp Technology“. Mathematical machines and systems 3 (2020): 23–48. http://dx.doi.org/10.34121/1028-9763-2020-3-23-48.

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We are witnessing rapidly growing world dynamics caused by climate change, military, religious and ethnic conflicts, terrorism, refugee flows and weapons proliferation, political and industrial restructuring too. Dealing with frequently emerging crises may need rapid integration of scattered heterogeneous resources into capable operational forces pursuing goals which may not be known in advance. Proper understanding and managing of unpredictable and crisis situations may need their detailed simulation at runtime and even ahead of it. The current paper aims at deep integration, actually symbiosis, of advanced simulation with live system control and management, which can be effectively organized in nationwide and world scale. It will be presenting the latest version of Spatial Grasp Technology (SGT) which is not based on traditional communicating parts or agents, as usual, but rather using self-spreading, self-replicating, and self-modifying higher-level code covering and matching distributed systems at runtime while providing global integrity, goal-orientation, and finding effective solutions. These spatial solutions are often hundreds of times shorter and simpler than with other approaches due to special recursive scenario language hiding traditional system management routines inside its parallel and distributed interpretation. The paper provides basics for deep integration, actually symbiosis, of different worlds allowing us to unite advanced distributed simulation with spatial parallel and fully distributed control, while doing all this within the same high-level and very simple Spatial Grasp formalism and its basic Spatial Grasp Language (SGL). It will also mention various SGT applications including economy, ecology, space research & conquest and security, where effective symbiosis of distributed interactive simulation with live control and management may provide a real breakthrough. SGL can be quickly implemented even within standard university environments by a group of system programmers, similar to its previous versions in different countries under the author’s supervision. The technology can be installed in numerous copies worldwide and deeply integrated with any other systems, actually acquiring unlimited power throughout the world.

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30

Bocciarelli, Paolo, Andrea D’Ambrogio, Alberto Falcone, Alfredo Garro und Andrea Giglio. „A model-driven approach to enable the simulation of complex systems on distributed architectures“. SIMULATION 95, Nr. 12 (26.02.2019): 1185–211. http://dx.doi.org/10.1177/0037549719829828.

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The increasing complexity of modern systems makes their design, development, and operation extremely challenging and therefore new systems engineering and modeling and simulation (M&S) methods, techniques, and tools are emerging, also to benefit from distributed simulation environments. In this context, one of the most mature and popular standards for distributed simulation is the IEEE 1516-2010 - Standard for M&S high level architecture (HLA). However, building and maintaining distributed simulations components, based on the IEEE 1516-2010 standard, is still a challenging and effort-consuming task. To ease the development of full-fledged HLA-based simulations, the paper proposes the MONADS method (MOdel-driveN Architecture for Distributed Simulation), which relies on the model-driven systems engineering paradigm. The method takes as input system models specified in Systems Modeling Language, the reference modeling language in the systems engineering field, and produces as output the final code of the corresponding HLA-based distributed simulation through a chain of model-to-model and model-to-text transformations. The obtained simulation code is based on the HLA Development Kit software framework, which has been developed by the SMASH-Lab (System Modeling and Simulation Hub - Laboratory) of the University of Calabria (Italy), in cooperation with the Software, Robotics, and Simulation Division (ER) of NASA’s Lyndon B. Johnson Space Center (JSC) in Houston (TX, USA). The effectiveness of the method is shown through a case study that concerns a military patrol operation, in which a set of drones are engaged to patrol the border of a military area, in order to prevent both ground and flight attacks from entering the area.

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31

Hibino, Hironori, Yoshiro Fukuda und Yoshiyuki Yura. „A Synchronization Mechanism with Shared Storage Model for Distributed Manufacturing Simulation Systems“. International Journal of Automation Technology 9, Nr. 3 (05.05.2015): 248–60. http://dx.doi.org/10.20965/ijat.2015.p0248.

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Simulators play important roles in the designing of new acturing systems. As manufacturing systems are being created on larger and more complicated scales than ever before, it is increasingly necessary to have opportunities for several persons to design a manufacturing system concurrently. In this case, the designers often use suitable discrete event simulators to evaluate their assigned subsystems. After the subsystems are evaluated, it is necessary to evaluate the full system. To do this, the designers need to make the manufacturing system model by synchronizing several different simulators. In such distributed simulation systems using discrete event simulators, it is important to manage a distributed simulation clock and each simulator clock as well as to define interfaces among the simulation models. With the simulation clock, it is often necessary to perform rollbacks. The rollback function returns the simulation clock to a past time in order to synchronize events among the simulations. However, most commercially available simulators do not include the rollback function.The purpose of this research is to develop a distributed simulation synchronization method that includes a function for managing distributed simulation clocks without the rollback function and for managing interfaces among simulation models.In this paper, we propose a storage model concept as the method. We develop an algorithm to implement the proposed concept, and we develop a distributed simulation system configuration using HLA. A case study is then carried out to evaluate the performance of the cooperative work.

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32

XHAFA, FATOS, JAVIER CARRETERO, LEONARD BAROLLI und ARJAN DURRESI. „REQUIREMENTS FOR AN EVENT-BASED SIMULATION PACKAGE FOR GRID SYSTEMS“. Journal of Interconnection Networks 08, Nr. 02 (Juni 2007): 163–78. http://dx.doi.org/10.1142/s0219265907001965.

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In this paper we present a study on the requirements for the design and implementation of simulation packages for Grid systems. Grids are emerging as new distributed computing systems whose main objective is to manage and allocate geographically distributed computing resources to applications and users in an efficient and transparent manner. Grid systems are at present very difficult and complex to use for experimental studies of large-scale distributed applications. Although the field of simulation of distributed computing systems is mature, recent developments in large-scale distributed systems are raising needs not present in the simulation of the traditional distributed systems. Motivated by this, we present in this work a set of basic requirements that any simulation package for Grid computing should offer. This set of functionalities is obtained after a careful review of most important existing Grid simulation packages and includes new requirements not considered in such simulation packages. Based on the identified set of requirements, a Grid simulator is developed and exemplified for the Grid scheduling problem.

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33

Afifi, L., M. Hakam, M. Bahadi und A. El Jai. „Enlarged Asymptotic Compensation in Discrete Distributed Systems“. Mathematical Modelling of Natural Phenomena 5, Nr. 7 (2010): 139–44. http://dx.doi.org/10.1051/mmnp/20105723.

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34

Borshchev, Andrei, Yuri Karpov und Vladimir Kharitonov. „Distributed simulation of hybrid systems with AnyLogic and HLA“. Future Generation Computer Systems 18, Nr. 6 (Mai 2002): 829–39. http://dx.doi.org/10.1016/s0167-739x(02)00055-9.

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35

Svyatnyy, V. A., O. M. Miroshkin, M. A. Minakov und H. E. Marhiiev. „DISTRIBUTED PARALLEL SIMULATION ENVIRONMENT USAGE FOR EMBEDDED SYSTEMS DEVELOPMENT“. Scientific notes of Taurida National V.I. Vernadsky University. Series: Technical Sciences 5, Nr. 1 (2019): 161–65. http://dx.doi.org/10.32838/2663-5941/2019.5-1/26.

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36

Yamane, Amine, Simon Abourida, Yahia Bouzid und François Tempez. „Real-Time Simulation of Distributed Energy Systems and Microgrids“. IFAC-PapersOnLine 49, Nr. 27 (2016): 183–87. http://dx.doi.org/10.1016/j.ifacol.2016.10.680.

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37

Santiago, W., und S. B. Jørgensen. „A Dynamic Simulation Strategy for Cycled Distributed Parameter Systems“. IFAC Proceedings Volumes 27, Nr. 2 (Mai 1994): 249–54. http://dx.doi.org/10.1016/s1474-6670(17)48159-6.

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38

Tricas, Fernando, und Javier Martı́nez. „Distributed control systems simulation using high level Petri nets“. Mathematics and Computers in Simulation 46, Nr. 1 (April 1998): 47–55. http://dx.doi.org/10.1016/s0378-4754(97)00157-2.

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39

Fujii, S., H. Sandoh, H. Matsuda und M. Tasaka. „A Study on Distributed Simulation for Flexible Manufacturing Systems“. IFAC Proceedings Volumes 23, Nr. 3 (September 1990): 27–32. http://dx.doi.org/10.1016/s1474-6670(17)52529-x.

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40

Jianchao, Zeng, Wu Juhua, Chen Zhixin und Li Linsheng. „A Hierarchical distributed simulation algorithm for discrete event systems“. IFAC Proceedings Volumes 24, Nr. 14 (Juni 1991): 201–3. http://dx.doi.org/10.1016/s1474-6670(17)69352-2.

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41

Wang, Jinzhong, Zheng-Dong Ma und Gregory M. Hulbert. „A Gluing Algorithm for Distributed Simulation of Multibody Systems“. Nonlinear Dynamics 34, Nr. 1/2 (Oktober 2003): 159–88. http://dx.doi.org/10.1023/b:nody.0000014558.70434.b0.

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42

El-Khattam, W., Y. G. Hegazy und M. M. A. Salama. „Investigating Distributed Generation Systems Performance Using Monte Carlo Simulation“. IEEE Transactions on Power Systems 21, Nr. 2 (Mai 2006): 524–32. http://dx.doi.org/10.1109/tpwrs.2006.873131.

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43

Legrand, Iosif. „Multi-threaded, discrete event simulation of distributed computing systems“. Computer Physics Communications 140, Nr. 1-2 (Oktober 2001): 274–85. http://dx.doi.org/10.1016/s0010-4655(01)00281-8.

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44

Ontuzheva, G. A., E. R. Bruchanova, I. N. Rudov, N. O. Pikov und O. A. Antamoshkin. „Simulation modelling of the heterogeneous distributed information processing systems“. IOP Conference Series: Materials Science and Engineering 450 (30.11.2018): 052018. http://dx.doi.org/10.1088/1757-899x/450/5/052018.

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45

Alian, Mohammad, Daehoon Kim und Nam Sung Kim. „pd-gem5: Simulation Infrastructure for Parallel/Distributed Computer Systems“. IEEE Computer Architecture Letters 15, Nr. 1 (01.01.2016): 41–44. http://dx.doi.org/10.1109/lca.2015.2438295.

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46

Botygin, I. A., V. N. Popov und S. G. Frolov. „Simulation model of load balancing in distributed computing systems“. IOP Conference Series: Materials Science and Engineering 177 (Februar 2017): 012017. http://dx.doi.org/10.1088/1757-899x/177/1/012017.

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47

Olsen, Arthur S. „Analysis methodology for simulation of distributed adaptive routing systems“. ACM SIGCOMM Computer Communication Review 27, Nr. 5 (Oktober 1997): 61–72. http://dx.doi.org/10.1145/269790.269796.

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48

Fujii, Susumu, Yasushi Kidani, Atsushi Ogita und Toshiya Kaihara. „Synchronization Mechanisms for Integration of Distributed Manufacturing Simulation Systems“. SIMULATION 72, Nr. 3 (März 1999): 187–97. http://dx.doi.org/10.1177/003754979907200310.

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49

Grebnev, V. A., Yu V. Kapitonova und A. A. Letichevskii. „Hardware simulation in distributed computing systems: Methods and tools“. Cybernetics and Systems Analysis 31, Nr. 3 (Mai 1995): 350–64. http://dx.doi.org/10.1007/bf02366514.

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50

Wu, Jian, Noel N. Schulz und Wenzhong Gao. „Distributed simulation for power system analysis including shipboard systems“. Electric Power Systems Research 77, Nr. 8 (Juni 2007): 1124–31. http://dx.doi.org/10.1016/j.epsr.2006.09.009.

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