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Journal articles on the topic 'Distributed systems modeling'

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Published: 28 June 2021
Last updated: 8 February 2022

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1

Hac, Anna. "Modeling distributed file systems." ACM SIGMETRICS Performance Evaluation Review 19, no. 4 (May 1992): 22–27. http://dx.doi.org/10.1145/140728.140729.

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2

Herzberg, D., and M. Broy. "Modeling layered distributed communication systems." Formal Aspects of Computing 17, no. 1 (October 29, 2004): 1–18. http://dx.doi.org/10.1007/s00165-004-0051-8.

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3

Zheng, Hong, Yu Yue Du, and Yu ShuXia. "Modeling Non-Repudiation in Distributed Systems." Information Technology Journal 7, no. 1 (December 15, 2007): 228–30. http://dx.doi.org/10.3923/itj.2008.228.230.

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4

Basta, Robert A., Bruce P. Kraemer, Walter P. Bond, and Thomas J. Billhartz. "Distributed Communication Network Systems Performance Modeling." INCOSE International Symposium 2, no. 1 (July 1992): 365–72. http://dx.doi.org/10.1002/j.2334-5837.1992.tb01515.x.

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5

Bogolyubov, A. N., A. I. Erokhin, and M. I. Svetkin. "Mathematical modeling of systems with distributed interaction." Физические основы приборостроения 8, no. 1 (March 15, 2019): 13–19. http://dx.doi.org/10.25210/jfop-1901-013019.

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6

Boldrin, Fabio, Chiara Taddia, and Gianluca Mazzini. "Web Distributed Computing Systems Implementation and Modeling." International Journal of Adaptive, Resilient and Autonomic Systems 1, no. 1 (January 2010): 75–91. http://dx.doi.org/10.4018/jaras.2010071705.

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This article proposes a new approach for distributed computing. The main novelty consists in the exploitation of Web browsers as clients, thanks to the availability of JavaScript, AJAX and Flex. The described solution has two main advantages: it is client-free, so no additional programs have to be installed to perform the computation, and it requires low CPU usage, so client-side computation is no invasive for users. The solution is developed using both AJAX and Adobe®Flex® technologies embedding a pseudo-client into a Web page that hosts the computation. While users browse the hosting Web page, computation takes place resolving single sub-problems and sending the solution to the server-side part of the system. Our client-free solution is an example of high resilient and auto-administrated system that is able to organize the scheduling of the processes and the error management in an autonomic manner. A mathematical model has been developed over this solution. The main goals of the model are to describe and classify different categories of problems on the basis of the feasibility and to find the limits in the dimensioning of the scheduling systems to have convenience in the use of this approach. The new architecture has been tested through different performance metrics by implementing two examples of distributed computing, the cracking of an RSA cryptosystem through the factorization of the public key and the correlation index between samples in genetic data sets. Results have shown good feasibility of this approach both in a closed environment and also in an Internet environment, in a typical real situation.
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7

De Decker, B., and P. Verbaeten. "Modeling distributed systems: Communication issues in Hermix." Microprocessing and Microprogramming 25, no. 1-5 (January 1989): 239–43. http://dx.doi.org/10.1016/0165-6074(89)90202-0.

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8

Kuske, Sabine, and Peter Knirsch. "Modeling Agent Systems with Distributed Transformation Units." Electronic Notes in Theoretical Computer Science 82, no. 7 (June 2003): 79–90. http://dx.doi.org/10.1016/s1571-0661(04)80748-5.

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9

Vega, M. P., E. L. Lima, and J. C. Pinto. "Modeling Lumped-Distributed Systems Using Neural Networks." IFAC Proceedings Volumes 33, no. 10 (June 2000): 803–8. http://dx.doi.org/10.1016/s1474-6670(17)38638-x.

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10

Helmicki, A. J., C. A. Jacobson, and C. N. Nett. "Control-Oriented Modeling of Distributed Parameter Systems." Journal of Dynamic Systems, Measurement, and Control 114, no. 3 (September 1, 1992): 339–46. http://dx.doi.org/10.1115/1.2897353.

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In this paper the use of linear, time-invariant, distributed parameter systems (LTI-DPS) as models of physical processes is considered from a control viewpoint. Specifically, recent theoretical results obtained by the authors for the control-oriented modeling of LTI-DPS are concisely reviewed and then a series of applications is given in order to illustrate the practical ramifications of these results.
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11

Kozlov, K. O. "MODELING PRINCIPLES OF SPATIALLY DISTRIBUTED RADAR SYSTEMS." Issues of radio electronics, no. 3 (March 20, 2018): 7–10. http://dx.doi.org/10.21778/2218-5453-2018-3-7-10.

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The physical and mathematical principles of bistatic radar are considered in the article, the essential characteristics are considered when constructing a mathematical model of a spatially distributed radar system. The necessity of software implementation of such a model for further investigation of bistatic radars is substantiated. The article discusses the necessity of upgrading radar equipment to improve the quality of the detection and identification of small flying machines and means of achieving this. Discusses the physical and mathematical principles of non-emitting radar system with diversity receiver and the transmitter upon detection of air targets on the background of the underlying surface, are analyzed essential when constructing mathematical models of spatially-separated radar systems. Provides a general description of the scheme, non-emitting radar station and timing diagram of signals within the system with diversity receiver and the transmitter. Analyzed analytical expressions for modeling non-emitting radar - which allows you to analyze the system in different variations, on the basis of the results obtained by theoretical calculations and experimental studies. The necessity of a software implementation of this model for further research on bistatic radar.
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12

Grabowski, M., J. R. W. Merrick, J. R. Harrold, T. A. Massuchi, and J. D. van Dorp. "Risk modeling in distributed, large-scale systems." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 30, no. 6 (2000): 651–60. http://dx.doi.org/10.1109/3468.895888.

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13

Wang, N., J. C. Lu, and P. Kvam. "Reliability Modeling in Spatially Distributed Logistics Systems." IEEE Transactions on Reliability 55, no. 3 (September 2006): 525–34. http://dx.doi.org/10.1109/tr.2006.879603.

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14

Hariri, S., and H. Mutlu. "Hierarchical modeling of availability in distributed systems." IEEE Transactions on Software Engineering 21, no. 1 (1995): 50–56. http://dx.doi.org/10.1109/32.341847.

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15

Emami-Naeini, A., J. L. Ebert, D. de Roover, R. L. Kosut, M. Dettori, L. M. L. Porter, and S. Ghosal. "Modeling and control of distributed thermal systems." IEEE Transactions on Control Systems Technology 11, no. 5 (September 2003): 668–83. http://dx.doi.org/10.1109/tcst.2003.816411.

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16

Ray, Arnab, and Rance Cleaveland. "Formal Modeling Of Middleware-based Distributed Systems." Electronic Notes in Theoretical Computer Science 108 (December 2004): 21–37. http://dx.doi.org/10.1016/j.entcs.2004.01.010.

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17

da Silva, Robson Marinho, Israel F. Benítez-Pina, Mauricio F. Blos, Diolino J. Santos Filho, and Paulo E. Miyagi. "Modeling of reconfigurable distributed manufacturing control systems." IFAC-PapersOnLine 48, no. 3 (2015): 1284–89. http://dx.doi.org/10.1016/j.ifacol.2015.06.262.

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18

Carver, Doris L. "Integrated modeling of distributed object-oriented systems." Journal of Systems and Software 26, no. 3 (September 1994): 233–44. http://dx.doi.org/10.1016/0164-1212(94)90014-0.

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19

GIESE, HOLGER, and GUIDO WIRTZ. "Visual Modeling of Object-Oriented Distributed Systems." Journal of Visual Languages & Computing 12, no. 2 (April 2001): 183–202. http://dx.doi.org/10.1006/jvlc.2000.0194.

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20

Orlikowski, Cezary, and Rafał Hein. "Port-Based Modeling of Distributed-Lumped Parameter Systems." Solid State Phenomena 164 (June 2010): 183–88. http://dx.doi.org/10.4028/www.scientific.net/ssp.164.183.

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This paper presents a uniform, port-based approach for modeling of both lumped and distributed parameter systems. Port-based model of the distributed system has been defined by application of bond graph methodology and distributed transfer function method (DTFM). The proposed approach combines versatility of port-based modeling and accuracy of distributed transfer function method. A concise representation of lumped-distributed systems has been obtained. The proposed method of modeling enables to formulate input data for computer analysis by application of DTFM.
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21

Karatza, Helen D. "Modeling and simulation of distributed systems and networks." Simulation Modelling Practice and Theory 12, no. 3-4 (July 2004): 183–85. http://dx.doi.org/10.1016/j.simpat.2004.04.001.

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22

Villar, Eugenio, Javier Merino, Hector Posadas, Rafik Henia, and Laurent Rioux. "Mega-modeling of complex, distributed, heterogeneous CPS systems." Microprocessors and Microsystems 78 (October 2020): 103244. http://dx.doi.org/10.1016/j.micpro.2020.103244.

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23

Wang, P. K. C. "Modeling and control of nonlinear micro-distributed systems." Nonlinear Analysis: Theory, Methods & Applications 30, no. 6 (December 1997): 3215–26. http://dx.doi.org/10.1016/s0362-546x(96)00117-4.

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24

Mohamed, Ahmed M., Lester Lipsky, and Reda Ammar. "Modeling parallel and distributed systems with finite workloads." Performance Evaluation 60, no. 1-4 (May 2005): 303–25. http://dx.doi.org/10.1016/j.peva.2004.10.005.

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25

González Harbour, Michael, J. Javier Gutiérrez, José M. Drake, Patricia López Martínez, and J. Carlos Palencia. "Modeling distributed real-time systems with MAST 2." Journal of Systems Architecture 59, no. 6 (June 2013): 331–40. http://dx.doi.org/10.1016/j.sysarc.2012.02.001.

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26

Ceroni, Jose A., Masayuki Matsui, and Shimon Y. Nof. "Communication-based coordination modeling in distributed manufacturing systems." International Journal of Production Economics 60-61 (April 1999): 281–87. http://dx.doi.org/10.1016/s0925-5273(98)00196-0.

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27

Dubey, Abhishek, Rajat Mehrotra, Sherif Abdelwahed, and Asser Tantawi. "Performance modeling of distributed multi-tier enterprise systems." ACM SIGMETRICS Performance Evaluation Review 37, no. 2 (October 16, 2009): 9–11. http://dx.doi.org/10.1145/1639562.1639566.

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28

Shieh, Y. B., D. Ghosal, P. R. Chintamaneni, and S. K. Tripathi. "Modeling of hierarchical distributed systems with fault-tolerance." IEEE Transactions on Software Engineering 16, no. 4 (April 1990): 444–57. http://dx.doi.org/10.1109/32.54296.

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29

Yang, B. "Distributed Transfer Function Analysis of Complex Distributed Parameter Systems." Journal of Applied Mechanics 61, no. 1 (March 1, 1994): 84–92. http://dx.doi.org/10.1115/1.2901426.

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This paper presents a new analytical and numerical method for modeling and synthesis of complex distributed parameter systems that are multiple continua combined with lumped parameter systems. In the analysis, the complex distributed parameter system is first divided into a number of subsystems; the distributed transfer functions of each subsystem are determined in exact and closed form by a state space technique. The complex distributed parameter system is then assembled by imposing displacement compatibility and force balance at the nodes where the subsystems are interconnected. With the distributed transfer functions and the transfer functions of the constraints and lumped parameter systems, exact, closed-form formulation is obtained for various dynamics and vibration problems. The method does not require a knowledge of system eigensolutions, and is valid for non-self-adjoint systems with inhomogeneous boundary conditions. In addition, the proposed method is convenient in computer coding and suitable for computerized symbolic manipulation.
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30

Prorok, Amanda, Nikolaus Correll, and Alcherio Martinoli. "Multi-level spatial modeling for stochastic distributed robotic systems." International Journal of Robotics Research 30, no. 5 (February 8, 2011): 574–89. http://dx.doi.org/10.1177/0278364911399521.

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We propose a combined spatial and non-spatial probabilistic modeling methodology motivated by an inspection task performed by a group of miniature robots. Our models explicitly consider spatiality and yield accurate predictions on system performance. An agent’s spatial distribution over time is modeled by the Fokker—Planck diffusion model and complements current non-spatial microscopic and macroscopic models that model the discrete state distribution of a distributed robotic system. We validate our models on a microscopic level based on sub-microscopic, embodied robot simulations as well as real robot experiments. Subsequently, using the validated microscopic models as our template, abstraction is raised to the level of macroscopic difference equations. We discuss the dependency of the modeling performance on the distance from the robot origin (drop-off location) and temporal convergence of the team distribution. Also, using an asymmetric setup, we show the necessity of spatial modeling methodologies for environments where the robotic platform underlies drift phenomena.
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31

Omarov, M. A. "CF Networks for Modeling of Distributed Computation Information Systems." Telecommunications and Radio Engineering 67, no. 11 (2008): 1017–24. http://dx.doi.org/10.1615/telecomradeng.v67.i11.70.

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32

Taentzer, G. "Visual Modeling of Distributed Object Systems by Graph Transformation." Electronic Notes in Theoretical Computer Science 51, no. 1 (February 2004): 1–15. http://dx.doi.org/10.1016/s1571-0661(04)00212-9.

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33

Taentzer, Gabriele. "Visual Modeling of Distributed Object Systems by Graph Transformation." Electronic Notes in Theoretical Computer Science 51 (May 2002): 304–18. http://dx.doi.org/10.1016/s1571-0661(04)80212-3.

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34

Wu, Yongwei, Feng Ye, Kang Chen, and Weimin Zheng. "Modeling of Distributed File Systems for Practical Performance Analysis." IEEE Transactions on Parallel and Distributed Systems 25, no. 1 (January 2014): 156–66. http://dx.doi.org/10.1109/tpds.2013.19.

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35

Jafari, Ali, Ehsan Khamespanah, Marjan Sirjani, Holger Hermanns, and Matteo Cimini. "PTRebeca: Modeling and analysis of distributed and asynchronous systems." Science of Computer Programming 128 (October 2016): 22–50. http://dx.doi.org/10.1016/j.scico.2016.03.004.

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36

Rahman, Rameez, Tamás Vinkó, David Hales, Johan Pouwelse, and Henk Sips. "Design space analysis for modeling incentives in distributed systems." ACM SIGCOMM Computer Communication Review 41, no. 4 (October 22, 2011): 182–93. http://dx.doi.org/10.1145/2043164.2018458.

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37

Simons, C., and G. Wirtz. "Modeling context in mobile distributed systems with the UML." Journal of Visual Languages & Computing 18, no. 4 (August 2007): 420–39. http://dx.doi.org/10.1016/j.jvlc.2007.07.001.

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38

Javadi, Bahman, and Kenan M. Matawie. "Modeling of correlated resources availability in distributed computing systems." Simulation Modelling Practice and Theory 82 (March 2018): 147–59. http://dx.doi.org/10.1016/j.simpat.2017.12.017.

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39

Chen, Yeong-Jia, Daniel Mossé, and Shi-Kuo Chang. "A Framework for Modeling Dependable Real-Time Distributed Systems." IFAC Proceedings Volumes 28, no. 25 (November 1995): 269–74. http://dx.doi.org/10.1016/s1474-6670(17)44855-5.

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40

Sunar, M., and S. S. Rao. "Distributed modeling and actuator location for piezoelectric control systems." AIAA Journal 34, no. 10 (October 1996): 2209–11. http://dx.doi.org/10.2514/3.13380.

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41

Nam, Yunyoung, Sangjin Hong, and Seungmin Rho. "Data modeling and query processing for distributed surveillance systems." New Review of Hypermedia and Multimedia 19, no. 3-4 (December 2013): 299–327. http://dx.doi.org/10.1080/13614568.2013.849762.

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42

Kartal, Yusuf Bora, Ece GÜran Schmidt, and Klaus Werner Schmidt. "Modeling Distributed Real-Time Systems in TIOA and UPPAAL." ACM Transactions on Embedded Computing Systems 16, no. 1 (November 3, 2016): 1–26. http://dx.doi.org/10.1145/2964202.

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43

Misra, Subhas Chandra, Virender Singh, Naveen Kumar Jha, and Sandip Bisui. "Modeling privacy issues in distributed enterprise resource planning systems." International Journal of Communication Systems 29, no. 2 (November 20, 2014): 378–401. http://dx.doi.org/10.1002/dac.2839.

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44

Longo, Francesco, Dario Bruneo, Salvatore Distefano, and Marco Scarpa. "Variable operating conditions in distributed systems: modeling and evaluation." Concurrency and Computation: Practice and Experience 27, no. 10 (October 13, 2014): 2506–30. http://dx.doi.org/10.1002/cpe.3419.

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45

Mavromatidis, Georgios, Kristina Orehounig, L. Andrew Bollinger, Marc Hohmann, Julien F. Marquant, Somil Miglani, Boran Morvaj, et al. "Ten questions concerning modeling of distributed multi-energy systems." Building and Environment 165 (November 2019): 106372. http://dx.doi.org/10.1016/j.buildenv.2019.106372.

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46

Prorok, A., N. Correll, and A. Martinoli. "Multi-level spatial modeling for stochastic distributed robotic systems." International Journal of Robotics Research 30, no. 5 (April 1, 2011): 574–89. http://dx.doi.org/10.1177/0278364910399521.

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47

Long, Q., M. Xie, and S. H. Ng. "PARAMETER ESTIMATION IN RELIABILITY MODELING OF DISTRIBUTED DETECTION SYSTEMS." IFAC Proceedings Volumes 40, no. 6 (2007): 19–24. http://dx.doi.org/10.3182/20070613-3-fr-4909.00006.

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48

Lee, Fu-Shin, Tess J. Moon, and Glenn Y. Masada. "Modeling of distributed electromechanical systems using Extended Bond Graphs." Journal of the Franklin Institute 331, no. 1 (January 1994): 43–60. http://dx.doi.org/10.1016/0016-0032(94)90078-7.

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49

Choi, Sung-il, Sangho Park, and Murali Subramaniyam. "Analysis of processing time between distributed geometric modeling systems." Computer-Aided Design 43, no. 2 (February 2011): 115–21. http://dx.doi.org/10.1016/j.cad.2010.09.004.

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50

Girault, Alain, and Eric Rutten. "Modeling Fault-tolerant Distributed Systems for Discrete Controller Synthesis." Electronic Notes in Theoretical Computer Science 133 (May 2005): 81–100. http://dx.doi.org/10.1016/j.entcs.2004.08.059.

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