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Networks are the widest spreading system which Mankind has ever developed, today. New needs, services, and use are created each day where network systems represent the most critical aspect. Additionally, the interconnection between networks of different types is growing as never. This acceleration of interconnection of all sorts brings to the front scene the issue of performance measure in networks design and operation. For that purpose, engineers are orienting their major efforts towards the development of methods, models and codes to assess and measure networks performance in terms of probability of being connected. From this stand point of view, the engineers’ attention is mainly focused on defining the network “Connectivity” and measuring it, in different manner, using probabilities. Subsequently, we are observing an accelerating course towards quantitative probabilistic models to describe and assess networks’ Connectivity, since the sixties. Modelling realistic networks is still far from being satisfactorily achieved using quantitative probabilistic models. On the other hand, little room has been lift to exploring the potential of graphical and topological models to develop qualitative and semi- quantitative models in order to assess networks performance. In this paper, we have tried to explore the potential of the topological modelling and are proposing an original one. The proposed model is still in its earliest phase of development. But, it sounds very promoting at that stage of maturation. According to this model, the term “performance” may be extended, beyond the “connectivity probabilistic concept”, to “robustness” and to a “deepest insight” of the “connectivity concept” itself. The impact is immediate on the way the “reliability concept” would be extended to cover network systems.
Słowa kluczowe
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Tom
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21--38
Opis fizyczny
Bibliogr. 20 poz., tab., wykr.
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- INSA-Rouen, F-76810 Saint-Etienne du Rouvray, France
autor
- INSA-Rouen, F-76810 Saint-Etienne du Rouvray, France
autor
- INSA-Rouen, F-76810 Saint-Etienne du Rouvray, France
Bibliografia
- [1] AboElFotoh, H.M. & Colbourn, C.J. (1989). Computing 2-terminal reliability for radiobroadcast networks. IEEE Transactions on Reliability, 38(5), 538-555.
- [2] Agrawal, A. & Satyanarayana, A. (1984). An O(|E|) Time Algorithm for Computing the Reliability of a Class of Directed Networks. Operations Research, 32(3), 493-515.
- [3] Agarwal, M., Sen, K. & Mohan, P. (2007). GERT Analysis of m-Consecutive-k-out-of-n Systems. IEEE Transactions on Reliability, Vol. 56, No 1.
- [4] Altiparmak, F. & Dengiz, B. (2003). Optimal Design of Reliable Computer Networks: A Comparison of Metaheuristics. Journal of Heuristics, 9, 471-487.
- [5] Altiparmak, F. et al. (2009). A General Neural Network Model for Estimating Telecommunications Network Reliability. IEEE Transactions on Reliability, Vol. 58, No 1.
- [6] Ball, M.O., Colbourn, C.J. & Provan, J.S. (1992). Network reliability. Network Models, 7, 673-762.
- [7] Cancela, H. & Khadiri, M. El. (2003). The Recursive Variance-Reduction Simulation Algorithm for Network Reliability Evaluation. IEEE Transactions on Reliability, Vol. 52, No 2.
- [8] Chen, X. & Lyu, M.R. (2005). Reliability analysis for various communication schemes in wireless CORBA. Reliability. IEEE Transactions on Reliability, 54(2), 232-242.
- [9] Colbourn, C.J. & Harms, D.D. (1988). Bounding all-terminal reliability in computer networks. Networks, Vol. 18, 1-12.
- [10] Dominiak, S., Bayer, N., Habermann, J., Rakocevic, V. & Xu, B. (2007). Reliability Analysis of IEEE 802.16 Mesh.” 2nd IEEE-IFIP International Workshop on Broadband Convergence Networks. Workshop proceedings. Vol. 16, 1-12, Publisher: IEEE. ISBN: 1424412978, DOI: 10.1109/BCN.2007.372739.
- [11] Dotson, W. & Gobien, J. (1979). A new analysis technique for probabilistic graphs. Circuits and Systems. IEEE Transactions on Reliability, 26(10), 855-865.
- [12] El Khadiri, M. & Rubino, G. (1992). A MonteCarlo Methode Based on Antithetic Variates for Network Rreliability Computations. Unite de Recherche INRIA-Rennes, rapport de recherche no 1609, Février.
- [13] Huang, T.H. (2003). The exact reliability of a 2dimentional k-within rxs-out-of-mxn: F system: A finite Markov approach. Thesis presented at the National University Kaohsiung, Taiwan, 2003. supervised by Yung-Ming Chang, Dept. of Mathematics, National Taitung University.
- [14] Mendiratta, B.V. (2002). A Simple ATM Backbone Network Reliability Model. An IMA/MCIM Joint Seminar in Applied Mathematics, April 19, 2002, Minnesota Center for Industrial Mathematics, University of Minnesota.
- [15] Ramirez-Marquez, J.E., Coit, D.W. & Tortorella, M. A Generalized Multistate Based Path Vector Approach for Multistate Two Terminal Reliability.” http://ie.rutgers.edu/resource/research_paper/pap er_05-001.pdf.
- [16] Torrieri, D. (1994). Calculation of node-pair reliability in large networks with unreliable nodes. IEEE Transactions on Reliability, 43(3), 375-377.
- [17] Van Slyke, R.M. & Frank, H. (1972). Network reliability analysis I. Networks, Vol. 1, 279-290.
- [18] Watcharasitthiwat, K., Pothiya, S. & Wardkein, P. Multiple Tabu Search Algorithm for Solving the Topology Network Design. Open Access Database www.i-techonline.com
- [19] Yeh, F.M., Lin, H.Y. & Kuo, S.Y. (2002). Analyzing network reliability with imperfect nodes using OBDD. Dependable Computing, 2002. Proceedings. 2002 Pacifc Rim International Symposium, 89-96.
- [20] Yo, Y.B. (1988). A Comparison of Algorithms for Terminal-Pair Reliability. IEEE Transactions on Reliability, Vol. 37(2).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-21d3f08b-14f5-4882-99c4-c67d01cd04cd