Abstract
Finding paths in networks is a well exercised activity both in theory and practice but still remains a challenge when the search domain is a dynamic communication network environment with changing traffic patterns and network topology. To enforce dependability in such network environments new routing techniques are called upon. In this paper we describe a distributed algorithm capable of finding cyclic paths in scarcely meshed networks using ant-like agents. Cyclic paths are especially interesting in the context of protection switching, and scarce meshing is typical in real world telecommunication networks. Two new next-node-selection strategies for the ant-like agents are introduced to better handle low degrees of meshing. Performance results from Monte Carlo Simulations of systems implementing the strategies are presented indicating a promising behavior of the second strategy.
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References
S. Aidarous and T. Plevyak, ed., Telecommunications Network Management into the 21st Century. IEEE Press, 1994.
Otto Wittner and Bjarne E. Helvik, “Cross Entropy Guided Ant-like Agents Finding Dependable Primary/Backup Path Patterns in Networks,” in Proceedings of Congress on Evolutionary Computation (CEC2002), (Honolulu, Hawaii), IEEE, May 12–17th 2002.
W.D. Grover and D. Stamatelakis, “Cycle-oriented distributed preconfiguration: ring-like speed with mesh-like capacity for self-planning network restoration,” in Proceedings of IEEE International Conference on Communications, vol. 1, pp. 537–543, 7–11 June 1998.
D. Stamatelakis and W.D. Grover, “Rapid Span or Node Restoration in IP Networks usingVirtual Protection Cycles,” in Proceedings of 3rd Canadian Conferance on Broadband Research (CCBR’99), (Ottawa), 7 November 1999.
M. Decina and T. Plevyak (editors), “Special Issue: Self-Healing Networks for SDH and ATM,” IEEE Communications Magazine, vol. 33, September 1995.
Bjarne E. Helvik and Otto Wittner, “Using the Cross Entropy Method to Guide/Govern Mobile Agent’s Path Finding in Networks,” in Proceedings of 3rd InternationalWorkshop on Mobile Agents for Telecommunication Applications, Springer Verlag, August 14–16 2001.
Vu Anh Pham and A. Karmouch, “Mobile Software Agents: An Overview,” IEEE Communications Magazine, vol. 36, pp. 26–37, July 1998.
R. Schoonderwoerd, O. Holland, J. Bruten, and L. Rothkrantz, “Ant-based Load Balancing in Telecommunications Networks,” Adaptive Behavior, vol. 5, no. 2, pp. 169–207, 1997.
G. D. Caro and M. Dorigo, “AntNet: Distributed Stigmergetic Control for CommunicationsNetworks,” Journal of Artificial Intelligence Research, vol. 9, pp. 317–365, Dec 1998.
T. W. B. P. F. Oppacher, “Connection Management using Adaptive Mobile Agents,” in Proceedings of 1998 International Conference on Parallel and Distributed Processing Techniques and Applications (PDAPTA’98), 1998.
J. Schuringa, “Packet Routing with Genetically Programmed Mobile Agents,” in Proceedings of SmartNet 2000, (Wienna), September 2000.
Marco Dorigo and Gianni Di Caro, “Ant Algorithms for Discrete Optimization,” Artificial Life, vol. 5, no. 3, pp. 137–172, 1999.
Reuven Y. Rubinstein, “The Cross-Entropy Method for Combinatorial and Continuous Optimization,” Methodology and Computing in Applied Probability, pp. 127–190, 1999.
M. Zlochin, M. Birattari, N. Meuleau, and M. Dorigo, “Model-based Search for Combinatorial Optimization,” IRIDIA IRIDIA/2001-15, Universite Libre de Bruxelles, Belgium, 2000.
Reuven Y. Rubinstein, “The Cross-Entropy and Rare Events for Maximum Cut and Bipartition Problems-Section 4.4,” Transactions on Modeling and Computer Simulation, To appear.
D. Stamatelakis and W.D. Grover, “Theoretical Underpinnings for the Efficiency of Restorable Networks Using Preconfigured Cycles (“p-cycles”),” IEEE Transactions on Communications, vol. 48, pp. 1262–1265, August 2000.
Kenneth A. Ross and Charles R.B. Wright, Discrete Mathematics. Prentice Hall, 2nd ed., 1988.
V. Maniezzo, “Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem,” INFORMS Journal on Computing, vol. 11, no. 4, pp. 358–369, 1999.
DARPA: VINT project, “UCB/LBNL/VINT Network Simulator-ns (version 2).” http://www.isi.edu/nsnam/ns/.
Calvert, K.I. and Doar, M.B. and Zegura, E.W., “Modeling Internet Topology,” IEEE Communications Magazine, vol. 35, pp. 160–163, June 1997.
Goldberg, D., Genetic Algorithms in Search, Optimization and MachineLearning. Addison Wesley, 1998.
Z. Michalewicz, Genetic algorithms+Data Stuctures=Evolution Programs. Springer Verlag, second ed., 1996.
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Wittner, O., Helvik, B.E. (2002). Cross-Entropy Guided Ant-Like Agents Finding Cyclic Paths in Scarcely Meshed Networks. In: Dorigo, M., Di Caro, G., Sampels, M. (eds) Ant Algorithms. ANTS 2002. Lecture Notes in Computer Science, vol 2463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45724-0_11
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DOI: https://doi.org/10.1007/3-540-45724-0_11
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