Parallel genetic algorithms for the tuning of a fuzzy AQM controller
Di Fatta, G., Lo Re, G. and Urso, A. (2003) Parallel genetic algorithms for the tuning of a fuzzy AQM controller. Lecture Notes in Computer Science, 2003 (2667). pp. 417-426. ISSN 0302-9743
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To link to this article DOI: 10.1007/3-540-44839-X_45
This paper presents the results of the application of a parallel Genetic Algorithm (GA) in order to design a Fuzzy Proportional Integral (FPI) controller for active queue management on Internet routers. The Active Queue Management (AQM) policies are those policies of router queue management that allow the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. Two different parallel implementations of the genetic algorithm are adopted to determine an optimal configuration of the FPI controller parameters. Finally, the results of several experiments carried out on a forty nodes cluster of workstations are presented.
1 S. Athuraliya, V. Li, S.H. Low, and K. Yin. Rem: Active queue management. IEEE Network Magazine, 15(3):48-53, May 2001. 2 Various Authors. ns-2, network simulator (ver. 2). 2000. http://www.isi.edu/nsnam/ns/. 3 E. Cantú-Paz. A summary of research on parallel genetic algorithms. Technical Report 950076, Univ. Illinois Urbana-Champaign, Urbana, IL, July 1995. 4 O. Cordon, F. Herrera, F. Hoffmann, and L. Magdalena. Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. Advances in Fuzzy Systems. World Scientific, Singapore, July 2001. 5 Guiseppe Di Fatta , Guiseppe Lo Re , Alfonso Urso, A Fuzzy Approach for the Network Congestion Problem, Proceedings of the International Conference on Computational Science-Part I, p.286-295, April 21-24, 2002 6 Sally Floyd , Van Jacobson, Random early detection gateways for congestion avoidance, IEEE/ACM Transactions on Networking (TON), v.1 n.4, p.397-413, Aug. 1993 [doi>10.1109/90.251892] 7 David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989 8 F. Hoffmann. Evolutionary algorithms for fuzzy control system design. Proceedings of the IEEE, 89(9):1318-33, September 2001. 9 C. Hollot, V. Misra, D. Towsley, and W. Gong. On designing improved controllers for aqm routers supporting tcp flows. In Proc. of IEEE INFOCOM, Anchorage US, April 2001. IEEE. 10 Ian Foster , Nicholas T. Karonis, A grid-enabled MPI: message passing in heterogeneous distributed computing systems, Proceedings of the 1998 ACM/IEEE conference on Supercomputing (CDROM), p.1-11, November 07-13, 1998, San Jose, CA 11 W. Li. Design of a hybrid fuzzy logic proportional plus conventional integral-derivative controller. IEEE Trans. on Fuzzy Systems, 6(4):449-463, 1998. 12 H. A. Malki, H. D. Li, and G. R. Chen. New design and stability analysis of fuzzy proprortional-derivative control system. IEEE Trans. on Fuzzy Systems, 2(4):245 -254, 1994. 13 M. May, J. Bolot, C. Diot, and B. Lyles. Reasons not to deply red. In Proc. of IWQoS, pages 260-262, 1999. 14 M. Tomassini. Parallel and distributed evolutionary algorithms: A review. In P. Neittaanmki J. Periaux K. Miettinen, M. Mkel, editor, Evolutionary Algorithms in Engineering and Computer Science. 15 W. Feng, D. Kundur, D. Saha, and K. Shin. A self configurating red gateway. In Proc. of IEEE INFOCOM, pages 1320-1328. IEEE, 1999.
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