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Continuous Learning Automata Solutions to the Capacity Assignment Problem
June 2000 (vol. 49 no. 6)
pp. 608-620
ASCII Text
x 
 
B. John Oommen, T. Dale Roberts, "Continuous Learning Automata Solutions to the Capacity Assignment Problem," IEEE Transactions on Computers, vol. 49, no. 6, pp. 608-620, June, 2000.
 
 
BibTex
x
 
@article{ 10.1109/12.862220,
author = {B. John Oommen and T. Dale Roberts},
title = {Continuous Learning Automata Solutions to the Capacity Assignment Problem},
journal ={IEEE Transactions on Computers},
volume = {49},
number = {6},
issn = {0018-9340},
year = {2000},
pages = {608-620},
doi = {http://doi.ieeecomputersociety.org/10.1109/12.862220},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Computers
TI - Continuous Learning Automata Solutions to the Capacity Assignment Problem
IS - 6
SN - 0018-9340
SP608
EP620
EPD - 608-620
A1 - B. John Oommen,
A1 - T. Dale Roberts,
PY - 2000
KW - Learning automata
KW - capacity assignment problem
KW - network design.
VL - 49
JA - IEEE Transactions on Computers
ER -
 

Abstract—The Capacity Assignment (CA) problem focuses on finding the best possible set of capacities for the links that satisfies the traffic requirements in a prioritized network while minimizing the cost. Most approaches consider a single class of packets flowing through the network, but, in reality, different classes of packets with different packet lengths and priorities are transmitted over the networks. In this paper, we assume that the traffic consists of different classes of packets with different average packet lengths and priorities. We shall look at three different solutions to this problem. Marayuma and Tang [9] proposed a single algorithm composed of several elementary heuristic procedures. Levi and Ersoy [8] introduced a simulated annealing approach that produced substantially better results. In this paper, we introduce a new method which uses continuous learning automata to solve the problem. Our new schemes produce superior results when compared with either of the previous solutions and is, to our knowledge, currently the best known solution.

[1] D. Bertzekas and R. Gallager, Data Networks, second ed. Prentice-Hall, 1992.
[2] T.D. Roberts, “Learning Automata Solutions to the Capacity Assignment Problem,” MCS thesis, School of Computer Science, Carleton Univ., Ottawa, Canada, 1997.
[3] R.L. Ellis, Designing Data Networks, pp. 99-114. Englewood Cliffs, N.J.: Prentice Hall, 1986.
[4] D. Etheridge and E. Simon, Information Networks Planning and Design, pp. 263-272. Englwood Cliffs, N.J.: Prentice Hall, 1992.
[5] M. Gerla and L. Kleinrock, “On the Topological Design of Distributed Computer Networks,” IEEE Trans. Comm., vol. 25, no. 1, pp. 48-60, 1977.
[6] L. Kleinrock, Communication Nets: Stochastic Message Flow and Delay. New York: McGraw-Hill, 1964.
[7] S. Lakshmivarahan, Learning Algorithms Theory and Applications. New York: Springer-Verlag, 1981.
[8] A. Levi and C. Ersoy, “Discrete Link Capacity Assignment in Prioritized Computer Networks: Two Approaches,” Proc. Ninth Int'l Symp. Computer and Information Sciences, pp. 408-415, Nov. 1994.
[9] K. Maruyama and D.T. Tang, “Discrete Link Capacity and Priority Assignments in Communication Networks,” IBM J. Research and Development, pp. 254-263, May 1977.
[10] K.S. Narendra and M.L. Thathachar, Learning Automata: An Introduction. Prentice Hall, 1989.
[11] B.J. Oommen and D.C.Y. Ma, “Deterministic Learning Automata Solutions to the Equi-Partitioning Problem,” IEEE Trans. Computers, vol. 37, no. 1, pp. 2-14, Jan. 1988.

Index Terms:
Learning automata, capacity assignment problem, network design.
Citation:
B. John Oommen, T. Dale Roberts, "Continuous Learning Automata Solutions to the Capacity Assignment Problem," IEEE Transactions on Computers, vol. 49, no. 6, pp. 608-620, June 2000, doi:10.1109/12.862220
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