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Discretized learning automata solutions to the capacity assignment problem for prioritized networks.

B J Oommen¹, T D Roberts

¹Sch. of Comput. Sci., Carleton Univ., Ottawa, Ont., Canada.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

Summary

We developed a new discretized learning automaton (LA) method to solve the complex network capacity assignment (CA) problem, optimizing link capacities for prioritized traffic while minimizing costs.

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Area of Science:

Computer Science
Network Engineering
Artificial Intelligence

Background:

Traditional network capacity assignment (CA) often simplifies traffic to a single class.
Real-world networks transmit diverse packet classes with varying lengths and priorities.
The CA problem is computationally challenging, classified as NP-hard.

Purpose of the Study:

To introduce a novel discretized learning automaton (LA) approach for network capacity assignment.
To address the generalized CA problem with multiple prioritized traffic classes.
To minimize network costs while meeting all traffic demands.

Main Methods:

Implementation of a discretized learning automaton (LA) solution.
Introduction of a new 'meta-action' philosophy within the LA framework.
Comparison against existing heuristic, simulated annealing, and genetic algorithm (GA) methods.

Main Results:

The proposed discretized LA solution effectively handles multi-class, prioritized network traffic.
The method demonstrates superior performance compared to previous continuous LA and heuristic approaches.
The novel meta-action philosophy enhances the LA's problem-solving capabilities.

Conclusions:

The discretized LA offers a highly effective solution for the complex network capacity assignment problem.
This approach provides a significant advancement for optimizing network resources with diverse traffic.
The meta-action philosophy represents a key innovation in learning automaton applications.