Distributed learning algorithms for data network routing problem: models, convergence analysis and optimality

Author

Vasilakos, A.V.

Author_Institution

Dept. of Comput. Eng., Patras Univ., Greece

fYear

1988

fDate

13-17 Jun 1988

Firstpage

218

Lastpage

221

Abstract

The behavior of the automata at the nodes of a data network is studied for an abstract network representation in which only very general functional properties are assumed. A model of a nonstationary environment is proposed with state variables as penalty parameters. The limiting behavior of the model is studied. Simulation results shown that under abnormal conditions (i.e. change of topology) the learning algorithms outperformed existing routing algorithms. Using a minimal amount of feedback, the equalizing properties of automata in equilibrium can still be used to produce optimal or nearly optimal routing

Keywords

automata theory; distributed processing; learning systems; telecommunication networks; telecommunications control; abstract network; automata; convergence analysis; data network routing; feedback; learning algorithms; models; optimality; state variables; topology; Algorithm design and analysis; Convergence; Feedback; Kernel; Learning automata; Probability distribution; Routing; State-space methods; Telecommunication traffic; Traffic control;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrotechnics, 1988. Conference Proceedings on Area Communication, EUROCON 88., 8th European Conference on

Conference_Location

Stockholm

Type

conf

DOI

10.1109/EURCON.1988.11143

Filename

11143