Title :
Distributed learning algorithms for data network routing problem: models, convergence analysis and optimality
Author_Institution :
Dept. of Comput. Eng., Patras Univ., Greece
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;
Conference_Titel :
Electrotechnics, 1988. Conference Proceedings on Area Communication, EUROCON 88., 8th European Conference on
Conference_Location :
Stockholm
DOI :
10.1109/EURCON.1988.11143
