A Learning Model for Adaptive Network Routing†
Department of Computer ScienceBrigham Young University, Provo, Utah 84602martinez@cs.byu.edugeorge@axon.cs.byu.edu
Abstract
Increasing size, complexity, and dynamics of networks require adaptive routing mechanisms. This paper proposes initial concepts towards a learning and generalization mechanism to support adaptive real-time routing. An ASOCS learning model is employed as the basic adaptive router. Generalization of routing is based not only on source/destination address, but also on such factors as packet size, priority, privacy, network congestion, etc. Mechanisms involving continual adaptation based on feedback are presented. Extensions to conventional ...
Get Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
