A Learning Model for Adaptive Network Routing

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 addressing which can support learning and generalization are proposed.