A Classical Conditioning Model for Policy-Based Management

With the overwhelming development of network to large-scale, heterogeneity and high-speed, policy-based management becomes a promising solution, but its static policy configurations can not accord with the target of self-management. Inspired by classical conditioning, the basic learning mode of biological system, we presented a dynamic policy adaptation model. The new neural network based model is composed of a number of simple building blocks composing a complete reflex arc. Some typical experiments are used to evaluate the new model and they represent the set of tasks that a model of classical conditioning needs to address in order to be successful. The output from the model fit the results of classical conditioning experiments. The model could successfully realize the self-learning process of classical conditioning and achieve an adaptive network policy management.