document: Towards autonomic web-sites based on learning automata Autonomics or self-reorganization becomes pertinent for web-sites serving a large number of users with highly varying workloads . An important component of self-adaptation is to model the behavior of users and adapt accordingly . This paper proposes a learning-automata based technique for model discovery . User access patterns are used to construct an FSM model of user behavior that in turn is used for prediction and prefetching . The proposed technique uses a generalization algorithm to classify behavior patterns into a small number of generalized classes . It has been tested on both synthetic and live data-sets and has shown a prediction hit-rate of up to 89 % on a real web-site .
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