Feedback-based learning for self-managed network elements

Autonomic network management systems will operate in a volatile network environment; thus they should be able to continuously adapt their decision making mechanism through learning from the behavior of the communication system. In this paper, a novel learning scheme is proposed based on the network-wide collected performance experience, targeting the enhancement of network elements' decision making engine. The algorithm employs a fuzzy logic inference engine in order to enable self-managed network elements faults or optimization opportunities identification, which is enhanced by applying data mining techniques on the accumulated observations.