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The growing complexity and scale of systems implies challenges to include Autonomic Computing capabilities that help to maintain or improve the performance, availability and reliability characteristics. The autonomic management of a system can be defined deterministically based on experiment observations on the system and possible results of associated plans. However in dynamic environments with changing conditions and requirements, a better technique to diagnose observations and learn about the functioning conditions of the managed system is needed to guide the autonomic management. In the case of medical diagnostic, tests have included statistical and probabilistic models to aid and improve the results and select better medical treatments. In this paper we also adopt a probabilistic approach to define a Bayesian network from monitored data of an Enterprise Service Bus under different workload conditions. This model is used by the Autonomic Service Bus as a knowledge base to diagnose the cause of degradation problems and repair them. Experimental results assess the effectiveness of our approach.

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