Improving ESB Capabilities through Diagnosis Based on Bayesian Networks and Machine Learning

The growing complexity and scale of systems implies challenges to include Autonomic Computing capabilities that help maintaining or improving the performance, availability and reliability of nowadays systems. In dynamic environments, the systems have to deal with changing conditions and requirements; thereby, the autonomic features need a better technique to analyze and diagnose problems, and learn about the functioning conditions of the managed system. In the medical diagnostic area, the tests have included statistical and probabilistic models to aid and improve the results and select better medical treatments. The authors have proposed a probabilistic approach to implement an analysis process. The base of their approach is building a Bayesian network as model representing runtime properties of the Managed Element and their relationships. The Bayesian network is initially built from monitored data of an Enterprise Service Bus platform under different workload conditions, by means a structure learning algorithm. The authors aim to improve the functionalities of an Enterprise Service Bus platform integrating monitoring and fault diagnosis capabilities.