doi: 10.4304/jsw.9.8.2206-2211
Improving ESB Capabilities through Diagnosis Based on Bayesian Networks and Machine Learning
- Universidad Autónoma de Yucatán, Facultad de Matemáticas, Mérida, México CNRS, LAAS, Toulouse, France
Abstract
The growing complexity and scale of systemsimplies challenges to include Autonomic Computing capabilitiesthat help maintaining or improving the performance,availability and reliability of nowadays systems. In dynamicenvironments, the systems have to deal with changing conditionsand requirements; thereby the autonomic featuresneed a better technique to analyze and diagnose problems,and learn about the functioning conditions of the managedsystem. In the medical diagnostic area, the tests have includedstatistical and probabilistic models to aid and improvethe results and select better medical treatments. We proposea probabilistic approach to implement an analysis process.The base of our approach is building a Bayesian network asmodel representing runtime properties of the Managed Elementand their relationships. The Bayesian network is initiallybuilt from monitored data of an Enterprise ServiceBus platform under different workload conditions, bymeans a structure learning algorithm. We aim to improvethe functionalities of an Enterprise Service Bus platformintegrating monitoring and fault diagnosis capabilities. Acase study is presented to prove the effectiveness of our approach.
Keywords
- Autonomic computing
- bayesian network
- pr
How to Cite
Roberto Koh-Dzul, Mariano Vargas-Santiago, Codé Diop, Ernesto Exposito, Francisco Moo-Mena, and Jorge Gómez-Montalvo, "Improving ESB Capabilities through Diagnosis Based on Bayesian Networks and Machine Learning," Journal of Software, vol. 9, no. 8, pp. 2206-2211, 2014. https://doi.org/ 10.4304/jsw.9.8.2206-2211
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).