This paper illustrates the effort to integrate a machine learning-based framework which can predict the remaining time to failure of computing nodes with Hadoop applications. This work is part of a larger effort targeting the development of a cloud-oriented autonomic framework to increase the availability of applications subject to software anomalies, and to jointly improve their performance. The framework uses machine-learning, software rejuvenation, and load distribution techniques to proactively prevent failures. We believe that this work allows to set a possible path towards the definition of best practices for the development of systems to support autonomic management of cloud applications, illustrating what are the issues that should be addressed by the research community. Indeed, given the scale and the complexity of modern computing infrastructures, effective autonomic management approaches of cloud applications are becoming mandatory.
Avresky Dimiter, R., Pellegrini, A., DI SANZO, P. (2017). Machine learning-based management of cloud applications in hybrid clouds: a hadoop case study. In 2017 IEEE 16th International Symposium on Network Computing and Applications, NCA 2017 (pp.114-119). IEEE Computer Society [10.1109/NCA.2017.8171352].