Machine Learning-based Management of Cloud Applications in Hybrid Clouds: a Hadoop Case Study
Dimiter R. Avresky, Alessandro Pellegrini, and Pierangelo Di Sanzo
Published in: Proceedings of the 16th IEEE International Symposium on Network Computing and Applications
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Abstract:
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.
BibTeX Entry:
author = {Avresky, Dimiter R. and Pellegrini, Alessandro and Di Sanzo, Pierangelo},
booktitle = {Proceedings of the 16th IEEE International Symposium on Network Computing and Applications},
title = {Machine Learning-based Management of Cloud Applications in Hybrid Clouds: a Hadoop Case Study},
year = {2017},
month = oct,
pages = {114--119},
publisher = {IEEE Computer Society},
series = {NCA},
doi = {10.1109/NCA.2017.8171352},
location = {Cambridge, MA, USA}
}