Proactive Scalability and Management of Resources in Hybrid Clouds via Machine Learning (short paper)
Dimiter R. Avresky, Pierangelo Di Sanzo, Alessandro Pellegrini, Bruno Ciciani, and Luca Forte
Published in: Proceedings of the 14th IEEE International Symposium on Network Computing and Applications
Download PDF
Abstract:
In this paper, we present a novel framework for supporting the management and optimization of application subject to software anomalies and deployed on large scale cloud architectures, composed of different geographically distributed cloud regions. The framework uses machine learning models for predicting failures caused by accumulation of anomalies. It introduces a novel workload balancing approach and a proactive system scale up/scale down technique. We developed a prototype of the framework and present some experiments for validating the applicability of the proposed approaches.
BibTeX Entry:
@inproceedings{Avr15,
author = {Avresky, Dimiter R. and Di Sanzo, Pierangelo and Pellegrini, Alessandro and Ciciani, Bruno and Forte, Luca},
booktitle = {Proceedings of the 14th IEEE International Symposium on Network Computing and Applications},
title = {Proactive Scalability and Management of Resources in Hybrid Clouds via Machine Learning (short paper)},
year = {2015},
month = sep,
pages = {114--119},
publisher = {IEEE Computer Society},
series = {NCA},
doi = {10.1109/NCA.2015.36},
location = {Boston, MA, USA}
}
author = {Avresky, Dimiter R. and Di Sanzo, Pierangelo and Pellegrini, Alessandro and Ciciani, Bruno and Forte, Luca},
booktitle = {Proceedings of the 14th IEEE International Symposium on Network Computing and Applications},
title = {Proactive Scalability and Management of Resources in Hybrid Clouds via Machine Learning (short paper)},
year = {2015},
month = sep,
pages = {114--119},
publisher = {IEEE Computer Society},
series = {NCA},
doi = {10.1109/NCA.2015.36},
location = {Boston, MA, USA}
}