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Dettaglio pubblicazione

2015, 2015 IEEE 14th International Symposium on Network Computing and Applications, Pages 114-119

Proactive Scalability and Management of Resources in Hybrid Clouds via Machine Learning (04b Atto di convegno in volume)

Avresky Dimiter R., DI SANZO Pierangelo, Pellegrini Alessandro, Ciciani Bruno, Forte Luca

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
ISBN: 978-150901849-9; 0769556817
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