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
Avresky, D.R., DI SANZO, P., Pellegrini, A., Ciciani, B., Forte, L. (2015). Proactive Scalability and Management of Resources in Hybrid Clouds via Machine Learning. In 2015 IEEE 14th International Symposium on Network Computing and Applications (pp.114-119). IEEE Computer Society [10.1109/NCA.2015.36].
Proactive Scalability and Management of Resources in Hybrid Clouds via Machine Learning
DI SANZO, PIERANGELO;
2015-01-01
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 approachesI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
