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Given the elasticity, dynamicity and on-demand nature of the cloud, cloud-based applications require dynamic models for Quality of Service (QoS), especially when the sensitivity of QoS tends to fluctuate at runtime. These models can be autonomically used by the cloud-based application to correctly self-adapt its QoS provision. The authors present a novel dynamic and self-adaptive sensitivity-aware QoS modeling approach, which is fine-grained and grounded on sound machine learning techniques. In particular, they combine symmetric uncertainty with two training techniques: Auto-Regressive Moving Average with eXogenous inputs model (ARMAX) and Artificial Neural Network (ANN) to reach two formulations of the model.
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