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2013 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) (2013)
San Francisco, CA, USA
May 20, 2013 to May 21, 2013
ISSN: 2157-2305
ISBN: 978-1-4799-0344-3
pp: 43-52
Tao Chen , School of Computer Science, University of Birmingham, Birmingham, UK. B15 2TT
Rami Bahsoon , School of Computer Science, University of Birmingham, Birmingham, UK. B15 2TT
ABSTRACT
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. We 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, we 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. We describe a middleware for implementing the approach. We experimentally evaluate the effectiveness of our models using the RUBiS benchmark and the FIFA 1998 workload trends. The results show that our modeling approach is effective and the resulting models produce better accuracy when compared with conventional models.
INDEX TERMS
Quality of service, Adaptation models, Sensitivity, Accuracy, Uncertainty, Software, Data models
CITATION

T. Chen and R. Bahsoon, "Self-adaptive and sensitivity-aware QoS modeling for the cloud," 2013 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), San Francisco, CA, USA, 2013, pp. 43-52.
doi:10.1109/SEAMS.2013.6595491
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