Self-adaptive and sensitivity-aware QoS modeling for the cloud

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Chen, T. ORCID: 0000-0001-5025-5472 and Bahsoon, R., 2013. Self-adaptive and sensitivity-aware QoS modeling for the cloud. In: M. Litoiu and J. Mylopoulos, eds., Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems - SEAMS 2013, San Francisco, CA, USA, 20-21 May 2013. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), pp. 43-52. ISBN 9781479903443

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Official URL: http://doi.org/10.1109/SEAMS.2013.6595491

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.

Item Type:

Chapter in book

Creators:

Chen, T. and Bahsoon, R.

Publisher:

Institute of Electrical and Electronics Engineers (IEEE)

Place of Publication:

Piscataway, NJ

Date:

May 2013

ISBN:

9781479903443