Adaptive signal processing
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
SLA Decomposition: Translating Service Level Objectives to System Level Thresholds
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Automated control of multiple virtualized resources
Proceedings of the 4th ACM European conference on Computer systems
Q-clouds: managing performance interference effects for QoS-aware clouds
Proceedings of the 5th European conference on Computer systems
Integrating Resource Consumption and Allocation for Infrastructure Resources on-Demand
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Resource provisioning with budget constraints for adaptive applications in cloud environments
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Statistical machine learning makes automatic control practical for internet datacenters
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Integrated estimation and tracking of performance model parameters with autoregressive trends
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Intelligent management of virtualized resources for database systems in cloud environment
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
TRACON: interference-aware scheduling for data-intensive applications in virtualized environments
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments
ICPP '11 Proceedings of the 2011 International Conference on Parallel Processing
Modeling virtualized applications using machine learning techniques
VEE '12 Proceedings of the 8th ACM SIGPLAN/SIGOPS conference on Virtual Execution Environments
Translation of application-level terms to resource-level attributes across the Cloud stack layers
ISCC '11 Proceedings of the 2011 IEEE Symposium on Computers and Communications
A workload characterization study of the 1998 World Cup Web site
IEEE Network: The Magazine of Global Internetworking
Profit-Based Experimental Analysis of IaaS Cloud Performance: Impact of Software Resource Allocation
SCC '12 Proceedings of the 2012 IEEE Ninth International Conference on Services Computing
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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. 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.