Automatic exploration of datacenter performance regimes
ACDC '09 Proceedings of the 1st workshop on Automated control for datacenters and clouds
Statistical machine learning makes automatic control practical for internet datacenters
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Engineering autonomic controllers for virtualized web applications
ICWE'10 Proceedings of the 10th international conference on Web engineering
SASO '10 Proceedings of the 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Statistical inference of software performance models for parametric performance completions
QoSA'10 Proceedings of the 6th international conference on Quality of Software Architectures: research into Practice - Reality and Gaps
Iterative test suites refinement for elastic computing systems
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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Cloud infrastructures allow service providers to implement elastic applications. These can be scaled at runtime to dynamically adjust their resources allocation to maintain consistent quality of service in response to changing working conditions, like flash crowds or periodic peaks. Providers need models to predict the system performances of different resource allocations to fully exploit dynamic application scaling. Traditional performance models such as linear models and queuing networks might be simplistic for real Cloud applications; moreover, they are not robust to change. We propose a performance modelling approach that is practical for highly variable elastic applications in the Cloud and automatically adapts to changing working conditions. We show the effectiveness of the proposed approach for the synthesis of a self-adaptive controller.