Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Automated control in cloud computing: challenges and opportunities
ACDC '09 Proceedings of the 1st workshop on Automated control for datacenters and clouds
SciCloud: Scientific Computing on the Cloud
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Management of Server Farms for Performance and Profit
The Computer Journal
Statistical machine learning makes automatic control practical for internet datacenters
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
BenchLab: an open testbed for realistic benchmarking of web applications
WebApps'11 Proceedings of the 2nd USENIX conference on Web application development
Resource provisioning of web applications in heterogeneous clouds
WebApps'11 Proceedings of the 2nd USENIX conference on Web application development
Achieving Performance and Availability Guarantees with Spot Instances
HPCC '11 Proceedings of the 2011 IEEE International Conference on High Performance Computing and Communications
Squeezing Out the Cloud via Profit-Maximizing Resource Allocation Policies
MASCOTS '12 Proceedings of the 2012 IEEE 20th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Perspectives and reflections on cloud computing and internet technologies from NordiCloud 2012
Proceedings of the Second Nordic Symposium on Cloud Computing & Internet Technologies
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By allowing resources to be acquired on-demand and in variable amounts, cloud computing provides an appealing environment for deploying pilot projects and for performance testing of Web applications and services. However, setting up cloud environments for performance testing still requires a significant amount of manual effort. To aid performance engineers in this task, we developed a framework that integrates several common benchmarking and monitoring tools. The framework helps performance engineers to test applications under various configurations and loads. Furthermore, the framework supports dynamic server allocation based on incoming load using a response-time-aware heuristics. We validated the framework by deploying and stress-testing the MediaWiki application. An experimental evaluation was conducted aimed at comparing the response-time-aware heuristics against Amazon Auto-Scale.