Performance Inference: A Novel Approach for Planning the Capacity of IaaS Cloud Applications

This work presents a novel approach to support application capacity planning in infrastructure-as-a-service (IaaS) clouds. The approach, called performance inference, relies on the assumption that it is possible to establish a capacity relation between different resource configurations offered by a given IaaS provider, enabling one to infer an application´s performance under certain resource configurations and workloads, based upon the application´s actual performance as observed for other related resource configurations and workloads. Preliminary evaluation results, obtained from testing the performance of a well-known blogging application (Word Press) in a public IaaS cloud (Amazon EC2), show that the best performance inference strategies can significantly reduce (over 80%) the total number of application deployment scenarios that need to be actually tested in the cloud, with a high (over 98%) inference accuracy.