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.