Performance Prediction for Unseen Virtual Machines

Various papers have reported on the differential performance of virtual machine instances of the same type, and same supposed performance rating, in Public Infrastructure Clouds. It has been established that instance performance is determined in large part by the underlying hardware, and performance variation is due to the heterogeneous nature of large and growing Clouds. Currently, customers have limited ability to request performance levels, and can only identify the physical CPU backing an instance, and so associate CPU models with expected performance levels, once resources have been obtained. Little progress has been made to predict likely performance for instances on such Public Clouds. In this paper, we demonstrate how such performance predictions could be provided for, predicated on knowledge derived empirically from one common Public Infrastructure Cloud.