The continuing growth of the cloud computing market has led to an unprecedented diversity of cloud services. To support service selection, micro benchmarks are commonly used to identify the best performing cloud service. However, it remains unclear how relevant these synthetic micro benchmarks are for gaining insights into the performance of real-world applications. Therefore, this thesis develops a cloud benchmarking methodology that uses micro benchmarks to profile application performance and subsequently estimates how an application performs on a wide range of cloud services. A study with a real cloud provider has been conducted to quantitatively evaluate the estimation model with 38 selected metrics from 23 micro benchmarks and 2 applications from different domains. The results reveal remarkably low variability in cloud service performance and show that selected micro benchmarks can estimate the duration of a scientific computing application with a relative error of less than 10% and the response time of a Web serving application with a relative error between 10% and 20%. In conclusion, this thesis emphasizes the importance of cloud benchmarking by substantiating the suitability of micro benchmarks for estimating application performance but also highlights that only selected micro benchmarks are relevant to estimate the performance of a particular application.
This master thesis was published by the University of Zurich at Merlin.