Lecture slides from Philipp’s lecture on cloud benchmarking (given as part of the IDEA League Summer School on Engineering Complex Systems and IT with Big data and Information Technology)
Joel’s Research Talk at IEEE CLOUD 2018 in San Francisco
Joel Scheuner, Philipp Leitner (2018). Estimating Cloud Application Performance Based on Micro-Benchmark Profiling in Proceedings of the 11th IEEE International Conference on Cloud Computing (CLOUD’18).
Cloud WorkBench: https://github.com/sealuzh/cloud-workbench
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 paper develops a cloud benchmarking methodology that uses micro-benchmarks to profile applications and subsequently predicts how an application performs on a wide range of cloud services. A study with a real cloud provider (Amazon EC2) has been conducted to quantitatively evaluate the estimation model with 38 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 paper emphasizes the importance of cloud benchmarking by substantiating the suitability of micro-benchmarks for estimating application performance in comparison to common baselines but also highlights that only selected micro-benchmarks are relevant to estimate the performance of a particular application.