Estimating Cloud Application Performance Based on Micro-Benchmark Profiling
Paper i proceeding, 2018

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.

cloud computing

performance prediction

application benchmark

performance

Web application

micro benchmark

benchmarking

Författare

Joel Scheuner

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for People, Architecture, Requirements and Traceability

Philipp Leitner

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for People, Architecture, Requirements and Traceability

2018 IEEE 11th International Conference on Cloud Computing (CLOUD)

2159-6190 (ISSN) 2159-6190 (eISSN)

90-97 8457787

IEEE 11th International Conference on Cloud Computing, CLOUD 2018
San Francisco, USA,

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Datavetenskap (datalogi)

DOI

10.1109/CLOUD.2018.00019

Mer information

Senast uppdaterat

2019-07-08