The Community for Technology Leaders
RSS Icon
Subscribe
Newport Beach, California USA
May 23, 2011 to May 26, 2011
ISBN: 978-0-7695-4395-6
pp: 104-113
ABSTRACT
Cloud computing is an emerging infrastructure paradigm that promises to eliminate the need for companies to maintain expensive computing hardware. Through the use of virtualization and resource time-sharing, clouds address with a single set of physical resources a large user base with diverse needs. Thus, clouds have the potential to provide their owners the benefits of an economy of scale and, at the same time, become an alternative for both the industry and the scientific community to self-owned clusters, grids, and parallel production environments. For this potential to become reality, the first generation of commercial clouds need to be proven to be dependable. In this work we analyze the dependability of cloud services. Towards this end, we analyze long-term performance traces from Amazon Web Services and Google App Engine, currently two of the largest commercial clouds in production. We find that the performance of about half of the cloud services we investigate exhibits yearly and daily patterns, but also that most services have periods of especially stable performance. Last, through trace-based simulation we assess the impact of the variability observed for the studied cloud services on three large-scale applications, job execution in scientific computing, virtual goods trading in social networks, and state management in social gaming. We show that the impact of performance variability depends on the application, and give evidence that performance variability can be an important factor in cloud provider selection.
INDEX TERMS
cloud, performance, traces, analysis, performance variability, production cloud services
CITATION
Alexandru Iosup, Nezih Yigitbasi, Dick Epema, "On the Performance Variability of Production Cloud Services", CCGRID, 2011, Cluster Computing and the Grid, IEEE International Symposium on, Cluster Computing and the Grid, IEEE International Symposium on 2011, pp. 104-113, doi:10.1109/CCGrid.2011.22
18 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool