MrMpZfVPjl13TBP
R. Mian, P. Martin, F. Zulkernine, J.L. Vazquez-Poletti. Towards building performance models for data-intensive workloads in public clouds. In 4th ACM/SPEC International Conference on Performance Engineering, ICPE '13, Pages 259-270, 2013.
Abstract
The cloud computing paradigm provides the "illusion" of infinite resources and, therefore, becomes a promising candidate for large-scale data-intensive computing. In this paper, we explore experiment-driven performance models for data-intensive workloads executing in an infrastructure-as-a-service (IaaS) public cloud. The performance models help in predicting the workload behaviour, and serve as a key component of a larger framework for resource provisioning in the cloud. We determine a suitable prediction technique after comparing popular regression methods. We also enumerate the variables that impact variance in the workload performance in a public cloud. Finally, we build a performance model for a multi-tenant data service in the Amazon cloud. We find that a linear classifier is sufficient in most cases. On a few occasions, a linear classifier is unsuitable and non-linear modeling is required, which is time consuming. Consequently, we recommend that a linear classifier be used in training the performance model in the first instance. If the resulting model is unsatisfactory, then non-linear modeling can be carried out in the next step
Keywords
[ Medianet ] [ Tin2012-31518 ] [ Cloud ]
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BibTex Reference
@InProceedings{MrMpZfVPjl13TBP,
Author = {Mian, R. and Martin, P. and Zulkernine, F. and Vazquez-Poletti, J.L.},
Title = {Towards building performance models for data-intensive workloads in public clouds},
BookTitle = {4th ACM/SPEC International Conference on Performance Engineering},
Pages = {259--270},
Series = {ICPE '13},
Publisher = {ACM},
Year = {2013}
}