Find your best match: predicting performance of consolidated workloads

Authors:
Danilo Ansaloni;Lydia Y. Chen;Evgenia Smirni;Akira Yokokawa;Walter Binder
Affiliations:
University of Lugano, Lugano, Switzerland;IBM Research Zürich Laboratory, Rüschlikon, Switzerland;College of William and Mary, Williamsburg, VA, USA;University of Lugano, Lugano, Switzerland;University of Lugano, Lugano, Switzerland
Venue:
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
Year:
2012

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Abstract

Modern multicore platforms allow system administrators to reduce the costs of the IT infrastructure by consolidating heterogeneous workloads on the same physical machine. To this end, it is important to develop efficient profiling techniques and accurate performance predictions to avoid violating service level objectives. In this work we present Tresa, a novel tool to automatically characterize workloads and accurately estimate the execution time of different consolidations. These results can be used to optimize consolidations depending on service-level objectives.