Cluster Computing

, Volume 17, Issue 1, pp 19–37 | Cite as

What to expect when you are consolidating: effective prediction models of application performance on multicores

  • Lydia Y. Chen
  • Giuseppe Serazzi
  • Danilo Ansaloni
  • Evgenia SmirniEmail author
  • Walter Binder


Consolidation of multiple applications with diverse and changing resource requirements is common in multicore systems as hardware resources are abundant. As opportunities for better system usage become ample, so are opportunities to degrade individual application performances due to unregulated performance interference between applications and system resources. Can we predict a performance region within which application performance is expected to lie under different consolidations? Alternatively, can we maximize resource utilization while maintaining individual application performance targets? In this work we provide a methodology that offers answers to the above difficult questions by constructing a queueing-theory based tool that can be used to accurately predict application scalability on multicores. The tool can also provide the optimal consolidation suggestions to maximize system resource utilization while meeting application performance targets. The proposed methodology is based on asymptotic analysis that can quickly provide a range of performance values that the user should expect under various consolidation scenarios. In addition, when more accurate performance forecasting is needed, the methodology can provide more accurate predictions using approximate mean value analysis. The methodology is light-weight as it relies on capturing application resource demands using standard system monitoring, via non-intrusive low-level measurements.

We evaluate our approach on an IBM Power7 system using the DaCapo and SPECjvm2008 benchmark suites. From 900 different consolidations of application instances, our tool accurately predicts the average iteration time of collocated applications with an average error below 9 per cent. Experimental and analytical results are in excellent agreement, confirming the robustness of the proposed methodology in suggesting the best consolidations that meet given performance objectives of individual applications while maximizing system resource utilization.


Performance Consolidation Multicores Prediction models Asymptotic analysis 



This work has been supported by IBM and the Swiss National Science Foundation (project 200021 141002). Part of this work was conducted while Danilo Ansaloni was on an internship and Evgenia Smirni was on sabbatical leave at the IBM Zurich Research Laboratory. Evgenia Smirni is partially supported by NSF grants CCF-0937925 and CCF-1218758. A preliminary version [6] of this paper appeared in the 21st International Symposium on High-Performance Parallel and Distributed Computing, HPDC’12, Delft, Netherlands, June 18–22, 2012.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Lydia Y. Chen
    • 1
  • Giuseppe Serazzi
    • 2
  • Danilo Ansaloni
    • 3
  • Evgenia Smirni
    • 4
    Email author
  • Walter Binder
    • 3
  1. 1.IBM Zurich LabZurichSwitzerland
  2. 2.Politecnico di MilanoMilanoItaly
  3. 3.University of LuganoLuganoSwitzerland
  4. 4.College of William and MaryWiiliamsburgUSA

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