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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 Smirni
  • Walter Binder
Article

Abstract

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.

Keywords

Performance Consolidation Multicores Prediction models Asymptotic analysis 

Notes

Acknowledgements

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.

References

  1. 1.
    Ansaloni, D., Chen, L.Y., Smirni, E., Binder, W.: Model-driven consolidation of Java workloads on multicores. In: Proceedings of IEEE/IFIP International Conference on Dependable Systems and Networks (DSN-PDS), pp. 1–12 (2012) CrossRefGoogle Scholar
  2. 2.
    Apparao, P., Iyer, R., Zhang, X., Newell, D., Adelmeyer, T.: Characterization & analysis of a server consolidation benchmark. In: Proceedings of VEE, pp. 21–30 (2008) Google Scholar
  3. 3.
    Balbo, G., Serazzi, G.: Asymptotic analysis of multiclass closed queueing networks: common bottleneck. Perform. Eval. 26(1), 51–72 (1996) CrossRefzbMATHGoogle Scholar
  4. 4.
    Blackburn, S., Garner, R., Hoffman, C., Khan, A., McKinley, K., Bentzur, R., Diwan, A., Feinberg, D., Frampton, D., Guyer, S., Hirzel, M., Hosking, A., Jump, M., Lee, H., Moss, J., Phansalkar, A., Stefanović, D., von Dincklage, D., Wiedermann, B.: The DaCapo benchmarks: Java benchmarking development and analysis. In: Proceedings of OOPSLA, pp. 169–190 (2006) Google Scholar
  5. 5.
    Chen, J., John, L., Kaseridis, D.: Modeling program resource demand using inherent program characteristics. In: Proceedings of SIGMETRICS, pp. 1–12 (2011) Google Scholar
  6. 6.
    Chen, L.Y., Ansaloni, D., Smirni, E., Yokokawa, A., Binder, W.: Achieving application-centric performance targets via consolidation on multicores: myth or reality? In: Proceedings of the IEEE/IFIP International Conference on Dependable Systems and Networks (HPDC), pp. 37–48 (2012) Google Scholar
  7. 7.
    Chen, L.Y., Das, A., Qin, W., Sivasubramaniam, A., Wang, Q., Harper, R., Morris, B.: Consolidating clients on back-end servers with co-location and frequency control. ACM SIGMETRICS Perform. Eval. Rev. 34, 383–384 (2006) CrossRefGoogle Scholar
  8. 8.
    Dey, T., Wang, W., Davidson, J., Soffa, M.: Characterizing multi-threaded applications based on shared-resource contention. In: Proceedings of ISPASS, pp. 76–86 (2011) Google Scholar
  9. 9.
    Govindan, S., Liu, J., Kansal, A., Sivasubramaniam, A.: Cuanta: quantifying effects of shared on-chip resource interference for consolidated virtual machines. In: Proceedings of the ACM Symposium on Cloud Computing (SOCC) (2011) Google Scholar
  10. 10.
    Hauswirth, M., Sweeney, P., Diwan, A., Hind, M.: Vertical profiling: understanding the behavior of object-oriented applications. In: Proceedings of OOPSLA, pp. 251–269 (2004) Google Scholar
  11. 11.
    Hines, M.R., Gordon, A., Silva, M., da Silva, D., Ryu, K.D., Ben-Yehuda, M.: Applications know best: performance-driven memory overcommit with ginkgo. Tech. rep., IBM (2011) Google Scholar
  12. 12.
    Ïpek, E., McKee, S., Caruana, R., de Supinski, B., Schulz, M.: Efficiently exploring architectural design spaces via predictive modeling. In: Proceedings of ASPLOS, pp. 195–206 (2006) Google Scholar
  13. 13.
    Jerger, N., Vantreaseand, D., Lipast, M.: An evaluation of server consolidation workloads for multi-core designs. In: Proceedings of IISWC, pp. 47–56 (2007) Google Scholar
  14. 14.
    Knauerhase, R., Brett, P., Hohlt, B., Li, T., Hahn, S.: Using OS observations to improve performance in multicore systems. IEEE MICRO 28, 54–66 (2008) CrossRefGoogle Scholar
  15. 15.
    Koh, Y., Knauerhase, R.C., Brett, P., Bowman, M., Wen, Z., Pu, C.: An analysis of performance interference effects in virtual environments. In: Proceedings of ISPASS, pp. 200–209 (2007) Google Scholar
  16. 16.
    Lee, B., Collins, J., Wang, H., Brooks, D.: CPR: composable performance regression for scalable multiprocessor models. In: Proceedings of Micro, pp. 270–281. IEEE Computer Society, Washington (2008) Google Scholar
  17. 17.
    Lipsky, L., Lieu, C., Tehranipour, A., van de Liefvoort, A.: On the asymptotic behavior of time-sharing systems. Commun. ACM 25(10), 707–714 (1982) CrossRefzbMATHGoogle Scholar
  18. 18.
    Menascé, D., Almeida, V., Dowdy, L.: Capacity Planning and Performance Modeling: From Mainframes to Client-Server Systems. Prentice Hall, New York (1994) Google Scholar
  19. 19.
    Meng, X., Isci, C., Kephart, J., Zhang, L., Bouillet, E., Pendarakis, D.: Efficient resource provisioning in compute clouds via VM multiplexing. In: Proceedings of ICAC, pp. 11–20 (2010) Google Scholar
  20. 20.
    Mi, N., Casale, G., Cherkasova, L., Smirni, E.: Burstiness in multi-tier applications: symptoms, causes, and new models. In: Proceedings of Middleware, pp. 265–286 (2008) Google Scholar
  21. 21.
    Nathuji, R., Kansal, A., Ghaffarkhah, A.: Q-clouds: managing performance interference effects for QoS-aware clouds. In: Proceedings of EuroSys, pp. 237–250 (2010) Google Scholar
  22. 22.
    Reiser, M., Lavenberg, S.S.: Mean-value analysis of closed multichain queuing networks. J. ACM 27, 313–322 (1980) CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Sharifi, A., Srikantaiah, S., Mishra, A., Kandemir, M., Das, C.: METE: meeting end-to-end QoS in multicores through system-wide resource management. In: Proceedings of SIGMETRICS, pp. 13–24 (2011) Google Scholar
  24. 24.
    Song, X., Chen, H., Chen, R., Wang, Y., Zang, B.: A case for scaling applications to many-core with OS clustering. In: Proceesings of EuroSys, pp. 61–76 (2011) Google Scholar
  25. 25.
    Tallent, N., Mellor-Crummey, J.: Effective performance measurement and analysis of multithreaded applications. SIGPLAN Not. 44, 229–240 (2009) CrossRefGoogle Scholar
  26. 26.
    Urgaonkar, B., Pacifici, G., Spreitzer, P.S.M., Tantawi, A.: An analytical model for multi-tier Internet services and its applications. In: Proceedings of SIGMETRICS, pp. 291–302 (2005) Google Scholar
  27. 27.
    Wood, T., Cherkasova, L., Ozonat, K., Shenoy, P.: Profiling and modeling resource usage of virtualized applications. In: Proceedings of Middleware, pp. 366–387 (2008) Google Scholar
  28. 28.
    Wood, T., Shenoy, P., Venkataramani, A., Yousif, M.: Sandpiper: black-box and gray-box resource management for virtual machines. Comput. Netw. 53, 2923–2938 (2009) CrossRefzbMATHGoogle Scholar
  29. 29.
    Zhang, Q., Cherkasova, L., Mi, N., Smirni, E.: A regression-based analytic model for capacity planning of multi-tier applications. Clust. Comput. 11, 197–211 (2008) CrossRefGoogle Scholar
  30. 30.
    Zhuravlev, S., Blagodurov, S., Fedorova, A.: Addressing shared resource contention in multicore processors via scheduling. In: Proceesings of ASPLOS, pp. 129–142 (2010) Google Scholar

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
  • 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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