On the Utility of DVFS for Power-Aware Job Placement in Clusters

  • Jean-Marc Pierson
  • Henri Casanova
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6852)


Placing compute jobs on clustered hosts in a way that optimizes both performance and power consumption has become a crucial issue. Most solutions to the power-aware job placement problem boil down to consolidating workload on a small number of hosts so as to reduce power consumption which achieving acceptable performance levels. The question we investigate in this paper is whether the capabilities provided by DVFS, i.e., the ability to configure a host in one of several power consumption modes, leads to improved solutions. We formalize the problem so that a bound on the optimal solution can be computed. We then study how the optimal, if it can be computed, and its bound vary across scenarios in which hosts provide various degrees of DVFS capabilities. We rely on a DVFS model that we instantiate based on real-world experiments. Our approach thus quantifies the potential improvements that hypothetical job placement algorithms can hope to achieve by exploiting DVFS capabilities.


Power Consumption Power Mode Cluster Node Resource Dimension Dynamic Voltage Scaling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Benoit, A., Renaud Goud, P., Robert, Y.: Sharing resources for performance and energy optimization of concurrent streaming applications,, RR-LIP-2010-05
  2. 2.
    Berral, J.L., Goiri, ĺ., Nou, R., Julià, F., Guitart, J., Gavaldà, R., Torres, J.: Towards energy-aware scheduling in data centers using machine learning. In: ACM eEnergy. University of Passau, Germany (2010)Google Scholar
  3. 3.
    Bolze, R., Cappello, F., Caron, E., Daydé, M.J., Desprez, F., Jeannot, E., Jégou, Y., Lanteri, S., Leduc, J., Melab, N., Mornet, G., Namyst, R., Primet, P., Quétier, B., Richard, O., Talbi, E.-G., Touche, I.: Grid’5000: A large scale and highly reconfigurable experimental grid testbed. IJHPCA 20(4), 481–494 (2006)Google Scholar
  4. 4.
    Borgetto, D., Casanova, H., Costa, G.D., Pierson, J.M.: Energy-aware service allocation. Tech. Rep. IRIT/RT-2010-7-FR, IRIT (October 2010)Google Scholar
  5. 5.
    Borgetto, D., Da Costa, G., Pierson, J.-M., Sayah, A.: Energy-Aware Resource Allocation. In: Proc. of the Energy Efficient Grids, Clouds and Clusters Workshop (E2GC2). IEEE, Los Alamitos (2009)Google Scholar
  6. 6.
    Carrera, D., Steinder, M., Whalley, I., Torres, J., Ayguadé, E.: Utility-based placement of dynamic web applications with fairness goals. In: IEEE Network Operations and Management Symposium, pp. 9–16 (2008)Google Scholar
  7. 7.
    Da Costa, G., Dias De Assuncao, M., Gelas, J.P., Georgiou, Y., LefËvre, L., Orgerie, A.C., Pierson, J.M., Richard, O., Sayah, A.: Multi-Facet Approach to Reduce Energy Consumption in Clouds and Grids: The GREEN-NET Framework. In: ACM/IEEE International Conference on Energy-Efficient Computing and Networking (e-Energy), Passau, Germany, pp. 95–104. ACM, New York (2010)CrossRefGoogle Scholar
  8. 8.
    Doyle, R.P., Chase, J.S., Asad, O.M., Jin, W., Vahdat, A.M.: Model-based resource provisioning in a web service utility. In: Proc. of the USENIX Symposium on Internet Technologies and Systems (2003)Google Scholar
  9. 9.
    Etinski, M., Corbalan, J., Labarta, J., Valero, M.: Utilization driven power-aware parallel job scheduling. Computer Science - Research and Development 25, 207–216 (2010), doi:10.1007/s00450-010-0129-xCrossRefGoogle Scholar
  10. 10.
    Etinski, M., Corbalan, J., Labarta, J., Valero, M., Veidenbaum, A.: Power-aware load balancing of large scale mpi applications. In: IPDPS 2009: Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing, pp. 1–8. IEEE Computer Society, Washington, DC, USA (2009)CrossRefGoogle Scholar
  11. 11.
    Fan, X., Weber, W.D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. In: Proceedings of the 34th Annual International Symposium on Computer Architecture, ISCA 2007, pp. 13–23. ACM, New York (2007)Google Scholar
  12. 12.
    Gandhi, A., Harchol-Balter, M., Das, R., Lefurgy, C.: Optimal power allocation in server farms. In: SIGMETRICS/Performance, pp. 157–168. ACM, New York (2009)Google Scholar
  13. 13.
    Ge, R., Feng, X., Cameron, K.W.: Performance-constrained distributed dvs scheduling for scientific applications on power-aware clusters. In: SC 2005: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, p. 34. IEEE Computer Society, Washington, DC, USA (2005)Google Scholar
  14. 14.
    Gmach, D., Rolia, J., Cherkasova, L., Kemper, A.: Workload Analysis and Demand Prediction of Enterprise Data Center Applications. In: Proc of the 10th IEEE Intnl. Symp. on Workload Characterization, September 2007, pp. 171–180 (2007)Google Scholar
  15. 15.
    Hermenier, F., Lorca, X., Menaud, J.M., Muller, G., Lawall, J.: Entropy: a Consolidation Manager for Clusters. Research Report RR-6639, INRIA (2008)Google Scholar
  16. 16.
    Hoyer, M., Schröder, K., Nebel, W.: Statistical static capacity management in virtualized data centers supporting fine grained QoS specification. In: ACM eEnergy. University of Passau, Germany (2010)Google Scholar
  17. 17.
    Kamitsos, Y., Andrew, L.L.H., Kim, H., Chiang, M.: Optimal Sleep Patterns for Serving Delay Tolerant Jobs. In: ACM eEnergy. University of Passau, Germany (2010), Google Scholar
  18. 18.
    Lawson, B., Smirni, E.: Power-aware resource allocation in high-end systems via online simulation. In: Proceedings of the 19th Annual international Conference on Supercomputing, ICS 2005, pp. 229–238. ACM, New York (2005)Google Scholar
  19. 19.
    Legrand, A., Su, A., Vivien, F.: Minimizing the Stretch when Scheduling Flows of Divisible Requests. Journal of Scheduling 11(5), 381–404 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Niyato, D., Chaisiri, S., Sung, L.B.: Optimal power management for server farm to support green computing. In: CCGRID 2009: Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 84–91. IEEE Computer Society, Washington, DC, USA (2009)Google Scholar
  21. 21.
    Petrucci, V., Loques, O., Mossé, D.: A Dynamic Optimization Model for Power and Performance Management of Virtualized Clusters. In: ACM eEnergy. University of Passau, Germany (2010)Google Scholar
  22. 22.
    Rodero, I., Jamarillo, J., Quiroz, A., Parashar, M., Guim, F., Poole, S.: Energy-efficient application-aware online provisioning for virtualized clouds and data centers. In: First IEEE Sponsored International Green Computing Conference (2010)Google Scholar
  23. 23.
    Stillwell, M., Schanzenbach, D., Vivien, F., Casanova, H.: Resource allocation algorithms for virtualized service hosting platforms. Journal of Parallel and Distributed Computing 70(9), 962–974 (2010)CrossRefzbMATHGoogle Scholar
  24. 24.
    Urgaonkar, B., Shenoy, P., Roscoe, T.: Resource Overbooking and Application Profiling in Shared Hosting Platforms. SIGOPS Oper. Syst. Rev. 36(SI), 239–254 (2002)CrossRefGoogle Scholar
  25. 25.
    Wang, Z., Tolia, N., Bash, C.: Opportunities and challenges to unify workload, power, and cooling management in data centers. SIGOPS Oper. Syst. Rev. 44, 41–46 (2010)CrossRefGoogle Scholar
  26. 26.
    Zhu, X., Young, D., Watson, B.J., Wang, Z., Rolia, J., Singhal, S., McKee, B., Hyser, C., Gmach, D., Gardner, R., Christian, T., Cherkasova, L.: 1000 Islands: Integrated Capacity and Workload Management for the Next Generation Data Center. In: Proceedings of the International Conference on Autonomic Computing (ICAC 2008), pp. 172–181 (June 2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jean-Marc Pierson
    • 1
  • Henri Casanova
    • 2
  1. 1.IRITUniversity of ToulouseToulouseFrance
  2. 2.Dept. of Information and Computer SciencesUniversity of Hawai‘i at ManoaHonolulu, Hawai‘iU.S.A.

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