Energy-Aware Scheduling of Flow Applications on Master-Worker Platforms

  • Jean-François Pineau
  • Yves Robert
  • Frédéric Vivien
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5704)


We consider the problem of scheduling an application composed of independent tasks on a fully heterogeneous master-worker platform with communication costs. We introduce a bi-criteria approach aiming at maximizing the throughput of the application while minimizing the energy consumed by participating resources. Assuming arbitrary super-linear power consumption laws, we investigate different models for energy consumption, with and without start-up overheads. Building upon closed-form expressions for the uniprocessor case, we derive optimal or asymptotically optimal solutions for both models.


Power Consumption Flow Application Unit Time Task Power Consumption Ratio Task Rejection 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ge, R., Feng, X., Cameron, K.W.: Performance-constrained distributed DVS scheduling for scientific applications on power-aware clusters. In: Proceedings of the 2005 ACM/IEEE conference on Supercomputing (SC 2005). IEEE CS, Los Alamitos (2005)Google Scholar
  2. 2.
    Skadron, K., Stan, M.R., Sankaranarayanan, K., Huang, W., Velusamy, S., Tarjan, D.: Temperature-aware microarchitecture: Modeling and implementation. ACM Transactions on Architecture and Code Optimization 1(1), 94–125 (2004)CrossRefGoogle Scholar
  3. 3.
    Casanova, H., Berman, F.: Parameter Sweeps on the Grid with APST. In: Hey, A., Berman, F., Fox, G. (eds.) Grid Computing: Making The Global Infrastructure a Reality. John Wiley, Chichester (2003)Google Scholar
  4. 4.
    Adler, M., Gong, Y., Rosenberg, A.L.: Optimal sharing of bags of tasks in heterogeneous clusters. In: Proceedings of SPAA, pp. 1–10. ACM Press, New York (2003)Google Scholar
  5. 5.
    Hong, B., Prasanna, V.: Distributed adaptive task allocation in heterogeneous computing environments to maximize throughput. In: Proceedings of IPDPS. IEEE CS, Los Alamitos (2004)Google Scholar
  6. 6.
    Hong, B., Prasanna, V.K.: Adaptive allocation of independent tasks to maximize throughput. IEEE TPDS 18(10), 1420–1435 (2007)Google Scholar
  7. 7.
    Pineau, J.F.: Communication-aware scheduling on heterogeneous master-worker platforms. PhD thesis, ENS Lyon (2008)Google Scholar
  8. 8.
    Hotta, Y., Sato, M., Kimura, H., Matsuoka, S., Boku, T., Takahashi, D.: Profile-based optimization of power performance by using dynamic voltage scaling on a PC cluster. In: Proceedings of IPDPS. IEEE CS, Los Alamitos (2006)Google Scholar
  9. 9.
    Albers, S., Fujiwara, H.: Energy-efficient algorithms for flow time minimization. ACM Transactions on Algorithms 3(4) (2007)Google Scholar
  10. 10.
    Bansal, N., Kimbrel, T., Pruhs, K.: Dynamic speed scaling to manage energy and temperature. In: Foundations of Computer Science (FoCS), pp. 520–529 (2004)Google Scholar
  11. 11.
    Bunde, D.P.: Power-aware scheduling for makespan and flow. In: Proceedings of SPAA, pp. 190–196. ACM Press, New York (2006)Google Scholar
  12. 12.
    Varatkar, G., Marculescu, R.: Communication-aware task scheduling and voltage selection for total systems energy minimization. In: International Conference on Computer-Aided Design (ICCAD). IEEE CS, Los Alamitos (2003)Google Scholar
  13. 13.
    Ishihara, T., Yasuura, H.: Voltage scheduling problem for dynamically variable voltage processors. In: Proceedings of ISLPED, pp. 197–202. ACM Press, New York (1998)Google Scholar
  14. 14.
    Okuma, T., Ishihara, T., Yasuura, H.: Real-time task scheduling for a variable voltage processor. In: Proceedings of ISSS. IEEE CS, Los Alamitos (1999)Google Scholar
  15. 15.
    Zhang, Y., Hu, X.S., Chen, D.Z.: Energy minimization of real-time tasks on variable voltage processors with transition energy overhead. In: Asia South Pacific Design Automation Conference (ASPDAC), pp. 65–70. ACM Press, New York (2003)Google Scholar
  16. 16.
    Aydin, H., Melhem, R., Mosse, D., Mejia-Alvarez, P.: Determining optimal processor speeds for periodic real-time tasks with different power characteristics. In: Proceedings of EMRTS, pp. 225–232. IEEE CS, Los Alamitos (2001)Google Scholar
  17. 17.
    Quan, G., Hu, X.: Energy efficient fixed-priority scheduling for real-time systems on variable voltage processors. In: Design Automation Conference, pp. 828–833 (2001)Google Scholar
  18. 18.
    Chan, H.L., Chan, W.T., Lam, T.W., Lee, L.K., Mak, K.S., Wong, P.W.H.: Energy efficient online deadline scheduling. In: Proceedings of SODA, pp. 795–804. SIAM, Philadelphia (2007)Google Scholar
  19. 19.
    Chen, J.J., Kuo, T.W., Yang, C.L., King, K.J.: Energy-efficient real-time task scheduling with task rejection. In: Proceedings of DATE, European Design and Automation Association, pp. 1629–1634 (2007)Google Scholar
  20. 20.
    Zhu, D., Melhem, R., Childers, B.R.: Scheduling with dynamic voltage/speed adjustment using slack reclamation in multiprocessor real-time systems. IEEE TPDS 14(7), 686–700 (2003)Google Scholar
  21. 21.
    Rusu, C., Melhem, R., Mossé, D.: Multi-version scheduling in rechargeable energy-aware real-time systems. Journal of Embedded Computing 1(2), 271–283 (2005)Google Scholar
  22. 22.
    Chen, J.-J., Thiele, L.: Energy-efficient task partition for periodic real-time tasks on platforms with dual processing elements. In: Proceedings of ICPADS. IEEE CS, Los Alamitos (2008)Google Scholar
  23. 23.
    Huang, T.Y., Tsai, Y.C., Chu, E.H.: A near-optimal solution for the heterogeneous multi-processor single-level voltage setup problem. In: Proceedings of IPDPS (2007)Google Scholar
  24. 24.
    Yu, Y., Prasanna, V.: Power-aware resource allocation for independent tasks in heterogeneous real-time systems. In: Proceedings of ICPADS, pp. 341–348 (2002)Google Scholar
  25. 25.
    Chen, J.J., Kuo, T.W.: Allocation cost minimization for periodic hard real-time tasks in energy-constrained dvs systems. In: International Conference on Computer-Aided Design (ICCAD), pp. 255–260. ACM, New York (2006)Google Scholar
  26. 26.
    Hsu, H.-R., Chen, J.-J., Kuo, T.-W.: Multiprocessor synthesis for periodic hard real-time tasks under a given energy constraint. In: Proceedings of DATE, pp. 1061–1066. European Design and Automation Association (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jean-François Pineau
    • 5
  • Yves Robert
    • 2
    • 3
    • 4
  • Frédéric Vivien
    • 1
    • 3
    • 4
  1. 1.INRIAFrance
  2. 2.ENS LyonFrance
  3. 3.Université de LyonFrance
  4. 4.LIP laboratoryENS Lyon–CNRS–INRIA–UCBL, LyonFrance
  5. 5.LIRMM laboratoryUMR 5506, CNRS–Université Montpellier 2France

Personalised recommendations