Towards Scheduling Evolving Applications

  • Cristian Klein
  • Christian Pérez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7155)


Most high-performance computing resource managers only allow applications to request a static allocation of resources. However, evolving applications have resource requirements which change (evolve) during their execution. Currently, such applications are forced to make an allocation based on their peak resource requirements, which leads to an inefficient resource usage. This paper studies whether it makes sense for resource managers to support evolving applications. It focuses on scheduling fully-predictably evolving applications on homogeneous resources, for which it proposes several algorithms and evaluates them based on simulations. Results show that resource usage and application response time can be significantly improved with short scheduling times.


Completion Time Schedule Algorithm Resource Usage Resource Requirement Resource Management System 
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.
    Feitelson, D.G., Rudolph, L., Schwiegelshohn, U.: Parallel Job Scheduling — A Status Report. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2004. LNCS, vol. 3277, pp. 1–16. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Lifka, D.: The ANL/IBM SP Scheduling System. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 1995. LNCS, vol. 949, pp. 295–303. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  3. 3.
    Mu’alem, A.W., Feitelson, D.G.: Utilization, predictability, workloads, and user runtime estimates in scheduling the IBM SP2 with backfilling. TPDS 12(6) (2001)Google Scholar
  4. 4.
    Plewa, T., Linde, T., Weirs, V.G. (eds.): Adaptive Mesh Refinement – Theory and Applications. Springer (2003)Google Scholar
  5. 5.
    Bouziane, H.L., Pérez, C., Priol, T.: A Software Component Model with Spatial and Temporal Compositions for Grid Infrastructures. In: Luque, E., Margalef, T., Benítez, D. (eds.) Euro-Par 2008. LNCS, vol. 5168, pp. 698–708. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Ribes, A., Caremoli, C.: Salome platform component model for numerical simulation. COMPSAC 2, 553–564 (2007)Google Scholar
  7. 7.
    Hungershofer, J.: On the combined scheduling of malleable and rigid jobs. In: SBAC-PAD (2004)Google Scholar
  8. 8.
    Buisson, J., Sonmez, O., Mohamed, H., et al.: Scheduling malleable applications in multicluster systems. Technical Report TR-0092, CoreGRID (2007)Google Scholar
  9. 9.
    El Maghraoui, K., Desell, T.J., Szymanski, B.K., Varela, C.A.: Dynamic malleability in iterative MPI applications. In: CCGRID (2007)Google Scholar
  10. 10.
    Cera, M.C., Georgiou, Y., Richard, O., Maillard, N., Navaux, P.O.A.: Supporting MPI malleable applications upon the OAR resource manager. In: COLIBRI (2009) Google Scholar
  11. 11.
    Buisson, J., Sonmez, O., Mohamed, H., et al.: Scheduling malleable applications in multicluster systems. Technical Report TR-0092, CoreGRID (2007)Google Scholar
  12. 12.
    Adaptive Computing Enterprises, Inc.: Moab workload manager administrator guide, version 6.0.2,
  13. 13.
    Cycles, C.: Lessons learned building a 4096-core cloud HPC supercomputer,

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Cristian Klein
    • 1
  • Christian Pérez
    • 1
  1. 1.INRIA/LIP, ENS de LyonFrance

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