Bin Repacking Scheduling in Virtualized Datacenters

  • Fabien Hermenier
  • Sophie Demassey
  • Xavier Lorca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6876)

Abstract

A datacenter can be viewed as a dynamic bin packing system where servers host applications with varying resource requirements and varying relative placement constraints. When those needs are no longer satisfied, the system has to be reconfigured. Virtualization allows to distribute applications into Virtual Machines (VMs) to ease their manipulation. In particular, a VM can be freely migrated without disrupting its service, temporarily consuming resources both on its origin and destination.

We introduce the Bin Repacking Scheduling Problem in this context. This problem is to find a final packing and to schedule the transitions from a given initial packing, accordingly to new resource and placement requirements, while minimizing the average transition completion time. Our CP-based approach is implemented into Entropy, an autonomous VM manager which detects reconfiguration needs, generates and solves the CP model, then applies the computed decision. CP provides the awaited flexibility to handle heterogeneous placement constraints and the ability to manage large datacenters with up to 2,000 servers and 10,000 VMs.

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References

  1. 1.
    Anderson, E., Hall, J., Hartline, J., Hobbes, M., Karlin, A., Saia, J., Swaminathan, R., Wilkes, J.: Algorithms for data migration. Algorithmica 57(2), 349–380 (2010)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Beldiceanu, N., Carlsson, M., Rampon, J.: Global constraint catalog. Tech. Rep. 2007, SICS (2010), http://www.emn.fr/z-info/sdemasse/gccat/
  3. 3.
    Dhyani, K., Gualandi, S., Cremonesi, P.: A constraint programming approach for the service consolidation problem. In: Lodi, A., Milano, M., Toth, P. (eds.) CPAIOR 2010. LNCS, vol. 6140, pp. 97–101. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Fukunaga, A.: Search spaces for min-perturbation repair. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 383–390. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-completeness. WH Freeman & Co., New York (1979)MATHGoogle Scholar
  6. 6.
    Hermenier, F., Lorca, X., Menaud, J.M., Muller, G., Lawall, J.: Entropy: a consolidation manager for clusters. In: VEE 2009, pp. 41–50. ACM, New York (2009)Google Scholar
  7. 7.
    Simonis, H., Cornelissens, T.: Modelling producer/consumer constraints. In: Montanari, U., Rossi, F. (eds.) CP 1995. LNCS, vol. 976, pp. 449–462. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  8. 8.
    Sirdey, R., Carlier, J., Kerivin, H., Nace, D.: On a resource-constrained scheduling problem with application to distributed systems reconfiguration. European Journal of Operational Research 183(2), 546–563 (2007)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    VMWare: Resource Management with VMWare DRS. Tech. rep. (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fabien Hermenier
    • 1
  • Sophie Demassey
    • 2
  • Xavier Lorca
    • 2
  1. 1.School of ComputingUniversity of UtahUSA
  2. 2.TASC project, Mines Nantes-INRIA, LINA CNRS UMR 6241France

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