Consolidation and Replication of VMs Matching Performance Objectives

  • Marco Gribaudo
  • Pietro Piazzolla
  • Giuseppe Serazzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7314)


The users of actual computing infrastructures allowing the resource provision (such as clouds) are often asked to decide about the proper amount of equipment (virtual machines, VMs) required to execute their requests while satisfying a set of performance objectives. These types of decisions are particularly difficult since the direct correlation between the resources allocated and the performance offered is influenced by a number of factors such as the characteristic of the different class of requests, the capacity of the resources, the workload sharing the same physical hardware, the dynamic variation of the mix of requests of the different classes in concurrent execution. In this paper we derive the impact on several performance indexes by two popular techniques, namely, consolidation and replication, adopted in virtual computing infrastructures. In particular we present an analytical model to determine the best consolidation or replication options that matches given performance objectives specified through a set of constraints.


Virtual Machine Arrival Rate Performance Objective Service Demand Physical Machine 
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.
    Balbo, G., Serazzi, G.: Asymptotic analysis of multiclass closed queueing networks: Common bottlenecks. Performance Evaluation 26(1), 51–72 (1996)zbMATHCrossRefGoogle Scholar
  2. 2.
    Benevenuto, F., Fernandes, C., Santos, M., Almeida, V., Almeida, J., Janakiraman, G(J.), Santos, J.R.: Performance Models for Virtualized Applications. In: Min, G., Di Martino, B., Yang, L.T., Guo, M., Rünger, G. (eds.) ISPA 2006. LNCS, vol. 4331, pp. 427–439. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Bennani, M., Menascé, D.: Resource allocation for autonomic data centers using analytic performance models. In: Autonomic Computing, ICAC 2005, pp. 229–240 (June 2005)Google Scholar
  4. 4.
    Bertoli, M., Casale, G., Serazzi, G.: Java modelling tools: an open source suite for queueing network modelling and workload analysis. In: Proc. of the 3rd Conf. on Quantitative Evaluation of Systems (QEST), pp. 119–120. IEEE (2006)Google Scholar
  5. 5.
    Bobroff, N., Kochut, A., Beaty, K.: Dynamic placement of virtual machines for managing sla violations. In: 10th IFIP/IEEE International Symposium on Integrated Network Management, IM 2007, pp. 119–128 (21, 2007-yearly 25, 2007)Google Scholar
  6. 6.
    Bushehrian, O.: The Application of FSP Models in Automatic Optimization of Software Deployment. In: Al-Begain, K., Balsamo, S., Fiems, D., Marin, A. (eds.) ASMTA 2011. LNCS, vol. 6751, pp. 43–54. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Curino, C., Jones, E.P., Madden, S., Balakrishnan, H.: Workload-aware database monitoring and consolidation. In: Proc. of the International Conference on Management of Data, SIGMOD 2011, pp. 313–324. ACM, New York (2011)CrossRefGoogle Scholar
  8. 8.
    Ganapathi, A., Chen, Y., Fox, A., Katz, R., Patterson, D.: Statistics-driven workload modeling for the cloud. In: 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW), pp. 87–92 (March 2010)Google Scholar
  9. 9.
    Jackson, J.R.: Jobshop-like queueing systems. Management Science 10(1), 131–142 (1963)CrossRefGoogle Scholar
  10. 10.
    Khanna, G., Beaty, K.A., Kar, G., Kochut, A.: Application performance management in virtualized server environments. In: NOMS, pp. 373–381 (2006)Google Scholar
  11. 11.
    Kokkinos, P., Christodoulopoulos, K., Kretsis, A., Varvarigos, E.: Data consolidation: A task scheduling and data migration technique for grid networks. In: Proc. of the 8th IEEE Int. Symposium on Cluster Computing and the Grid, pp. 722–727. IEEE Computer Society, Washington, DC (2008)Google Scholar
  12. 12.
    Lazowska, E.D., Zahorjan, J., Graham, G.S., Sevcik, K.C.: Quantitative System Performance. Prentice-Hall (1984)Google Scholar
  13. 13.
    Menascé, D.A.: Virtualization: Concepts, applications, and performance modeling (2005)Google Scholar
  14. 14.
    Mi, N., Casale, G., Cherkasova, L., Smirni, E.: Sizing multi-tier systems with temporal dependence: benchmarks and analytic models. J. Internet Services and Applications 1(2), 117–134 (2010)CrossRefGoogle Scholar
  15. 15.
    Padala, P., Shin, K.G., Zhu, X., Uysal, M., Wang, Z., Singhal, S., Merchant, A., Salem, K.: Adaptive control of virtualized resources in utility computing environments. In: Proc. of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems, EuroSys 2007, pp. 289–302. ACM, New York (2007)Google Scholar
  16. 16.
  17. 17.
  18. 18.
    Watson, B.J., Marwah, M., Gmach, D., Chen, Y., Arlitt, M., Wang, Z.: Probabilistic performance modeling of virtualized resource allocation. In: Proc. of the 7th International Conference on Autonomic Computing, ICAC 2010, pp. 99–108. ACM, NY (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marco Gribaudo
    • 1
  • Pietro Piazzolla
    • 1
  • Giuseppe Serazzi
    • 1
  1. 1.Dip. di Elettronica e InformazionePolitecnico di MilanoMilanoItaly

Personalised recommendations