Virtual Machine Placement for Predictable and Time-Constrained Peak Loads

  • Wubin Li
  • Johan Tordsson
  • Erik Elmroth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7150)

Abstract

We present an approach to optimal virtual machine placement within datacenters for predicable and time-constrained load peaks. A method for optimal load balancing is developed, based on binary integer programming. For tradeoffs between quality of solution and computation time, we also introduce methods to pre-process the optimization problem before solving it. Upper bound based optimizations are used to reduce the time required to compute a final solution, enabling larger problems to be solved. For further scalability, we also present three approximation algorithms, based on heuristics and/or greedy formulations. The proposed algorithms are evaluated through simulations based on synthetic data sets. The evaluation suggests that our algorithms are feasible, and that these can be combined to achieve desired tradeoffs between quality of solution and execution time.

Keywords

Cloud Computing Virtual Machine Placement Binary Integer Programming Off-line Scheduling Load Balancing 

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References

  1. 1.
    Gurobi Optimization (2010), http://www.gurobi.com (visited October 2011)
  2. 2.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Communications of the ACM 53, 50–58 (2010)CrossRefGoogle Scholar
  3. 3.
    Bobroff, N., Kochut, A., Beaty, K.A.: Dynamic placement of virtual machines for managing sla violations. In: Proceedings of the 10th IFIP/IEEE International Symposium on Integrated Network Management, IM 2007, pp. 119–128 (2007)Google Scholar
  4. 4.
    den Bossche, R.V., Vanmechelen, K., Broeckhove, J.: Cost-optimal scheduling in hybrid iaas clouds for deadline constrained workloads. In: Proceedings of the 3rd IEEE International Conference on Cloud Computing, pp. 228–235 (2010)Google Scholar
  5. 5.
    Breitgand, D., Marashini, A., Tordsson, J.: Policy-driven service placement optimization in federated clouds. Tech. rep., IBM Haifa Labs (2011)Google Scholar
  6. 6.
    Chambers, C., Feng, W., Sahu, S., Saha, D.: Measurement-based characterization of a collection of on-line games. In: Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement, IMC 2005, pp. 1–14 (2005)Google Scholar
  7. 7.
    Chen, C., Tsai, K.: The server reassignment problem for load balancing in structured p2p systems. IEEE Transactions on Parallel and Distributed Processing 19(2), 234–246 (2008)CrossRefMATHGoogle Scholar
  8. 8.
    Ferrer, A.J., Hernádez, F., Tordsson, J., Elmroth, E., Ali-Eldin, A., Zsigri, C., Sirvent, R., Guitart, J., Badia, R.M., Djemame, K., Ziegler, W., Dimitrakos, T., Nair, S.K., Kousiouris, G., Konstanteli, K., Varvarigou, T., Hudzia, B., Kipp, A., Wesner, S., Corrales, M., Forgó, N., Sharif, T., Sheridan, C.: OPTIMIS: A holistic approach to cloud service provisioning. Future Generation Computer Systems 28(1), 66–77 (2012)CrossRefGoogle Scholar
  9. 9.
    Fourer, R., Gay, D.M., Kernighan, B.W.: AMPL: A Modeling Language for Mathematical Programming. Duxbury Press (November 2002)Google Scholar
  10. 10.
    Kousiouris, G., Cucinotta, T., Varvarigou, T.: The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks. Journal of Systems and Software 84(8), 1270–1291 (2011)CrossRefGoogle Scholar
  11. 11.
    Lampe, U., Siebenhaar, M., Schuller, D., Steinmetz, R.: A cloud-oriented broker for cost-minimal software service distribution. In: Proceedings of the 2nd ServiceWave Workshop on Optimizing Cloud Services (2011)Google Scholar
  12. 12.
    Li, W., Tordsson, J., Elmroth, E.: Modeling for Dynamic Cloud Scheduling via Migration of Virtual Machines. In: Proceedings of the 3rd IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2011), pp. 163–171 (2011)Google Scholar
  13. 13.
    Mell, P., Grance, T.: The NIST definition of cloud computing. National Institute of Standards and Technology, NIST (2011)Google Scholar
  14. 14.
    Shachnai, H., Tamir, T.: On two class-constrained versions of the multiple knapsack problem. Algorithmica 29(3), 442–467 (2001)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Tang, C., Steinder, M., Spreitzer, M., Pacifici, G.: A scalable application placement controller for enterprise data centers. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 331–340. ACM (2007)Google Scholar
  16. 16.
    Tian, C., Jiang, H., Iyengar, A., Liu, X., Wu, Z., Chen, J., Liu, W., Wang, C.: Improving application placement for cluster-based web applications. IEEE Transactions on Network and Service Management 8(2), 104–115 (2011)CrossRefGoogle Scholar
  17. 17.
    Tordsson, J., Montero, R., Moreno-Vozmediano, R., Llorente, I.: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Generation Computer Systems 28(2), 358–367 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wubin Li
    • 1
  • Johan Tordsson
    • 1
  • Erik Elmroth
    • 1
  1. 1.Department of Computing Science and HPC2NUmeå UniversityUmeåSweden

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