Interplay of Virtual Machine Selection and Virtual Machine Placement

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9846)

Abstract

Previous work on optimizing resource provisioning in virtualized environments focused either on mapping virtual machines to physical machines (i.e., virtual machine placement) or mapping computational tasks to virtual machines (i.e., virtual machine selection). In this paper, we investigate how these two optimization problems influence each other. Our study shows that exploiting knowledge about the physical machines and about the virtual machine placement algorithm in the course of virtual machine selection leads to better overall results than considering the two problems in isolation.

Notes

Acknowledgments

A part of this work was carried out when Z.Á. Mann was with Budapest University of Technology and Economics. This work was partially supported by the Hungarian Scientific Research Fund (Grant Nr. OTKA 108947) and the European Union’s 7th Framework Programme (FP7/2007–2013) under grant agreement 610802 (CloudWave).

References

  1. 1.
    Anthesis Group: 30% of servers are sitting “comatose” (2015). http://anthesisgroup.com/30-of-servers-are-sitting-comatose/
  2. 2.
    Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28, 755–768 (2012)CrossRefGoogle Scholar
  3. 3.
    Beloglazov, A., Buyya, R.: Energy efficient allocation of virtual machines in cloud data centers. In: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 577–578 (2010)Google Scholar
  4. 4.
    Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. Pract. Exp. 24(13), 1397–1420 (2012)CrossRefGoogle Scholar
  5. 5.
    Biran, O., Corradi, A., Fanelli, M., Foschini, L., Nus, A., Raz, D., Silvera, E.: A stable network-aware VM placement for cloud systems. In: Proceedings of the 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID 2012), pp. 498–506. IEEE Computer Society (2012)Google Scholar
  6. 6.
    Bittencourt, L.F., Madeira, E.R., da Fonseca, N.L.: Scheduling in hybrid clouds. IEEE Commun. Mag. 50(9), 42–47 (2012)CrossRefGoogle Scholar
  7. 7.
    Breitgand, D., Epstein, A.: SLA-aware placement of multi-virtual machine elastic services in compute clouds. In: 12th IFIP/IEEE International Symposium on Integrated Network Management, pp. 161–168 (2011)Google Scholar
  8. 8.
    Candeia, D., Araújo, R., Lopes, R., Brasileiro, F.: Investigating business-driven cloudburst schedulers for e-science bag-of-tasks applications. In: 2nd IEEE International Conference on Cloud Computing Technology and Science, pp. 343–350 (2010)Google Scholar
  9. 9.
    Chang, C.R., Wu, J.J., Liu, P.: An empirical study on memory sharing of virtual machines for server consolidation. In: IEEE 9th International Symposium on Parallel and Distributed Processing with Applications, pp. 244–249 (2011)Google Scholar
  10. 10.
    Ganesan, R., Sarkar, S., Narayan, A.: Analysis of SaaS business platform workloads for sizing and collocation. In: IEEE 5th International Conference on Cloud Computing (CLOUD), pp. 868–875 (2012)Google Scholar
  11. 11.
    Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79, 1230–1242 (2013)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Genez, T.A.L., Bittencourt, L.F., Madeira, E.R.M.: Workflow scheduling for SaaS/PaaS cloud providers considering two SLA levels. In: Network Operations and Management Symposium (NOMS), pp. 906–912. IEEE (2012)Google Scholar
  13. 13.
    Gmach, D., Rolia, J., Cherkasova, L., Kemper, A.: Resource pool management: reactive versus proactive or let’s be friends. Comput. Netw. 53(17), 2905–2922 (2009)CrossRefGoogle Scholar
  14. 14.
    Guazzone, M., Anglano, C., Canonico, M.: Exploiting VM migration for the automated power and performance management of green cloud computing systems. In: Huusko, J., de Meer, H., Klingert, S., Somov, A. (eds.) E2DC 2012. LNCS, vol. 7396, pp. 81–92. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Hoenisch, P., Hochreiner, C., Schuller, D., Schulte, S., Mendling, J., Dustdar, S.: Cost-efficient scheduling of elastic processes in hybrid clouds. In: IEEE 8th International Conference on Cloud Computing, pp. 17–24 (2015)Google Scholar
  16. 16.
    HP: Power efficiency and power management in HP ProLiant servers (2012). http://h10032.www1.hp.com/ctg/Manual/c03161908.pdf
  17. 17.
    Jung, G., Hiltunen, M.A., Joshi, K.R., Schlichting, R.D., Pu, C.: Mistral: dynamically managing power, performance, and adaptation cost in cloud infrastructures. In: IEEE 30th International Conference on Distributed Computing Systems, pp. 62–73 (2010)Google Scholar
  18. 18.
    Lampe, U., Siebenhaar, M., Hans, R., Schuller, D., Steinmetz, R.: Let the clouds compute: cost-efficient workload distribution in infrastructure clouds. In: Vanmechelen, K., Altmann, J., Rana, O.F. (eds.) GECON 2012. LNCS, vol. 7714, pp. 91–101. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    Li, W., Tordsson, J., Elmroth, E.: Virtual machine placement for predictable and time-constrained peak loads. In: Vanmechelen, K., Altmann, J., Rana, O.F. (eds.) GECON 2011. LNCS, vol. 7150, pp. 120–134. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    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, pp. 163–171 (2011)Google Scholar
  21. 21.
    Mann, Z.A.: Allocation of virtual machines in cloud data centers - a survey of problem models and optimization algorithms. ACM Comput. Surv. 48(1) (2015). Article No. 11Google Scholar
  22. 22.
    Mann, Z.A.: Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center. Future Gener. Comput. Syst. 51, 1–6 (2015)CrossRefGoogle Scholar
  23. 23.
    Mann, Z.A.: A taxonomy for the virtual machine allocation problem. Int. J. Math. Models Methods Appl. Sci. 9, 269–276 (2015)Google Scholar
  24. 24.
    Mishra, M., Sahoo, A.: On theory of VM placement: anomalies in existing methodologies and their mitigation using a novel vector based approach. In: IEEE International Conference on Cloud Computing, pp. 275–282 (2011)Google Scholar
  25. 25.
    Natural Resources Defense Council: Scaling up energy efficiency across the data center industry: evaluating key drivers and barriers (2014). http://www.nrdc.org/energy/files/data-center-efficiency-assessment-IP.pdf
  26. 26.
    Oliveira, D., Ocana, K.A.C.S., Baiao, F., Mattoso, M.: A provenance-based adaptive scheduling heuristic for parallel scientific workflows in clouds. J. Grid Comput. 10, 521–552 (2012)CrossRefGoogle Scholar
  27. 27.
    Pandey, S., Wu, L., Guru, S.M., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 400–407. IEEE (2010)Google Scholar
  28. 28.
    Piraghaj, S.F., Dastjerdi, A.V., Calheiros, R.N., Buyya, R.: Efficient virtual machine sizing for hosting containers as a service. In: IEEE World Congress on Services, pp. 31–38 (2015)Google Scholar
  29. 29.
    Sáez, S.G., Andrikopoulos, V., Hahn, M., Karastoyanova, D., Leymann, F., Skouradaki, M., Vukojevic-Haupt, K.: Performance and cost trade-off in IaaS environments: a scientific workflow simulation environment case study. In: Helfert, M., Muñoz, V.M., Ferguson, D. (eds.) Cloud Computing and Services Science. CCIS, vol. 581, pp. 153–170. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  30. 30.
    Svärd, P., Li, W., Wadbro, E., Tordsson, J., Elmroth, E.: Continuous datacenter consolidation. In: IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), pp. 387–396 (2015)Google Scholar
  31. 31.
    Tomás, L., Tordsson, J.: An autonomic approach to risk-aware data center overbooking. IEEE Trans. Cloud Comput. 2(3), 292–305 (2014)CrossRefGoogle Scholar
  32. 32.
    Tordsson, J., Montero, R.S., Moreno-Vozmediano, R., Llorente, I.M.: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Gener. Comput. Syst. 28(2), 358–367 (2012)CrossRefGoogle Scholar
  33. 33.
    Tsamoura, E., Gounaris, A., Tsichlas, K.: Multi-objective optimization of data flows in a multi-cloud environment. In: Proceedings of the Second Workshop on Data Analytics in the Cloud, pp. 6–10 (2013)Google Scholar
  34. 34.
    Verma, A., Dasgupta, G., Nayak, T.K., De, P., Kothari, R.: Server workload analysis for power minimization using consolidation. In: Proceedings of the 2009 USENIX Annual Technical Conference, pp. 355–368 (2009)Google Scholar
  35. 35.
    Villegas, D., Antoniou, A., Sadjadi, S.M., Iosup, A.: An analysis of provisioning and allocation policies for infrastructure-as-a-service clouds. In: 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 612–619 (2012)Google Scholar
  36. 36.
    Zhou, Y., Zhang, Y., Liu, H., Xiong, N., Vasilakos, A.V.: A bare-metal and asymmetric partitioning approach to client virtualization. IEEE Trans. Serv. Comput. 7(1), 40–53 (2014)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.paluno – The Ruhr Institute for Software Technology, University of Duisburg-EssenEssenGermany

Personalised recommendations