Advertisement

VM Placement in non-Homogeneous IaaS-Clouds

  • Konstantinos Tsakalozos
  • Mema Roussopoulos
  • Alex Delis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7084)

Abstract

Infrastructure-as-a-Service (IaaS) cloud providers often combine different hardware components in an attempt to form a single infrastructure. This single infrastructure hides any underlying heterogeneity and complexity of the physical layer. Given a non-homogeneous hardware infrastructure, assigning VMs to physical machines (PMs) becomes a particularly challenging task. VM placement decisions have to take into account the operational conditions of the cloud (e.g., current PM load) and load balancing prospects through VM migrations. In this work, we propose a service realizing a two-phase VM-to-PM placement scheme. In the first phase, we identify a promising group of PMs, termed cohort, among the many choices that might be available; such a cohort hosts the virtual infrastructure of the user request. In the second phase, we determine the final VM-to-PM mapping considering all low-level constraints arising from the particular user requests and special characteristics of the selected cohort. Our evaluation shows that in large non-homogeneous physical infrastructures, we significantly reduce the VM placement plan production time and improve plan quality.

Keywords

Virtual Machine Constraint Satisfaction Problem Cloud Provider User Request 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.

References

  1. 1.
    Opennebula. (November 2010), http://www.opennebula.org
  2. 2.
    Al-Fares, M., Loukissas, A., Vahdat, A.: A Scalable, Commodity Data Center Network Architecture. In: Proc. of the ACM SIGCOMM Conference, pp. 63–74. ACM, Seattle (2008)Google Scholar
  3. 3.
    Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.H., Vahi, K.: Characterization of Scientific Workflows. In: 3rd Workshop on Workflows in Support of Large-Scale Science, Austin, TX, November 2008, pp. 1–10 (2008)Google Scholar
  4. 4.
    Bobroff, N., Kochut, A., Beaty, K.: Dynamic Placement of Virtual Machines for Managing SLA Violations. In: Proc of the 10th IFIP/IEEE International Symposium on Integrated Network Management, Munich, Germany (May 2007)Google Scholar
  5. 5.
    Breitgand, D., Epstein, A.: SLA-aware Placement of Multi-Virtual Machine Elastic Services in Compute Clouds. In: IFIP/IEEE International Symposium on Integrated Network Management, Dublin, Ireland (May 2011)Google Scholar
  6. 6.
    Chowdhury, N., Rahman, M., Boutaba, R.: Virtual Network Embedding with Coordinated Node and Link Mapping. In: Proc. of IEEE INFOCOM, Rio de Janeiro, Brazil (April 2009)Google Scholar
  7. 7.
    Cierniak, M., Zaki, M.J., Li, W.: Compile-Time Scheduling Algorithms for a Heterogeneous Network of Workstations. The Computer Journal 40(6), 356–372 (1997)CrossRefGoogle Scholar
  8. 8.
    Hermenier, F., Lorca, X., Menaud, J., Muller, G., Lawall, J.: Entropy: a Consolidation Manager for Clusters. In: Proc. of the 2009 ACM SIGPLAN/SIGOPS Int’l Conf. on Virtual Execution Environments, Washington, DC (March 2009)Google Scholar
  9. 9.
    Hyser, C., McKee, B., Gardner, R., Watson, B.J.: Autonomic Virtual Machine Placement in the Data Center. HP Laboratories HPL-2007 189 (2008)Google Scholar
  10. 10.
    Khanna, G., Beaty, K., Kar, G., Kochut, A.: Application Performance Management in Virtualized Server Environments. In: Proc of the 10th IEEE/IFIP Network Operations and Management Symposium, Vancouver, Canada (April 2006)Google Scholar
  11. 11.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Kumar, S., Talwar, V., Kumar, V., Ranganathan, P., Schwan, K.: vManage: Loosely Coupled Platform and Virtualization Management in Data Centers. In: Proceedings of the 6th International Conference on Autonomic Computing, June 2009, pp. 127–136. ACM, Barcelona (2009)Google Scholar
  13. 13.
    Meng, X., Pappas, V., Zhang, L.: Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement. In: Proceedings of IEEE INFOCOM, San Diego, CA, USA (March 2010)Google Scholar
  14. 14.
    Ricci, R., Alfeld, C., Lepreau, J.: A Solver for the Network Testbed Mapping Problem. SIGCOMM Computer Communications Review 33(2), 65–81 (2003)CrossRefGoogle Scholar
  15. 15.
    Rotithor, H.: Taxonomy of dynamic task scheduling schemes in distributed computing systems. In: IEEE Proceedings Computers and Digital Techniques (January 1994)Google Scholar
  16. 16.
    Sindelar, M., Sitaraman, R.K., Shenoy, P.: Sharing-Aware Algorithms for Virtual Machine Colocation. In: Proceedings of the 23rd ACM Symposium on Parallelism in Algorithms and Architectures, San Jose, California, USA (June 2011)Google Scholar
  17. 17.
    Singh, A., Korupolu, M., Mohapatra, D.: Server-Storage Virtualization: Integration and Load Balancing in Data Centers. In: Proc. of the 2008 ACM/IEEE Conference on Supercomputing SC 2008, pp. 53:1–53:12 (2008)Google Scholar
  18. 18.
    Tsakalozos, K., Kllapi, H., Sitaridi, E., Roussopoulos, M., Paparas, D., Delis, A.: Flexible Use of Cloud Resources through Profit Maximization and Price Discrimination. In: Proc. of the 27th IEEE Int. Conf. on Data Engineering (ICDE 2011), Hannover, Germany (April 2011)Google Scholar
  19. 19.
    Tsakalozos, K., Roussopoulos, M., Floros, V., Delis, A.: Nefeli: Hint-based Execution of Workloads in Clouds. In: Proc. of the 30th IEEE Int. Conf. on Distributed Computing Sytems (ICDCS 2010), Genoa, Italy (June 2010)Google Scholar
  20. 20.
    Wang, X., Lan, D., Wang, G., Fang, X., Ye, M., Chen, Y., Wang, Q.: Appliance-Based Autonomic Provisioning Framework for Virtualized Outsourcing Data Center. In: Proc. of the 4th Int. Conf. on Autonomic Computing, Washington, DC, p. 29 (2007)Google Scholar
  21. 21.
    Weng, C., Li, M., Wang, Z., Lu, X.: Automatic Performance Tuning for the Virtualized Cluster System. In: Proc. of the 29th IEEE International Conference on Distributed Computing Systems, Montreal, Canada (June 2009)Google Scholar
  22. 22.
    Wood, T., Shenoy, P., Venkataramani, A., Yousif, M.: Black-box and Gray-box Strategies for Virtual Machine Migration. In: Proc of the 4th USENIX Symposium on Networked Systems Design and Implementation, Cambridge, MA (2007)Google Scholar
  23. 23.
    Xu, J., Fortes, J.A.B.: Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments. In: Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing, Hangshou, PR of China (December 2010)Google Scholar
  24. 24.
    Yeo, C.S., Buyya, R.: A taxonomy of market-based resource management systems for utility-driven cluster computing. Softw. Pract. Exper. 36(13) (November 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Konstantinos Tsakalozos
    • 1
  • Mema Roussopoulos
    • 1
  • Alex Delis
    • 1
  1. 1.University of AthensAthensGreece

Personalised recommendations