Advertisement

SLA-Based Resource Provisioning for Heterogeneous Workloads in a Virtualized Cloud Datacenter

  • Saurabh Kumar Garg
  • Srinivasa K. Gopalaiyengar
  • Rajkumar Buyya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7016)

Abstract

Efficient provisioning of resources is a challenging problem in cloud computing environments due to its dynamic nature and the need for supporting heterogeneous applications with different performance requirements. Currently, cloud datacenter providers either do not offer any performance guarantee or prefer static VM allocation over dynamic, which lead to inefficient utilization of resources. Earlier solutions, concentrating on a single type of SLAs (Service Level Agreements) or resource usage patterns of applications, are not suitable for cloud computing environments. In this paper, we tackle the resource allocation problem within a datacenter that runs different type of application workloads, particularly non-interactive and transactional applications. We propose admission control and scheduling mechanism which not only maximizes the resource utilization and profit, but also ensures the SLA requirements of users. In our experimental study, the proposed mechanism has shown to provide substantial improvement over static server consolidation and reduces SLA Violations.

Keywords

Admission Control High Performance Computing Service Level Agreement Cloud Provider Service Level Agreement Violation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Azoff, E.: Neural network time series forecasting of financial markets. John Wiley & Sons, Inc., New York (1994)Google Scholar
  2. 2.
    Beloglazov, et al.: A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems. In: Zelkowitz, M. (ed.) Advances in Computers. Elsevier, Amsterdam (2011) ISBN 13: 978-0-12-012141-0Google Scholar
  3. 3.
    Buyya, et al.: Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility. FGCS 25(6), 599–616 (2009)CrossRefGoogle Scholar
  4. 4.
    Carrera, D., Steinder, M., Whalley, I., Torres, J., Ayguadé, E.: Enabling resource sharing between transactional and batch workloads using dynamic application placement. In: Issarny, V., Schantz, R. (eds.) Middleware 2008. LNCS, vol. 5346, pp. 203–222. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Dodonov, E., de Mello, R.: A novel approach for distributed application scheduling based on prediction of communication events. FGCS 26(5), 740–752 (2010)CrossRefGoogle Scholar
  6. 6.
    Iosup, A., Epema, D.: Grid computing workloads: Bags of tasks, workflows, pilots, and others. IEEE Internet Computing 99(PrePrints) (2010)Google Scholar
  7. 7.
    Iosup, et al.: The grid workloads archive. FGCS 24(7), 672–686 (2008)CrossRefGoogle Scholar
  8. 8.
    Kim, J.K., Siegel, H.J., Maciejewski, A.A., Eigenmann, R.: Dynamic resource management in energy constrained heterogeneous computing systems using voltage scaling. IEEE Trans. Parallel Distrib. Syst. 19(11), 1445–1457 (2008)CrossRefGoogle Scholar
  9. 9.
    Meng, et al.: Efficient resource provisioning in compute clouds via vm multiplexing. In: Proc. of the 7th Intl. Conf. on Auton. Comp., Washington, DC, USA (2010)Google Scholar
  10. 10.
    Mohammadi, S., Abbasi-Nejad, H.: Forecasting With Matlab. 129.3.20.41/eps/prog/papers/0505/0505001.pdf (2005)Google Scholar
  11. 11.
    Park, K., Pai, V.: CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Operating Systems Review 40(1), 65–74 (2006)CrossRefGoogle Scholar
  12. 12.
    Quiroz, et al.: Towards autonomic workload provisioning for enterprise grids and clouds. In: Proc. of 10th IEEE/ACM Intl. Conf. on Grid Comp., USA (2009)Google Scholar
  13. 13.
    Singh, et al.: Autonomic mix-aware provisioning for non-stationary data center workloads. In: Proc. of the 7th Intl. Conf. on Autc. Comp., USA (2010)Google Scholar
  14. 14.
    Smith, et al.: Secure on-demand grid computing. FGCS 25(3), 315–325 (2009)CrossRefGoogle Scholar
  15. 15.
    Sotomayor, et al.: Combining batch execution and leasing using virtual machines. In: Proc. of the 17th Intl. Sym. on HPDC, Boston, MA, USA (2008)Google Scholar
  16. 16.
    Soundararajan, et al.: The impact of mngt. operations on the virtualized datacenter. In: Proc. of the 37th Ann. Intl. Sym. on Comp. Arch., France (2010)Google Scholar
  17. 17.
    Srinivasa, et al.: An efficient fuzzy based neuro-genetic algorithm for stock market prediction. Intl. Jnl. of Hyb. Intelligent Sys. 3(2), 63–81 (2006)CrossRefzbMATHGoogle Scholar
  18. 18.
    Wang, et al.: Capacity and performance overhead in dynamic resource allocation to virtual containers. In: Proc. of the 10th IFIP/IEEE Intl. Symp. on Intgd. Net. Mangt., Munich, Germany (2007)Google Scholar
  19. 19.
    Yeo, C., Buyya, R.: Service Level Agreement based Alloc. of Cluster Resources: Handling Penalty to Enhance Utility. In: Proc. of the 7th IEEE Intl. Conf. on Cluster Comp., Boston, USA (2005)Google Scholar
  20. 20.
    Zhang, et al.: Agile resource management in a virtualized data center. In: Proc. of Ist Joint WOSP/SIPEW Intl. Conf. on Perf. Eng., California, USA (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Saurabh Kumar Garg
    • 1
  • Srinivasa K. Gopalaiyengar
    • 1
  • Rajkumar Buyya
    • 1
  1. 1.Cloud Computing and Distributed Systems Laboratory Department of Computer Science and Software EngineeringThe University of MelbourneAustralia

Personalised recommendations