Annals of Telecommunications

, Volume 73, Issue 3–4, pp 205–218 | Cite as

Dynamic VM allocation in a SaaS environment

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Abstract

Given the costs associated with a cloud infrastructure, dynamic scheduling of virtual machines (VMs) can significantly lower costs while providing an acceptable service level. We develop a series of forecasting models for predicting demand for VMs in a cloud-based software used as a software-as-a-service (SaaS). These models are then used in a periodic-review provision model which determines how many VMs should be provisioned or de-provision at each inspection interval. A simple provisioning heuristic model is also proposed, whereby a fixed reserve capacity of VMs is continuously maintained. We evaluate and compare the performance of these models for different model parameters using historical data from the Virtual Computing Laboratory (VCL) at North Carolina State University.

Keywords

Provision of virtual machines (VMs) Time-series forecasting models Hidden Markov models (HMM) Virtual computing laboratory (VCL) Periodic-review model Capacity planning 

References

  1. 1.
    Urgaonkar B, Shenoy P, Chandra A, Goyal P, Wood T (2008) Agile dynamic provisioning of multi-tier internet applications. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 3(1):1Google Scholar
  2. 2.
    Roy N, Dubey A, Gokhale A (2011) Efficient autoscaling in the cloud using predictive models for workload forecasting, In: Proceedings of the 2011 I.E. 4th International Conference on Cloud Computing, Washington DC, USA, pp. 500–507Google Scholar
  3. 3.
    Minarolli D, Freisleben B Cross-correlation prediction of resource demand for virtual machine resource allocation in clouds. Computational Intelligence, Communication Systems and Networks (CICSyN), 2014 Sixth International Conference, 27–29 May 2014Google Scholar
  4. 4.
    Gong Z, Gu X, Wilkes J (2010) Press: predictive elastic resource scaling for cloud systems. International Conference on Network and Service Management, pp 9–16. IEEE PressGoogle Scholar
  5. 5.
    Hu R, Jiang J, Liu G, Wang L (2013) KSwSVR: a new load forecasting method for efficient resources provisioning in cloud. IEEE International Conference on Services Computing, pp 120–127. IEEE PressGoogle Scholar
  6. 6.
    Islam S, Keung J, Lee K, Liu A (2012) Empirical prediction models for adaptive resource provisioning in the cloud. Futur Gener Comput Syst 28(1):155–162CrossRefGoogle Scholar
  7. 7.
    Sotomayor B, Montero RS, Llorente IM, Foster I (2008) Capacity leasing in cloud systems using the opennebula engine. Workshop on Cloud Computing and its Applications 2008 (CCA08), October 22–23Google Scholar
  8. 8.
    Silva JN, Veiga L, Ferreira P (2008) Heuristic for resources allocation on utility computing infrastructures. In Proceedings of the 6th International Workshop on Middleware for Grid ComputingGoogle Scholar
  9. 9.
    Lorido-Botran T, Miguel-Alonso J, Lozano JA (2014) A review of auto-scaling techniques for elastic applications in cloud environments. Journal of Grid Computing:1–34Google Scholar
  10. 10.
    Jiang J, Lu J, Zhang G (2011) An innovative self-adaptive configuration optimization system in cloud computing. Dependable, Autonomic and Secure Computing (DASC), 2011 I.E. Ninth International Conference, 12–14, pp 621–627Google Scholar
  11. 11.
    Mao M, Humphrey M (2012) A performance study on the VM startup time in the cloud, in Cloud Computing (CLOUD), 2012 I.E. 5th International Conference on, pp. 423–430Google Scholar
  12. 12.
    Jiang Y, Perng C-S, Li T, Chang R (2012) Intelligent cloud capacity management, IEEE/IFIP Network Operations and Management Symposium (NOMS)Google Scholar
  13. 13.
    Bouterse B, Perros H (2012) Scheduling cloud capacity for time-varying customer demand, in Cloud Networking (CLOUDNET), 2012 I.E. 1st International Conference on, pp. 137–142Google Scholar
  14. 14.
    Schaffer HE, Averitt SF, Hoit MI, Peeler A, Sills ED, Vouk MA (2009) NCSU’s virtual computing lab: a cloud computing solution. Computer 42(7):94–97CrossRefGoogle Scholar
  15. 15.
    Bouterse B (2016) VM capacity planning for software-as-a-service environments, Ph.D. Thesis, North Carolina State UniversityGoogle Scholar
  16. 16.
    Groskinsky B, Medhi D, Tipper D (2001) An investigation of adaptive capacity control schemes in a dynamic traffic environment. IEICE Trans Commun Educ B 84:263–274Google Scholar
  17. 17.
    Dubois É, Michaux E (2001) Grocer 1.64: an econometric toolbox for ScilabGoogle Scholar

Copyright information

© Institut Mines-Télécom and Springer-Verlag France SAS 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceNorth Carolina State UniversityRaleighUSA

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