Skip to main content
Book cover

Information and Communication Technology - EurAsia Conference

ICT-EurAsia 2013: Information and Communication Technology pp 183–191Cite as

A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7804)

Abstract

Energy efficiency has become an important measurement of scheduling algorithm for private cloud. The challenge is trade-off between minimizing of energy consumption and satisfying Quality of Service (QoS) (e.g. performance or resource availability on time for reservation request). We consider resource needs in context of a private cloud system to provide resources for applications in teaching and researching. In which users request computing resources for laboratory classes at start times and non-interrupted duration in some hours in prior. Many previous works are based on migrating techniques to move online virtual machines (VMs) from low utilization hosts and turn these hosts off to reduce energy consumption. However, the techniques for migration of VMs could not use in our case. In this paper, a genetic algorithm for power-aware in scheduling of resource allocation (GAPA) has been proposed to solve the static virtual machine allocation problem (SVMAP). Due to limited resources (i.e. memory) for executing simulation, we created a workload that contains a sample of one-day timetable of lab hours in our university. We evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list of virtual machines in start time (i.e. earliest start time first) and using best-fit decreasing (i.e. least increased power consumption) algorithm, for solving the same SVMAP. As a result, the GAPA algorithm obtains total energy consumption is lower than the baseline algorithm on simulated experimentation.

Keywords

  • Cloud Computing
  • Virtual Machine
  • Total Energy Consumption
  • Hard Disk Drive
  • Private Cloud

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.

Download conference paper PDF

References

  1. Albers, S., Fujiwara, H.: Energy-efficient algorithms. ACM Review 53(5), 86–96 (2010), doi:10.1145/1735223.1735245

    CrossRef  Google Scholar 

  2. Barroso, L.A., Hölzle, U.: The Case for Energy-Proportional Computing, vol. 40, pp. 33–37. ACM (2007), doi:10.1109/MC.2007.443

    Google Scholar 

  3. Beloglazov, A., Buyya, R.: Energy Efficient Resource Management in Virtualized Cloud Data Centers. In: Proceedings of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 826–831 (2010), doi:10.1109/CCGRID.2010.46

    Google Scholar 

  4. Beloglazov, A., Buyya, R.: Adaptive Threshold-Based Approach for Energy-Efficient Consolidation of VMs in Cloud Data Centers. ACM (2010)

    Google Scholar 

  5. Beloglazova, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. FGCS 28(5), 755–768 (2012), doi:10.1016/j.future.2011.04.017

    CrossRef  Google Scholar 

  6. 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. In: Concurrency and Computation: Practice and Experience, Concurrency Computat.: Pract. Exper., pp. 1–24 (2011), doi: 10.1002/cpe

    Google Scholar 

  7. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. FGCS 25(6), 599–616 (2009), doi:10.1016/j.future.2008.12.001

    CrossRef  Google Scholar 

  8. Fan, X., Weber, W.-D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. In: Proceedings of the 34th Annual International Symposium on Computer Architecture, pp. 13–23. ACM (2007), doi:10.1145/1273440.1250665

    Google Scholar 

  9. Goiri, J.F., Nou, R., Berral, J., Guitart, J., Torres, J.: Energy-aware Scheduling in Virtualized Datacenters. In: IEEE International Conference on Cluster Computing, CLUSTER 2010, pp. 58–67 (2010)

    Google Scholar 

  10. Kołodziej, J., Khan, S.U., Zomaya, A.Y.: A Taxonomy of Evolutionary Inspired Solutions for Energy Management in Green Computing: Problems and Resolution Methods. In: Kolodziej, J., Khan, S.U., Burczynski, T., et al. (eds.) Advances in Intelligent Modelling and Simulation. SCI, vol. 422, pp. 215–233. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  11. Sotomayor, B., Keahey, K., Foster, I.: Combining batch execution and leasing using virtual machines. In: Proceedings of the 17th International Symposium on High Performance Distributed Computing - HPDC 2008, pp. 87–96. ACM (2008), doi: 10.1145/1383422.1383434

    Google Scholar 

  12. Sotomayor, B.: Provisioning Computational Resources Using Virtual Machines and Leases, PhD Thesis submited to The University of Chicago, US (2010)

    Google Scholar 

  13. Laszewski, G.V., Wang, L., Younge, A.J., He, X.: Power-aware scheduling of virtual machines in DVFS-enabled clusters. In: 2009 IEEE International Conference on Cluster Computing and Workshops, pp. 368–377 (2009), doi:10.1109/CLUSTR.2009.5289182

    Google Scholar 

  14. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience 41(1), 23–50 (2011)

    Google Scholar 

  15. SPECpower ssj2008 results for Dell Inc. PowerEdge R620 (Intel Xeon E5-2660, 2.2 GHz) http://www.spec.org/power_ssj2008/results/res2012q2/power_ssj2008-20120417-00451.html (last accessed November 29, 2012)

  16. SPECpower ssj2008 results for IBM x3250 (1 x [Xeon X3470 2933 MHz, 4 cores], 8GB). http://www.spec.org/power_ssj2008/results/res2009q4/power_ssj2008-20091104-00213.html (last accessed November 29, 2012)

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam

    Nguyen Quang-Hung, Pham Dac Nien, Nguyen Hoai Nam, Nguyen Huynh Tuong & Nam Thoai

Authors
  1. Nguyen Quang-Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pham Dac Nien
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nguyen Hoai Nam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nguyen Huynh Tuong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nam Thoai
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Sceince and Electronics, University Gadjah Mada, Sekip Utara Bis, 55281, Yogyakarta, Indonesia

    Khabib Mustofa

  2. Faculty of Computer Science, University of Vienna, Währinger Straße 29, 1190, Wien, Austria

    Erich J. Neuhold

  3. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstraße 9-11, 1040, Wien, Austria

    A Min Tjoa

  4. Institute of Software Technology and Interactive Systems, Vienna University of Technology and SBA Research, Favoritenstraße 9-11, 1040, Wien, Austria

    Edgar Weippl

  5. School of Informatic Science, Korean Bible University, 16 Danghyun 2-gil, 139-791, Seoul, Nowon-gu, South Korea

    Ilsun You

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Quang-Hung, N., Nien, P.D., Nam, N.H., Huynh Tuong, N., Thoai, N. (2013). A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud. In: Mustofa, K., Neuhold, E.J., Tjoa, A.M., Weippl, E., You, I. (eds) Information and Communication Technology. ICT-EurAsia 2013. Lecture Notes in Computer Science, vol 7804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36818-9_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-36818-9_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36817-2

  • Online ISBN: 978-3-642-36818-9

  • eBook Packages: Computer ScienceComputer Science (R0)