Skip to main content
SpringerLink
Log in
Book cover

Second International Conference on Cloud Computing and Big Data in Asia

CloudCom-Asia 2015: Cloud Computing and Big Data pp 42–54Cite as

Energy-Efficient VM Placement Algorithms for Cloud Data Center

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9106))

Abstract

Cloud is the computing paradigm which provides computing resource as a service through network. The client can use computing resource in a convenient and on-demand way, just like the water and the electricity we use daily. The mapping between virtual machine and physical machine is the key of the VM scheduling problem. Nowadays we advocate low-carbon life. It calls for the green cloud computing solutions whether protecting the environment or saving the cost of cloud suppliers. The proposed VM placement algorithm is energy-efficient, and considers the multi-dimentional resource constrains, such as CPU, memory, network bandwidth, and so on. The experimental results show that the proposed algorithms not only contribute a lot to energy saving, but also try best to meet the quality of service (QoS). Therefore, we make significant savings in operating cost and make full use of various resources in the cloud data center. The algorithm has promising prospect in application.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bilal, K., Malik, S.U.R., Khalid, O., et al.: A taxonomy and survey on green data center networks. Future Gener. Comput. Syst. (2013)

    Google Scholar 

  2. Kim, N., Cho, J., Seo, E.: Energy-credit scheduler: an energy-aware virtual machine scheduler for cloud systems. Future Gener. Comput. Syst. 32, 128–137 (2014)

    Article  Google Scholar 

  3. Lucas-Simarro, J.L., Moreno-Vozmediano, R., Montero, R.S., et al.: Scheduling strategies for optimal service deployment across multiple clouds. Future Gener. Comput. Syst. 29(6), 1431–1441 (2013)

    Article  Google Scholar 

  4. Song, Y., Sun, Y., Shi, W.: A two-tiered on-demand resource allocation mechanism for VM-based data centers. IEEE Trans. Serv. Comput. 6(1), 116–129 (2013)

    Article  Google Scholar 

  5. Li, Q., Hao, Q., Xiao, L., Li, Z.: Adaptive management and multi-objective optimization of virtual machine placement in cloud computing. Chin. J. Comput. 34(12), 2253–2264 (2011)

    Article  Google Scholar 

  6. Tang, C., Steinder, M., Spreitzer, M., et al.: A scalable application placement controller for enterprise data centers. In: Proceedings of the 16th International Conference on World Wide Web, pp. 331–340. ACM (2007)

    Google Scholar 

  7. Li, H., Wang, J., Peng, J., Wang, J., Liu, T.: Energy-aware scheduling scheme using workload-aware consolidation technique in cloud data centres. China Commun. 10, 114–124 (2013)

    Article  Google Scholar 

  8. 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(5), 755–768 (2012)

    Article  Google Scholar 

  9. Ardagna, D., Panicucci, B., Trubian, M., et al.: Energy-aware autonomic resource allocation in multitier virtualized environments. IEEE Trans. Serv. Comput. 5(1), 2–19 (2012)

    Article  Google Scholar 

  10. Katsaros, G., Subirats, J., Fitó, J.O., et al.: A service framework for energy-aware monitoring and VM management in Clouds. Future Gener. Comput. Syst. 29(8), 2077–2091 (2013)

    Article  Google Scholar 

  11. Cardosa, M., Korupolu, M.R., Singh, A.: Shares and utilities based power consolidation in virtualized server environments. In: IFIP/IEEE International Symposium on Integrated Network Management (IM 2009), pp. 327–334. IEEE (2009)

    Google Scholar 

  12. Verma, A., Dasgupta, G., Nayak, T.K., et al.: Server workload analysis for power minimization using consolidation. In: Proceedings of the 2009 Conference on USENIX Annual Technical Conference, p. 28. USENIX Association (2009)

    Google Scholar 

  13. Buyya, R., Yeo, C.S., Venugopal, S.: Market-oriented cloud computing: vision, hype, and reality for delivering it services as computing utilities. In: 10th IEEE International Conference on High Performance Computing and Communications (HPCC 2008), pp. 5–13. IEEE (2008)

    Google Scholar 

  14. García, A.G., Espert, I.B., García, V.H.: SLA-driven dynamic cloud resource management. Future Gener. Comput. Syst. 31, 1–11 (2014)

    Article  Google Scholar 

  15. Tordsson, J., Montero, R.S., Moreno-Vozmediano, R., et al.: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Gener. Comput. Syst. 28(2), 358–367 (2012)

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported in part by the Fujian province Education Scientific Research Project of Young and middle-aged teachers under Grant JA13356.

Author information

Authors and Affiliations

  1. Fujian Provincial Key Lab of the Network Computing and Intelligent Information Processing, College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China

    Xiuyan Lin, Zhanghui Liu & Wenzhong Guo

Authors
  1. Xiuyan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhanghui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenzhong Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenzhong Guo .

Editor information

Editors and Affiliations

  1. School of Computer Science and Tech., Huazhong Univ. of Science and Technology, Wuhan, China

    Weizhong Qiang

  2. College of Mathematics and Computer Sci., Fuzhou University, Fuzhou, China

    Xianghan Zheng

  3. Dept. of Computer Scie and Informat. Eng, Chung Hua University, Hsinchu, Taiwan

    Ching-Hsien Hsu

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Lin, X., Liu, Z., Guo, W. (2015). Energy-Efficient VM Placement Algorithms for Cloud Data Center. In: Qiang, W., Zheng, X., Hsu, CH. (eds) Cloud Computing and Big Data. CloudCom-Asia 2015. Lecture Notes in Computer Science(), vol 9106. Springer, Cham. https://doi.org/10.1007/978-3-319-28430-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28430-9_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28429-3

  • Online ISBN: 978-3-319-28430-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation