Skip to main content
SpringerLink
Log in

Enhancing service capability with multiple finite capacity server queues in cloud data centers

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Cloud computing took a step forward in the efficient use of hardware through virtualization technology. And as a result, cloud brings evident benefits for both users and providers. While users can acquire computational resources on-demand elastically, cloud vendors can also utilize maximally the investment costs for data centers infrastructure. In the Internet era, the number of appliances and services migrated to cloud environment increases exponentially. This leads to the expansion of data centers, which become bigger and bigger. Not just that these data centers must have the architecture with a high elasticity in order to serve the huge upsurge of tasks and balance the energy consumption. Although in recent times, many research works have dealt with finite capacity for single job queue in data centers, the multiple finite-capacity queues architecture receives less attention. In reality, the multiple queues architecture is widely used in large data centers. In this paper, we propose a novel three-state model for cloud servers. The model is deployed in both single and multiple finite capacity queues. We also bring forward several strategies to control multiple queues at the same time. This approach allows to reduce service waiting time for jobs and managing elastically the service capability for the whole system. We use CloudSim to simulate the cloud environment and to carry out the experiments in order to demonstrate the operability and effectiveness of the proposed method and strategies. The power consumption is also evaluated to provide insights into the system performance in respect of performance-energy trade-off.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Average power use per server. http://www.vertatique.com/average-power-use-server. Accessed 4 Feb 2016

  2. Barroso, L.A., Holzle, U.: The case for energy-proportional computing. Computer 40(12), 33–37 (2007)

    Article  Google Scholar 

  3. 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. Concurr. Comput. 24(13), 1397–1420 (2012)

    Article  Google Scholar 

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

  5. Gandhi, A., Harchol-Balter, M., Adan, I.: Server farms with setup costs. Perform. Eval. 67(11), 1123–1138 (2010)

    Article  Google Scholar 

  6. Goswami, V., Patra, S.S., Mund, G.: Performance analysis of cloud with queue-dependent virtual machines. In: IEEE 1st International Conference on Recent Advances in Information Technology (RAIT), pp. 357–362 (2012)

  7. Halabian, H., Lambadaris, I., Lung, C.H.: Network capacity region of multi-queue multi-server queueing system with time varying connectivities. In: IEEE International Symposium on Information Theory Proceedings (ISIT), pp. 1803–1807 (2010)

  8. Hamilton, J.: Cooperative expendable micro-slice servers (cems): low cost, low power servers for internet-scale services. In: Conference on Innovative Data Systems Research (CIDR09). Citeseer (2009)

  9. Jarzab, M., Zielinski, K.: Adaptable service oriented infrastructure provisioning with lightweight containers virtualization technology. Comput. Inform. 34(6), 1309–1339 (2015)

    Google Scholar 

  10. Karthick, A., Ramaraj, E., Kannan, R.: An efficient tri queue job scheduling using dynamic quantum time for cloud environment. In: IEEE International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), pp. 871–876 (2013)

  11. Karthick, A., Ramaraj, E., Subramanian, R.G.: An efficient multi queue job scheduling for cloud computing. In: IEEE World Congress on Computing and Communication Technologies (WCCCT), pp. 164–166 (2014)

  12. Kato, M., Masuyama, H., Kasahara, S., Takahashi, Y.: Effect of energy-saving server scheduling on power consumption for large-scale data centers. Management 12(2), 667–685 (2016)

    MathSciNet  MATH  Google Scholar 

  13. Katz, R.H.: Tech titans building boom. IEEE Spectr. 2(46), 40–54 (2009)

    Article  Google Scholar 

  14. Khazaei, H., Mišić, J., Mišić, V.B.: Performance analysis of cloud computing centers using m/g/m/m+ r queuing systems. IEEE Trans. Parallel Distrib. Syst. 23(5), 936–943 (2012)

    Article  Google Scholar 

  15. Kim, H.S., Shin, D., Yu, Y., Eom, H., Yeom, H.Y.: Towards energy proportional cloud for data processing frameworks. In: SustainIT (2010)

  16. Kosta, S., Aucinas, A., Hui, P., Mortier, R., Zhang, X.: Thinkair: dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In: IEEE Proceedings INFOCOM, pp. 945–953 (2012)

  17. Lin, C.C., Liu, P., Wu, J.J.: Energy-efficient virtual machine provision algorithms for cloud systems. In: Fourth IEEE International Conference on Utility and Cloud Computing (UCC), pp. 81–88 (2011)

  18. Liu, Y., Whitt, W.: Algorithms for time-varying networks of many-server fluid queues. INFORMS J. Comput. 26(1), 59–73 (2013)

    Article  MathSciNet  Google Scholar 

  19. Liu, Y., Whitt, W.: Stabilizing performance in many-server queues with time-varying arrivals and customer feedback. Tech. rep., Working paper (2014)

  20. Liu, L., Wang, H., Liu, X., Jin, X., He, W.B., Wang, Q.B., Chen, Y.: Greencloud: a new architecture for green data center. In: Proceedings of the 6th international conference industry session on Autonomic computing and communications industry session, pp. 29–38. ACM (2009)

  21. Long, S., Zhao, Y., Chen, W.: A three-phase energy-saving strategy for cloud storage systems. J. Syst. Softw. 87, 38–47 (2014)

    Article  Google Scholar 

  22. Luo, Z., Qian, Z.: Burstiness-aware server consolidation via queuing theory approach in a computing cloud. In: IEEE 27th International Symposium on Parallel & Distributed Processing (IPDPS), pp. 332–341 (2013)

  23. Milenkovic, M., Castro-Leon, E., Blakley, J.R.: Power-aware management in cloud data centers. In: Cloud Computing, pp. 668–673. Springer, New York (2009)

  24. Nguyen, M.B., Tran, V., Hluchy, L.: A generic development and deployment framework for cloud computing and distributed applications. Comput. Inform. 32(3), 461–485 (2013)

    Google Scholar 

  25. Nguyen, B.M., Tran, D., Nguyen, Q.: A strategy for server management to improve cloud service qos. In: IEEE/ACM 19th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), pp. 120–127 (2015). doi:10.1109/DS-RT.2015.14

  26. Phung-Duc, T.: Server farms with batch arrival and staggered setup. In: Proceedings of the Fifth Symposium on Information and Communication Technology, pp. 240–247. ACM (2014)

  27. Phung-Duc, T.: Multiserver queues with finite capacity and setup time. In: Analytical and Stochastic Modelling Techniques and Applications, pp. 173–187. Springer, New York (2015)

  28. Powell, M.J.: A fast algorithm for nonlinearly constrained optimization calculations. In: Numerical analysis, pp. 144–157. Springer, New York (1978)

  29. Toporkov, V., Yemelyanov, D., Potekhin, P., Toporkova, A., Tselishchev, A.: Metascheduling and heuristic co-allocation strategies in distributed computing. Comput. Inform. 34(1), 45–76 (2015)

    MathSciNet  Google Scholar 

  30. Zhang, Q., Zhu, Q., Boutaba, R.: Dynamic resource allocation for spot markets in cloud computing environments. In: Fourth IEEE International Conference on Utility and Cloud Computing (UCC), pp. 178–185 (2011)

  31. Zhang, Q., Zhani, M.F., Zhang, S., Zhu, Q., Boutaba, R., Hellerstein, J.L.: Dynamic energy-aware capacity provisioning for cloud computing environments. In: Proceedings of the 9th International Conference on Autonomic Computing, pp. 145–154. ACM (2012)

Download references

Acknowledgments

This research is supported by the Vietnamese MOET’s Project “Research on development and deployment of cloud-based bioinformatics services applying for metagenomics” No. B2015-01-89, the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant Number 102.05-2014.28, and the Slovak national Project “Methods and algorithms for the semantic processing of Big Data in distributed computing environment” VEGA 2/0167/16.

Author information

Authors and Affiliations

  1. School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam

    Binh Minh Nguyen & Dang Tran

  2. Institute of Informatics, Slovak Academy of Sciences, Dúbravská cesta 9, 845 07, Bratislava, Slovakia

    Giang Nguyen

Authors
  1. Binh Minh Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dang Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giang Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Binh Minh Nguyen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, B.M., Tran, D. & Nguyen, G. Enhancing service capability with multiple finite capacity server queues in cloud data centers. Cluster Comput 19, 1747–1767 (2016). https://doi.org/10.1007/s10586-016-0653-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-016-0653-y

Keywords

Search

Navigation