Skip to main content
SpringerLink
Log in

Analysis model for server consolidation of virtualized heterogeneous data centers providing internet services

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Server consolidation based on virtualization technology simplifies system administration, reduces the cost of power and physical infrastructure, and improves resource utilizations in today’s service-oriented Internet data centers. How many servers for the underlying physical infrastructure are saved via server consolidation in virtualized data centers is of great interest to the administrators and designers of the data centers. Various workload consolidations differ in saving physical servers for the infrastructure. The impacts caused by virtualization to these concurrent services are fluctuating considerably which may have a great effect on server consolidation. This paper proposes an analytic model for server consolidation in virtualized Internet data centers based on the queuing theory. According to the features of these services’ workloads, this model can provide the supremum number of consolidated physical servers needed to guarantee QoS with same loss probabilities of requests as in dedicated servers. We verify the model via a case study. The experiments results confirm the superior accuracy of our model and show that the virtual machine-based server consolidation saves up to 50% physical infrastructure and improves 50% CPU resource utilization as well as 2.67 times in I/O bandwidth utilization, satisfying required QoS.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. In this paper, dedicated servers represent physical servers providing only one service at one time in the dedicated scenario.

References

  1. Gupta, D., Lee, S., Vrable, M., Savage, S., Snoeren, Alex C., Varghese, George, Voelker, Geoffrey M., Vahdat, Amin: Difference engine: harnessing memory redundancy in virtual machines. Commun. ACM 53(10), 85–93 (2010)

    Article  Google Scholar 

  2. Willmann, P., Rixner, S., Cox, A.L.: Protection strategies for direct access to virtualized I/O devices. In: USENIX 2008 Annual Technical Conference on Annual Technical Conference, ATC’08, pp. 15–28. (2008)

  3. Cui, L., Li, J., Li, B., Huai, J., Hu, C., Wo, T., Al-Aqrabi, H., Liu, L..: VMScatter: migrate virtual machines to many hosts. In: Proceedings of the 9th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, VEE ’13, pp. 63–72, ACM, New York, NY, USA (2013)

  4. Guo, Y., Stolyar, A.L., Walid, A.: Shadow-routing based dynamic algorithms for virtual machine placement in a network cloud. In: 2013 Proceedings of the IEEEINFOCOM, pp. 620–628 (2013)

  5. Song, Y., Wang, H., Li, Y., Feng, B., Sun, Y.: Multi-tiered on-demand resource scheduling for vm-based data center. In: Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID ’09, pp. 148–155. IEEE Computer Society, Washington, DC, USA (2009)

  6. 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 

  7. Padala, P., Shin, K.G., Zhu, X., Uysal, M., Wang, Z., Singhal, S., Merchant, A., Salem, K.: Adaptive control of virtualized resources in utility computing environments. In: Proceedings of the 2Nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007, EuroSys ’07, pp. 289–302. ACM, New York, NY, USA (2007)

  8. Vasić, N., Novaković, D., Miučin, S., Kostić, D., Bianchini, R.: DejaVu: Accelerating resource allocation in virtualized environments. In: Proceedings of the Seventeenth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS XVII, pp. 423–436. ACM, New York, NY, USA (2012)

  9. Wood, T., Cherkasova, L., Ozonat, K., Shenoy, P.: Profiling and modeling resource usage of virtualized applications. In: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, Middleware ’08, pp. 366–387. Springer, New York, NY, USA (2008)

  10. Chiang, R.C., Hwang, J., Huang, H.H., Wood, T.: Matrix: achieving predictable virtual machine performance in the clouds. In: 11th International Conference on Autonomic Computing (ICAC 14), pp. 45–56, Philadelphia, PA (2014)

  11. Kundu, S., Rangaswami, R., Dutta, K., Zhao, M.: Application performance modeling in a virtualized environment. In: 2010 IEEE 16th International Symposium on High Performance Computer Architecture (HPCA), pp. 1–10. (2010)

  12. Kundu, S., Rangaswami, R., Gulati, A., Zhao, M., Dutta, K.: Modeling virtualized applications using machine learning techniques. In: Proceedings of the 8th ACM SIGPLAN/SIGOPS Conference on Virtual Execution Environments, VEE ’12, pp. 3–14. ACM, New York, NY, USA (2012)

  13. Song, Y., Zhang, Y., Sun, Y., Shi, W.: Utility analysis for internet-oriented server consolidation in VM-based data centers. In: 2009 IEEE International Conference on Cluster Computing and Workshops, CLUSTER ’09, pp. 1–10. (2009)

  14. Gross, D., Harris, C.M.: Fundamentals of Queueing Theory. Wiley, New York, NY (1988)

    MATH  Google Scholar 

  15. Heidelberger, P., Lavenberg, Stephen S: Computer performance evaluation methodology. Comput. IEEE Trans. C–33(12), 1195–1220 (1984)

    Article  MathSciNet  Google Scholar 

  16. Smith, J.E.: Characterizing computer performance with a single number. Commun. ACM 31(10), 1202–1206 (1988)

    Article  Google Scholar 

  17. Lilja, David J.: Measuring Computer Performance: A Practitioner’s Guide. Cambridge University Press, Cambridge (2000)

    Book  Google Scholar 

  18. John, L.K.: More on finding a single number to indicate overall performance of a Benchmark suite. SIGARCH Comput. Archit. News 32(1), 3–8 (2004)

    Article  Google Scholar 

  19. Krishnaswamy, U., Scherson, I.D.: A Framework for computer performance evaluation using benchmark sets. Comput. IEEE Trans. 49(12), 1325–1338 (2000)

    Article  Google Scholar 

  20. Chen, T., Chen, Y., Guo, Q., Temam, O., Wu, Y., Hu, W.: Statistical performance comparisons of computers. In: 2012 IEEE 18th International Symposium on High Performance Computer Architecture (HPCA), pp. 1–12 (2012)

  21. Chen, T., Guo, Q., Temam, O., Wu, Y., Bao, Y., Xu, Z., Chen, Y.: Statistical performance comparisons of computers. Comput. IEEE Trans. 64, 1442–1455 (2014)

    Article  MathSciNet  Google Scholar 

  22. Cao, J., Andersson, M., Nyberg, C., Kihl, M.: Web server performance modeling using an M/G/1/K*PS queue. In: 10th International Conference on Telecommunications, ICT 2003, vol. 2, pp. 1501–1506. (2003)

  23. Liu, X., Heo, J., Sha, L.: Modeling 3-tiered web applications. In: Proceedings of the 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, 2005, pp. 307–310. (2005)

  24. Ghaith, S., Wang, M., Perry, P., Murphy, L.: Software contention aware queueing network model of three-tier web systems. In: Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering, ICPE ’14, pp. 273–276. ACM, New York, NY, USA (2014)

  25. Malkowski, S., Hedwig, M., Pu, C.: Experimental evaluation of N-tier systems: observation and analysis of multi-bottlenecks. In: IEEE International Symposium on Workload Characterization, IISWC 2009, pp. 118–127. IEEE (2009)

  26. Lim, S.-H., Huh, J.-S., Kim, Y., Shipman, G.M., Das, C.R.: D-factor: a quantitative model of application slow-down in multi-resource shared systems. In: Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS ’12, pp. 271–282. CM, New York, NY, USA (2012)

  27. Yao, J., Jung, G.: Bottleneck detection and solution recommendation for cloud-based multi-tier application. In: Xavier, F., Aditya, G., Grace, L., Sami, B. (eds.) Service-Oriented Computing. Lecture Notes in Computer Science, pp. 470–477. Springer, Berlin (2014)

    Chapter  Google Scholar 

  28. Padala, P., Zhu, X., Wang, Z., Singhal, S., Shin, K.G, et al.: Performance Evaluation of Virtualization Technologies for Server Consolidation. HP Labs Tec. Report (2007)

  29. Deshane, T., Shepherd, Z., Matthews, J., Ben-Yehuda, M., Shah, A., Rao, B.: Quantitative Comparison of Xen and KVM, pp. 1–2. Xen Summit, Boston (2008)

    Google Scholar 

  30. Scheepers, M.J.: Virtualization and containerization of application infrastructure: A Comparison. In: 21st Twente Student Conference on IT, Enschede, The Netherlands (2014)

  31. Iosup, A., Ostermann, S., Yigitbasi, M.N., Prodan, R., Fahringer, T., Epema, D.H.J.: Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans. Parallel Distrib. Syst. 22(6), 931–945 (2011)

    Article  Google Scholar 

  32. Gupta, D., Gardner, R., Cherkasova, L.: XenMon: QoS monitoring and performance profiling tool. Technical report, HP Labs, HPL-2005-187 (2005)

  33. Benevenuto, F., Fernandes, C., Santos, M., Almeida, V., Almeida, J., Janakiraman, G., Santos, J.: Performance models for virtualized applications. In: Frontiers of High Performance Computing and Networking–ISPA 2006 Workshops, pp. 427–439. Springer (2006)

  34. Menascé, D.A.: Virtualization: concepts, applications, and performance modeling. In: International Conference on CMG, pp. 407–414 (2005)

  35. Huber, N., von Quast, M., Brosig, F., Kounev, S.: Analysis of the performance-influencing factors of virtualization platforms. In: OTM 2010 on the On the Move to Meaningful Internet Systems, pp. 811–828. Springer, Berlin (2010)

  36. Huber, N., von Quast, M., Brosig, F., Hauck, M., Kounev, S.: A method for experimental analysis and modeling of virtualization performance overhead. In: Cloud Computing and Services Science, pp. 353–370. Springer, New York (2012)

  37. Minhas, U.F., Yadav, J., Aboulnaga, A., Salem, K.: Database systems on virtual machines: how much do you lose? In: IEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008, pp. 35–41 (2008)

  38. Zhang, S., Qian, Z., Luo, Z., Wu, J., Lu, S.: Burstiness-aware resource reservation for server consolidation in computing clouds. IEEE Trans. Parallel Distrib. Syst. 27(4), 964–977 (2016)

    Article  Google Scholar 

  39. He, L., Zou, D., Zhang, Z., Chen, C., Jin, Hai, Jarvis, Stephen A.: Developing resource consolidation frameworks for moldable virtual machines in clouds. Future Gener. Comput. Syst. 32, 69–81 (2014)

    Article  Google Scholar 

  40. Speitkamp, B., Bichler, Martin: A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE Trans. Serv. Comput. 3(4), 266–278 (2010)

    Article  Google Scholar 

  41. Chiang, R.C., Huang, H.H.: Profiling-based workload consolidation and migration in virtualized data centers. IEEE Trans. Parallel Distrib. Syst. 26(3), 878–890 (2015)

    Article  Google Scholar 

  42. Ferreto, T.C., Netto, M.A.S., Calheiros, R.N., Rose, C.A.F.: Server consolidation with migration control for virtualized data centers. Future Generat. Comput. Syst. 27(8), 1027–1034 (2011)

    Article  Google Scholar 

  43. Srikantaiah, S., Kansal, A., Zhao, F.: Energy aware consolidation for cloud computing. In: Proceedings of the 2008 Conference on Power Aware Computing and Systems, HotPower’08 (2008)

  44. Nathuji, R., Schwan, K.: VirtualPower: coordinated Power Management in Virtualized Enterprise Systems. In: Proceedings of Twenty-first ACM SIGOPS Symposium on Operating Systems Principles, SOSP ’07, pp. 265–278. ACM, New York, NY, USA (2007)

  45. Beloglazov, A., Buyya, R.: Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans. Parallel Distrib. Syst. 24(7), 1366–1379 (2013)

    Article  Google Scholar 

  46. Medina, V., García, Juan Manuel: A survey of migration mechanisms of virtual machines. ACM Comput. Surv. 46(3), 30:1–30:33 (2014)

    Article  MathSciNet  Google Scholar 

  47. Verma, A., Ahuja, P., Neogi, A.: pMapper: Power and migration cost aware application placement in virtualized systems. In: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, Middleware ’08, pp. 243–264. Springer, New York, USA (2008)

  48. Strunk, A., Dargie, W.: Does live migration of virtual machines cost energy? In: 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), pp. 514–521 (2013)

  49. Liu, H., Xu, C.-Z., Jin, H., Gong, J., Liao, X.: Performance and energy modeling for live migration of virtual machines. In: Proceedings of the 20th International Symposium on High Performance Distributed Computing, HPDC ’11, pp. 171–182. ACM, New York, NY, USA (2011)

  50. Fei, X., Fangming, L., Hai, J., Vasilakos, A.V.: Managing performance overhead of virtual machines in cloud computing. Proc. IEEE Surv. State Art Future Dir. 102, 11–31 (2014)

    Google Scholar 

  51. Menasce, D.: Two-level iterative queuing modeling of software contention. In: Proceedings of the 10th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems, 2002, pp. 267–276. MASCOTS (2002)

  52. Rao, L., Liu, X., Xie, L., Liu, W.: Coordinated energy cost management of distributed internet data centers in smart grid. IEEE Trans. Smart Grid 3(1), 50–58 (2012)

    Article  Google Scholar 

  53. Lin, M., Wierman, A., Andrew, L.L.H., Thereska, E.: Dynamic right-sizing for power-proportional data centers. IEEE/ACM Trans. Netw 21(5), 1378–1391 (2013)

    Article  Google Scholar 

  54. Branch and Cut. http://en.wikipedia.org/wiki/Branch_and_cut (2015)

  55. Storn, R., Price, Kenneth: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  56. Deb, Kalyanmoy: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Hoboken (2001)

    MATH  Google Scholar 

  57. Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., Warfield, A.: Xen and the art of virtualization. In: Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, SOSP ’03, pp. 164–177. ACM, New York, NY, USA (2003)

  58. Cain, H.W., Rajwar, R., Marden, M., Lipasti, M.H.: An Architectural Evaluation of Java TPC-W. In: Proceedings of the The Seventh International Symposium on High-Performance Computer Architecture, HPCA, pp. 229–240 (2001)

  59. Cooper, B.F, Silberstein, A., Tam, E., Ramakrishnan, R., Sears, R.: Benchmarking cloud serving systems with YCSB. In: Proceedings of the 1st ACM Symposium on Cloud Computing, SoCC ’10, pp. 143–154. ACM, New York, NY, USA (2010)

  60. SysBench: a System Performance Benchmark. https://launchpad.net/sysbench (2015)

  61. MySQL: The World’s Most Popular Open-Source Database. http://www.mysql.com/ (2015)

  62. Korf, R.E.: A new algorithm for optimal bin packing. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence, pp. 731–736. American Association for Artificial Intelligence Menlo Park, CA, USA (2002)

  63. Meisner, D., Sadler, C.M., Barroso, L.A., Weber, W.-D., Wenisch, T.F.: Power management of online data-intensive services. In: 38th Annual International Symposium on Computer Architecture (ISCA), 2011, pp. 319–330. IEEE (2011)

  64. Rameshan, N., Navarro, L., Monte, E., Vlassov, V.: Stay-away, protecting sensitive applications from performance interference. In: Proceedings of the 15th International Middleware Conference, Middleware ’14, pp. 301–312. ACM, New York, NY, USA (2014)

  65. Xu, F., Liu, F., Liu, L., Jin, H., Li, B., Li, B.: iAware: making live migration of virtual machines interference-aware in the cloud. IEEE Trans. Comput. 63(12), 3012–3025 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  66. Chiang, R.C., Huang, H.H.: TRACON: interference-aware scheduling for data-intensive applicationsin virtualized environments. IEEE Trans. Parallel Distrib. Syst. 25(5), 1349–1358 (2014)

    Article  Google Scholar 

  67. Novaković, D., Vasić, N., Novaković, S., Kostić, D., Bianchini, R.: DeepDive: transparently identifying and managing performance interference in virtualized environments. In: Proceedings of the 2013 USENIX Conference on Annual Technical Conference., pp. 219–230. USENIX ATC’13 (2013)

Download references

Acknowledgements

The research was supported in part by National Science Foundation of China under Grant Nos. 61202060, 912183001, 61173112 and 61221062; National High Technology Research and Development Program 863 of China under Grant No. 2013AA01A212; The Ministry of Education Innovation Research Team No. IRT13035; Key Projects in the National Science and Technology Pillar Program under Grant No. 2012BAH16F02.

Author information

Authors and Affiliations

  1. Software Engineering College, Zhengzhou University of Light Industry, No. 5 Dongfeng Road, Zhengzhou, 450002, People’s Republic of China

    Bo Wang

  2. Shaanxi Province Key Lab. of Satellite and Terrestrial Network Tech. R&D, Department of Computer Science and Technology, Xi’an Jiaotong University, No. 28 West Xianning Road, Xi’an, 710049, Shaanxi Province, People’s Republic of China

    Jun Liu

  3. Computer School, Beijing Information Science and Technology University, Beijing, 100192, People’s Republic of China

    Ying Song

  4. Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Yuzhong Sun

Authors
  1. Bo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuzhong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, B., Song, Y., Sun, Y. et al. Analysis model for server consolidation of virtualized heterogeneous data centers providing internet services. Cluster Comput 22, 911–928 (2019). https://doi.org/10.1007/s10586-018-2880-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2880-x

Keywords

Search

Navigation