Skip to main content
SpringerLink
Log in

Self-adaptive architecture for virtual machines consolidation based on probabilistic model evaluation of data centers in Cloud computing

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

By employing the virtual machines (VMs) consolidation technique at a virtualized data center, optimal mapping of VMs to physical machines (PMs) can be performed. The type of optimization approach and the policy of detecting the appropriate time to implement the consolidation process are influential in the performance of the consolidation technique. In a majority of researches, the consolidation approach merely focuses on the management of underloaded or overloaded PMs, while a number of VMs could also be in an underload or overload state. Managing an abnormal state of VM results in the postponement of PM getting into an abnormal state as well and affects the implementation time of the consolidation process. For the aim of optimal VM consolidation in this research, a self-adaptive architecture is presented to detect and manage underloaded and overloaded VMs /PMs in reaction to workload changes in the data center. The goal of consolidation process is employing the minimum number of active VMs and PMs, while guaranteeing the quality of service (QoS). Assessment criteria of QoS are two parameters including average number of requests in the PM buffer and average waiting time in the VM. To evaluate these two parameters, a probabilistic model of the data center is proposed by applying the queuing theory. The assessment results of the probabilistic model form a basis for decision-making in the modules of the proposed architecture. Numerical results obtained from the assessment of the probabilistic model via discrete-event simulator under various parameter settings confirm the efficiency of the proposed architecture in achieving the aims of the consolidation process.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Notes

  1. https://eclipse.org/downloads.

References

  1. Vaquero, L.M., Rodero-Merino, L., Caceres, J., Lindner, M.: A break in the clouds: towards a cloud definition. ACM SIGCOMM Comput. Commun. Rev. 39(1), 50–55 (2008)

    Article  Google Scholar 

  2. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)

    Article  Google Scholar 

  3. Varasteh, A., Goudarzi, M.: Server consolidation techniques in virtualized data centers: a survey. IEEE Syst. J. 11, 772–783 (2015)

    Article  Google Scholar 

  4. Pettey, C.: Gartner estimates ICT industry accounts for 2 percent of global CO\(_2\) emissions 14, 2013 (2007). https://www.gartner.com/newsroom/id/503867

  5. Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. Computer 40(12) (2007)

    Article  Google Scholar 

  6. Furht, B.: Cloud computing fundamentals. In: Handbook of Cloud Computing, pp. 3–19. Springer, Berlin (2010)

    Chapter  Google Scholar 

  7. Nathuji, R., Schwan, K.: Virtualpower: coordinated power management in virtualized enterprise systems. In: ACM SIGOPS Operating Systems Review, vol. 41, pp. 265–278. ACM (2007)

  8. 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 

  9. Setzer, T., Bichler, M.: Using matrix approximation for high-dimensional discrete optimization problems: server consolidation based on cyclic time-series data. Eur. J. Oper. Res. 227(1), 62–75 (2013)

    Article  MathSciNet  Google Scholar 

  10. da Silva, R.A., da Fonseca, N.L.: Topology-aware virtual machine placement in data centers. J. Grid Comput. 14(1), 75–90 (2016)

    Article  Google Scholar 

  11. Garg, S.K., Toosi, A.N., Gopalaiyengar, S.K., Buyya, R.: Sla-based virtual machine management for heterogeneous workloads in a cloud datacenter. J. Netw. Comput. Appl. 45, 108–120 (2014)

    Article  Google Scholar 

  12. Corradi, A., Fanelli, M., Foschini, L.: Vm consolidation: a real case based on openstack cloud. Future Gener. Comput. Syst. 32, 118–127 (2014)

    Article  Google Scholar 

  13. Hankendi, C., Coskun, A.K.: Scale 8 cap: Scaling-aware resource management for consolidated multi-threaded applications. ACM Trans. Des. Autom. Electron. Syst. 22(2), 30 (2017)

    Article  Google Scholar 

  14. Bila, N., Wright, E.J., Lara, E.D., Joshi, K., Lagar-Cavilla, H.A., Park, E., Goel, A., Hiltunen, M., Satyanarayanan, M.: Energy-oriented partial desktop virtual machine migration. ACM Trans. Comput. Syst. 33(1), 2 (2015)

    Article  Google Scholar 

  15. Kleinrock, L.: Queueing Systems, Volume 2: Computer Applications, vol. 66. Wiley, New York (1976)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  17. Navimipour, N.J., Rahmani, A.M., Navin, A.H., Hosseinzadeh, M.: Expert cloud: a cloud-based framework to share the knowledge and skills of human resources. Comput. Hum. Behav. 46, 57–74 (2015)

    Article  Google Scholar 

  18. Hanani, A., Rahmani, A.M., Sahafi, A.: A multi-parameter scheduling method of dynamic workloads for big data calculation in cloud computing. J. Supercomput. 73, 1–27 (2017)

    Article  Google Scholar 

  19. Ghomi, E.J., Rahmani, A.M., Qader, N.N.: Load-balancing algorithms in cloud computing: a survey. J. Netw. Comput. Appl. 88, 50–71 (2017)

    Article  Google Scholar 

  20. Rezaee, A., Rahmani, A.M., Movaghar, A., Teshnehlab, M.: Formal process algebraic modeling, verification, and analysis of an abstract fuzzy inference cloud service. J. Supercomput. 67(2), 345–383 (2014)

    Article  Google Scholar 

  21. Siadat, S., Rahmani, A.M., Navid, H.: Identifying fake feedback in cloud trust management systems using feedback evaluation component and bayesian game model. J. Supercomput. 73(6), 2682–2704 (2017)

    Article  Google Scholar 

  22. Mesbahi, M.R., Rahmani, A.M., Hosseinzadeh, M.: Highly reliable architecture using the 80/20 rule in cloud computing datacenters. Future Gener. Comput. Syst. 77, 77–86 (2017)

    Article  Google Scholar 

  23. Mesbahi, M., Rahmani, A.M., Chronopoulos, A.T.: Cloud light weight: A new solution for load balancing in cloud computing. In: Data Science & Engineering (ICDSE), 2014 International Conference on, pp. 44–50. IEEE (2014)

  24. Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(8), 1230–1242 (2013)

    Article  MathSciNet  Google Scholar 

  25. Shameli-Sendi, A., Pourzandi, M., Fekih-Ahmed, M., Cheriet, M.: Taxonomy of distributed denial of service mitigation approaches for cloud computing. J. Netw. Comput. Appl. 58, 165–179 (2015)

    Article  Google Scholar 

  26. Wang, Y., Wang, X.: Performance-controlled server consolidation for virtualized data centers with multi-tier applications. Sustain. Comput. 4(1), 52–65 (2014)

    Google Scholar 

  27. Qiu, X., Li, H., Wu, C., Li, Z., Lau, F.C.: Cost-minimizing dynamic migration of content distribution services into hybrid clouds. IEEE Trans. Parallel Distrib. Syst. 26(12), 3330–3345 (2015)

    Article  Google Scholar 

  28. Khazaei, H., Misic, J., Misic, 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 

  29. Esfandiarpoor, S., Pahlavan, A., Goudarzi, M.: Structure-aware online virtual machine consolidation for datacenter energy improvement in cloud computing. Comput. Electr. Eng. 42, 74–89 (2015)

    Article  Google Scholar 

  30. Kumar, M.R.V., Raghunathan, S.: Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds. J. Comput. Syst. Sci. 82(2), 191–212 (2016)

    Article  MathSciNet  Google Scholar 

  31. Mastroianni, C., Meo, M., Papuzzo, G.: Probabilistic consolidation of virtual machines in self-organizing cloud data centers. IEEE Trans. Cloud Comput. 1(2), 215–228 (2013)

    Article  Google Scholar 

  32. Li, X., Ventresque, A., Murphy, J., Thorburn, J.: Soc: satisfaction-oriented virtual machine consolidation in enterprise data centers. Int. J. Parallel Program. 44(1), 130–150 (2016)

    Article  Google Scholar 

  33. Mazumdar, S., Pranzo, M.: Power efficient server consolidation for cloud data center. Future Gener. Comput. Syst. 70, 4–16 (2017)

    Article  Google Scholar 

  34. Rao, K.S., Thilagam, P.S.: Heuristics based server consolidation with residual resource defragmentation in cloud data centers. Future Gener. Comput. Syst. 50, 87–98 (2015)

    Article  Google Scholar 

  35. Khani, H., Latifi, A., Yazdani, N., Mohammadi, S.: Distributed consolidation of virtual machines for power efficiency in heterogeneous cloud data centers. Comput. Electr. Eng. 47, 173–185 (2015)

    Article  Google Scholar 

  36. Jiang, J., Feng, Y., Zhao, J., Li, K.: Dataabc: a fast abc based energy-efficient live vm consolidation policy with data-intensive energy evaluation model. Future Gener. Comput. Syst. 74, 132–141 (2017)

    Article  Google Scholar 

  37. Sedaghat, M., Hernández-Rodriguez, F., Elmroth, E.: Decentralized cloud datacenter reconsolidation through emergent and topology-aware behavior. Future Gener. Comput. Syst. 56, 51–63 (2016)

    Article  Google Scholar 

  38. Li, Z., Yan, C., Yu, X., Yu, N.: Bayesian network-based virtual machines consolidation method. Future Gener. Comput. Syst. 69, 75–87 (2017)

    Article  Google Scholar 

  39. Li, M., Bi, J., Li, Z.: Improving consolidation of virtual machine based on virtual switching overhead estimation. J. Netw. Comput. Appl. 59, 158–167 (2016)

    Article  Google Scholar 

  40. Rajabzadeh, M., Haghighat, A.T.: Energy-aware framework with markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers. J. Supercomput. 73(5), 2001–2017 (2017)

    Article  Google Scholar 

  41. 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 

  42. Gaggero, M., Caviglione, L.: Predictive control for energy-aware consolidation in cloud datacenters. IEEE Tran. Control Syst. Technol. 24(2), 461–474 (2016)

    Google Scholar 

  43. Goudarzi, H., Pedram, M.: Energy-efficient virtual machine replication and placement in a cloud computing system. In: Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on, pp. 750–757. IEEE (2012)

  44. Yang, J.S., Liu, P., Wu, J.J.: Workload characteristics-aware virtual machine consolidation algorithms. In: Cloud Computing Technology and Science (CloudCom), 2012 IEEE 4th International Conference on, pp. 42–49. IEEE (2012)

  45. Huang, Z., Tsang, D.H.: M-convex vm consolidation: towards a better vm workload consolidation. IEEE Trans. Cloud Comput. 4(4), 415–428 (2016)

    Article  MathSciNet  Google Scholar 

  46. Sansottera, A., Zoni, D., Cremonesi, P., Fornaciari, W.: Consolidation of multi-tier workloads with performance and reliability constraints. In: High Performance Computing and Simulation (HPCS), 2012 International Conference on, pp. 74–83. IEEE (2012)

  47. Bui, D.M., Yoon, Y., Huh, E.N., Jun, S., Lee, S.: Energy efficiency for cloud computing system based on predictive optimization. J. Parallel Distrib. Comput. 102, 103–114 (2017)

    Article  Google Scholar 

  48. Fox, A., Turner, A., Kim, H.S.: Resource contention-aware virtual machine management for enterprise applications. In: Global Communications Conference (GLOBECOM), 2012 IEEE, pp. 1641–1646. IEEE (2012)

  49. Deng, W., Liu, F., Jin, H., Liao, X., Liu, H.: Reliability-aware server consolidation for balancing energy-lifetime tradeoff in virtualized cloud datacenters. Int. J. Commun. Syst. 27(4), 623–642 (2014)

    Article  Google Scholar 

  50. Hallawi, H., Mehnen, J., He, H.: Multi-capacity combinatorial ordering ga in application to cloud resources allocation and efficient virtual machines consolidation. Future Gener. Comput. Syst. 69, 1–10 (2017)

    Article  Google Scholar 

  51. Farahnakian, F., Ashraf, A., Pahikkala, T., Liljeberg, P., Plosila, J., Porres, I., Tenhunen, H.: Using ant colony system to consolidate vms for green cloud computing. IEEE Trans. Serv. Comput. 8(2), 187–198 (2015)

    Article  Google Scholar 

  52. Cao, Z., Dong, S.: Dynamic VM consolidation for energy-aware and SLA violation reduction in cloud computing. In: Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2012 13th International Conference on, pp. 363–369. IEEE (2012)

  53. Ferreto, T., De Rose, C.A., Heiss, H.U.: Maximum migration time guarantees in dynamic server consolidation for virtualized data centers. In: European Conference on Parallel Processing, pp. 443–454. Springer, Berlin (2011)

    Chapter  Google Scholar 

  54. Ross, S.M.: Introduction to probability models. Academic Press, Cambridge (2014)

    MATH  Google Scholar 

  55. Wang, Y., Chen, S., Goudarzi, H., Pedram, M.: Resource allocation and consolidation in a multi-core server cluster using a markov decision process model. In: Quality Electronic Design (ISQED), 2013 14th International Symposium on, pp. 635–642. IEEE (2013)

  56. Vilaplana, J., Solsona, F., Teixidó, I., Mateo, J., Abella, F., Rius, J.: A queuing theory model for cloud computing. J. Supercomput. 69(1), 492–507 (2014)

    Article  Google Scholar 

  57. Cao, Z., Dong, S.: An energy-aware heuristic framework for virtual machine consolidation in cloud computing. J. Supercomput. 69(1), 429–451 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Reza Mohammadi Bahram Abadi & Sasan Hossein Alizadeh

  2. Computer Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Amir Masoud Rahmani

  3. Computer Science, University of Human Development, Sulaimanyah, Iraq

    Amir Masoud Rahmani

Authors
  1. Reza Mohammadi Bahram Abadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Masoud Rahmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sasan Hossein Alizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amir Masoud Rahmani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohammadi Bahram Abadi, R., Rahmani, A.M. & Hossein Alizadeh, S. Self-adaptive architecture for virtual machines consolidation based on probabilistic model evaluation of data centers in Cloud computing. Cluster Comput 21, 1711–1733 (2018). https://doi.org/10.1007/s10586-018-2806-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2806-7

Keywords

Search

Navigation