Skip to main content

Advertisement

Springer Nature Link
Log in

A replicas placement approach of component services for service-based cloud application

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Placement of component service replicas for service-based application (SBA) in cloud environments has become increasingly important. A SBA is usually communication topology-aware, and component service replicas possess stronger data dependency than data replicas; therefore, there are huge amounts of communication between the computer nodes that are used to place component service replicas. Because the conventional methods do not consider the communication topology of component services and the relations between computer nodes, they are not appropriate for placing component service replicas. In this paper, we propose a topological matching-based component service replicas placement method that takes into account not only the topology of SBAs but also the communication performance between different computing nodes. This method first discovers the communication topology of a SBA via multi-scale graph clustering then acquires the topology of computer nodes through spectral clustering. It then places the component service replicas by matching the above two topological structures. Comprehensive experiments are conducted by comparing the performance of our method with those of other methods based on CloudSim simulation software. The results show the effectiveness of our method for improving the performance of SBAs.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

References

  1. Anderson, D.P.: Boinc: a system for public-resource computing and storage. In: The fifth IEEE/ACM international workshop on grid computing, pp. 4–10 (2004). doi:10.1109/GRID.2004.14

  2. Araujo, F., Boychenko, S., Barbosa, R., et al.: Replica placement to mitigate attacks on clouds. J. Internet Serv. Appl. 5(1), 1–13 (2014). doi:10.1186/s13174-014-0007-z

    Article  Google Scholar 

  3. Boru, D., Kliazovich, D., Granelli, F., et al.: Energy-efficient data replication in cloud computing datacenters. Cluster Comput. 18, 385–402 (2015)

    Article  Google Scholar 

  4. Burger, M., Zelazo, D., Allgower, F.: Hierarchical clustering of dynamical networks using a saddle-point analysis. J. IEEE Trans. Autom. Control 58(1), 113–124 (2013). doi:10.1109/TAC.2012.2206695

    Article  MATH  MathSciNet  Google Scholar 

  5. Calheiros, R.N., Ranjan, R., Beloglazov, A., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)

  6. Chen, N., Xu, Z., Xia, M.: Hierarchical hesitant fuzzy K-means clustering algorithm. J. Appl. Math. 29(1), 1–17 (2014). doi:10.1007/s11766-014-3091-8

  7. Chen, K., Zheng, W.M.: Cloud computing: system instances and current research. J. Softw. 20(5), 1337–1348 (2009). doi:10.3724/SP.J.1001.2009.03493

    Article  Google Scholar 

  8. Ding, C., He, X.: Cluster merging and splitting in hierarchical clustering algorithms. In: The IEEE international conference on data mining, pp. 139–146 (2002). doi:10.1109/ICDM.2002.1183896

  9. Durao, F., Carvalho, J.F.S., Fonseka, A., Garcia, V.C.: A systematic review on cloud computing. J. Supercomput. 68(3), 1321–1346 (2014). doi:10.1007/s11227-014-1089-x

    Article  Google Scholar 

  10. Ghanbari, H., Litoiu, M., Pawluk, P., et al.: Replica placement in cloud through simple stochastic model predictive control. In: The 7th IEEE international conference on cloud computing (CLOUD), pp. 80–87 (2014). doi:10.1109/CLOUD.2014.21

  11. Hu, C., Xu, Z., et al.: Semantic link network based model for organizing multimedia big data. IEEE Trans. Emerg. Top Comput. 2(3), 376–387 (2014)

    Article  Google Scholar 

  12. Hussain, M., Abdulsalam, H.M.: Software quality in the clouds: a cloud-based solution. Cluster Comput. 17(2), 389–402 (2014)

    Article  Google Scholar 

  13. Jung, J.-K., et al.: Improved CloudSim for Simulating QoS-Based Cloud Services. Ubiquitous Information Technologies and Applications, pp. 537–545. Springer, Netherlands (2013). doi: 10.1007/978-94-007-5857-5_58

  14. Kalayci, S., Dasgupta, G., Fong, L.: Distributed and adaptive execution of condor DAGMan workflows. In: SEKE, pp. 587–590 (2010)

  15. Ko, B.J., Rubenstein, D.: Distributed self-stabilizing placement of replicated resources in emerging networks. J. IEEE/ACM Trans. Netw. 13(3), 476–487 (2005). doi:10.1109/TNET.2005.850196

    Article  Google Scholar 

  16. Kumar, R., Sahoo, G.: Cloud computing simulation using CloudSim. (2014). arXiv:1403.3253. doi:10.14445/22315381/IJETT-V8P216

  17. Leitner, P., Hummer, W., Dustdar, S.: Cost-based optimization of service compositions. J. IEEE Trans. Serv. Comput. 6(2), 239–251 (2013). doi:10.1109/TSC.2011.53

    Article  Google Scholar 

  18. Luo, X., Xu, Z., Yu, J., Chen, X.: Building association link network for semantic link on web resources. IEEE Trans. Autom. Sci. Eng. 8(3), 482–494 (2011)

    Article  Google Scholar 

  19. Newman, M.E.J.: Analysis of weighted networks. J. Phys. Rev. E. 70(5), 056131 (2004). doi:10.1103/PhysRevE.70.056131

    Article  Google Scholar 

  20. Noack, A.: Energy models for graph clustering. J. Graph Algorithms Appl. 11(2), 453–480 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  21. Serrano, N., Hernantes, J., Gallardo, G.: Service-oriented architecture and legacy systems. IEEE J. Softw. 31(5), 15–19 (2014). doi:10.1109/MS.2014.125

  22. Spielman, D.: Spectral Graph Theory. Lecture Notes. Yale University, New Haven (2009)

    Google Scholar 

  23. Tang, X., Xu, J.: QoS-aware replica placement for content distribution. J IEEE Trans. Parallel Distrib. Syst. 16(10), 921–932 (2005). doi:10.1109/TPDS.2005.126

    Article  Google Scholar 

  24. Tartare, G., Hamad, D., Azahaf, M., et al.: Spectral clustering applied for dynamic contrast-enhanced MR analysis of time-intensity curves. J. Comput. Med. Imaging Graph. 38(8), 702–713 (2014). doi:10.1016/j.compmedimag.2014.07.005

    Article  Google Scholar 

  25. Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  26. Wada, H., Suzuki, J., Yamano, Y., et al.: Evolutionary deployment optimization for service—oriented clouds. J. Softw. Pract. Exp. 41(5), 469–493 (2011). doi:10.1002/spe.1032

    Article  Google Scholar 

  27. Wang, H., Liu, P., Wu, J.: A QoS-aware heuristic algorithm for replica placement. In: Proceedings of the 7th IEEE/ACM international conference on grid computing, pp. 96–103 (2006). doi:10.1109/ICGRID.2006.311003

  28. Xu, Z., et al.: Crowdsourcing based description of urban emergency events using social media big data. IEEE Trans. Cloud Comput. doi:10.1109/TCC.2016.2517638

  29. Xu, Z., et al.: Semantic based representing and organizing surveillance big data using video structural description technology. J. Syst. Softw. 102, 217–225 (2015)

    Article  Google Scholar 

  30. Xu, Z., et al.: Semantic enhanced cloud environment for surveillance data management using video structural description. Computing 98(1–2), 35–54 (2016)

    Article  MATH  MathSciNet  Google Scholar 

  31. Yau, S.S., Ye, N., Sarjoughian, H.S., et al.: Toward development of adaptive service-based software systems. J. IEEE Trans. Serv. Comput. 2(3), 247–260 (2009). doi:10.1109/TSC.2009.17

    Article  Google Scholar 

  32. Zhao, W., Xu, X., Wang, Z.: Load balancing-based replica placement strategy in data grid system. In: The third IEEE international conference on education technology and training, pp. 314–316 (2010)

  33. Zheng, Z, Zhang, Y, Lyu, M.R.: CloudRank: a QoS-driven component ranking framework for cloud computing. In: The 29th IEEE symposium on reliable distributed systems, pp. 184–193 (2010). doi:10.1109/SRDS.2010.29

Download references

Acknowledgments

This research was supported by the National Natural Science Foundation Program of China (61572116, 61572117, 61502089), the National key Technology R&D Program of the Ministry of Science and Technology (2015BAH09F02), the Provincial Scientific and Technological Project (2015302002), and the Special Fund for Fundamental Research of Central Universities of Northeastern University (N150408001, N150404009).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Jiaxuan Wu, Bin Zhang, Lei Yang, Peng Wang & Changsheng Zhang

Authors
  1. Jiaxuan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Changsheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changsheng Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, J., Zhang, B., Yang, L. et al. A replicas placement approach of component services for service-based cloud application. Cluster Comput 19, 709–721 (2016). https://doi.org/10.1007/s10586-016-0552-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-016-0552-2

Keywords