SpringerLink
Log in

Optimal computing resource allocation algorithm in cloud computing based on hybrid differential parallel scheduling

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In order to improve the ability of resource allocation and scheduling in cloud computing, optimize resource allocation and improve the efficiency of cloud computing, an optimal computing resource allocation algorithm in cloud computing based on hybrid differential parallel scheduling is proposed. In this algorithm, the models of data structure and gird structure of computing resource allocation in cloud computing are constructed, and the sample clustering analysis method of resource information flow is used to classify the attributes of computing resources; the sliding window of computing resource allocation is divided into multiple sub-windows; characteristic quantities associated with computing resource allocation attributes are selected in neighbor samples as standard vector sets for adaptive pairing; the computing resources in cloud computing are done with singular value decomposition and the resource allocation is transformed into the least square problem; the hybrid differential parallel computing method is used for optimal solution finding of resource scheduling vector set to prevent the allocation results from falling into local optimal solution, so as to improve the global convergence of resource allocation. The simulation results show that when the method proposed in this paper is used for resource allocation in clouding computing, the clustering performance is high and the convergence control ability to computing resources with different attributes is high; the allocation speedup can reach 3.67, which is improved by 14.65 and 7.43% respectively compared with that in the traditional HEFT algorithm and HCNF algorithm; when the number of allocate nodes is 100, the overhead is only 5.6, which is reduced by 14.56 and 8.33% than that in traditional HEFT algorithm and HCNF algorithm. So it shows that the proposed method has a higher practical application performance for its shorter execution time and lower overhead.

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
EUR 32.99 /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 includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Bi, A.Q., Wang, S.T.: Transfer affinity propagation clustering algorithm based on Kullback-Leiber distance. JEIT 38(8), 2076–2084 (2016)

    Google Scholar 

  2. Alahmad, B.N., Gopalakrishnan, S.: Energy efficient task partitioning and real-time scheduling on heterogeneous multiprocessor platforms with QoS requirements. Sustain. Comput. 1(4), 314–328 (2011)

    Google Scholar 

  3. Chillet, D., Eiche, A., Pillement, S., et al.: Real-time scheduling on heterogeneous system-on-chip architectures using an optimised artificial neural network. J. Syst. Archit. 57(4), 340–353 (2011)

    Article  Google Scholar 

  4. Guo, X.Y.: Simulation and analysis on uncertain attenuation property of underwater acoustic signal for oil field pipe. Comput. Simul. 31(3), 118–121 (2014)

    Google Scholar 

  5. Femando, N., Hong, Y., Viterbo, E.: Flip-OFDM for unipolar communication systems. IEEE Trans. Commun. 60(12), 3726–3733 (2012)

    Article  Google Scholar 

  6. Patricia, N., Caputo, B.: Learning to learn, from transfer learning to domain adaptation: a unifying perspective. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 1442–1449 (2014)

  7. Wang, Z., Su, X.: Dynamically hierarchical resource-allocation algorithm in cloud computing environment. J. Supercomput. 71(7), 2748–2766 (2015)

    Article  Google Scholar 

  8. Sun, L., Guo, C.H.: Incremental affinity propagation clustering based on message passing. IEEE Trans. Knowl. Data Eng. 26(11), 2731–2744 (2014)

    Article  Google Scholar 

  9. Ling, C.G., Wang, H.Z.: Optimization research on differential evolution algorithm and its application in clustering analysis. Modern Electron. Technol. 39(13), 103–107 (2016)

    Google Scholar 

  10. Li, M.D., Zhao, H., Weng, X.W., et al.: Differential evolution based on optimal Gaussian random walk and individual selection strategies. Control Decis. 31(08), 1379–1386 (2016)

    MATH  Google Scholar 

  11. Patel, V.M., Nguyen, H.V.: VIDAL R.: latent space sparse and low-rank subspace clustering. IEEE J. Sel. Top. Signal Process. 9(4), 691–701 (2015)

    Article  Google Scholar 

  12. Sun, L.J., Chen, X.D., Han, C., et al.: New fuzzy-clustering algorithm for data stream. JEIT 37(7), 1620–1625 (2015)

    Google Scholar 

  13. Bi, A.Q., Dong, A.M., Wang, S.T.: A dynamic data stream clustering algorithm based on probability and exemplar. J. Comput. Res. Dev. 53(5), 1029–1042 (2016)

    Google Scholar 

  14. Jiang, Y.Z., Chung, F.L., Wang, S.T., et al.: Collaborative fuzzy clustering from multiple weighted views. IEEE Trans. Cybern. 45(4), 688–701 (2015)

    Article  Google Scholar 

  15. Wang, W.B.: Simulation of cloud computing resource optimization scheduling under the interference of debris resources. Comput. Simul. 33(7), 65–71 (2016)

    Google Scholar 

  16. Dai, L., Li, J.H.: An optimal resource allocation algorithm in cloud computing environment. Appl. Mech. Mater. 733(23), 779–783 (2015)

    Google Scholar 

  17. Hu, W., Li, K., Xu, J., et al.: Cloud-computing-based resource allocation research on the perspective of improved ant colony algorithm. In: IEEE International Conference on Computer Science and Mechanical Automation, pp. 76–80 (2016)

  18. Shao, Y.: Virtual resource allocation based on improved particle swarm optimization in cloud computing environment. Int. J. Grid Distrib. Comput. 8(1), 228–233 (2015)

    Google Scholar 

  19. Liu, Y., Lee, M.J.: An adaptive resource allocation algorithm for partitioned services in mobile cloud computing. In: IEEE Service-Oriented System Engineering, pp. 209–215 (2015)

Download references

Author information

Authors and Affiliations

  1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xu Zhou, 221116, China

    **g Wei

  2. College of Construction Management, Jiangsu Vocational Institute of Architectural Technology, Xu Zhou, 221116, China

    **g Wei

  3. College of Civil Engineering, Hunan City University, Yiyang Hunan, 413000, China

    **n-fa Zeng

Authors
  1. **g Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. **n-fa Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to **n-fa Zeng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wei, J., Zeng, Xf. Optimal computing resource allocation algorithm in cloud computing based on hybrid differential parallel scheduling. Cluster Comput 22 (Suppl 3), 7577–7583 (2019). https://doi.org/10.1007/s10586-018-2138-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2138-7

Keywords

Search

Navigation