Skip to main content
Log in

Improved differential search algorithm based dynamic resource allocation approach for cloud application

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The performance of a service-based system (SBS) in a cloud environment may not satisfy service-level agreement (SLA) constraints when the system load changes. To improve the profits of resource providers and satisfy the global SLA, it is necessary to dynamically allocate proper resource for SBS based on the forecasted system load. By analyzing the complex workflow of the SBS, this paper proposes improved differential search algorithm-based dynamic resource allocation approach which adopts an active mechanism to respond to the change of system load so as to ensure the timely response to change. The dynamic resource allocation model based on costs optimization and SLA constraint is then proposed. The improved differential search algorithm is designed to solve the dynamic resource allocation model. This paper proposes a load forecasting approach based on deep belief networks (DBNs) in order to accurately forecast the load to support dynamic resource allocation. Experimental results show that the approach performs well in terms of the quality of the solution compared with other related approaches.

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

Access this article

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

Similar content being viewed by others

References

  1. Marco A, Claudio AA, Ernesto D, Filippo G (2016) A cost-effective certification-based service composition for the cloud. IEEE Int Conf Serv Comput 2016:58–65

    Google Scholar 

  2. Faouzi BC, Samir T (2016) An efficient algorithm for the bursting of service-based applications in hybrid clouds. IEEE Trans Serv Comput 9(3):257–267

    Google Scholar 

  3. Rosa L, Rodrigues L, Lopes A, Hiltunen M (2013) Self-management of adaptable component-based applications. IEEE Trans Softw Eng 39(3):403–421

    Article  Google Scholar 

  4. Zhao XT, Zhang B, Zhang CS (2015) Service selection based resource allocation for SBS in cloud environments. J Softw 26(4):867–885

    MathSciNet  Google Scholar 

  5. Stephen SY, Ho GA (2009) Adaptive resource allocation for service-based systems. Int J Softw Inform 3(4):483–499

    Google Scholar 

  6. Mohd HM, Azizol A, Shamala S, Masnida H (2014) A survey on resource allocation and monitoring in cloud computing. Int J Mach Learn Comput 4(1):31–38

    Google Scholar 

  7. Linlin W, Saurabh KG, Steve V, Rajkumar B (2014) SLA-based resource provisioning for hosted software as a service applications in cloud computing environments. IEEE Trans Serv Comput 7(3):465–485

    Article  Google Scholar 

  8. Li S, Zhou YF, Jiao L (2015) Towards operational cost minimization in hybrid clouds for dynamic resource provisioning with delay-aware optimization. IEEE Trans Serv Comput 8(3):398–409

    Article  Google Scholar 

  9. Huang CJ, Guan CT, Chen HM, Wang YW (2013) An adaptive resource management scheme in cloud computing. Eng Appl Artif Intell 26(1):382–389

    Article  Google Scholar 

  10. Jonathan C, Dusit N (2007) Joint optimization of resource provisioning in cloud computing. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2015.2476812

    Google Scholar 

  11. Qian Z, Gagan A (2012) Resource provisioning with budget constraints for adaptive applications in cloud environments. IEEE Trans Serv Comput 5(4):497–511

    Article  Google Scholar 

  12. Lee TJ, Huang KC, Shen BJ, Chang H-Y (2012) Resource allocation and dynamic provisioning for service-oriented applications in cloud environment. In: IEEE 4th international conference on cloud computing technology and science, 839–844

  13. Pandey S, Wu LL, Guru SM (2010) A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. Int Conf Adv Inf Netw Appl, 400–407

  14. Chen MS, Huang SJ, Fu X et al (2016) Statistical model checking-based evaluation and optimization for cloud workflow resource allocation. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2016.2586067

    Google Scholar 

  15. Lin WW, Wang JZ, Liang C (2011) A threshold-based dynamic resource allocation scheme for cloud computing. Proced Eng 33:695–703

    Article  Google Scholar 

  16. Liu J,Teo K L (2015) An exact penalty function-based differential search algorithm for constrained global optimization. Soft Comput, 1–9

  17. Zhang B, Wang L, Zhao XT (2015) A novel modeling method for relationships between resources and service performance. J Northeast Univ 36(6):773–776

    Google Scholar 

  18. Li D (2014) A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal and Inf Process 3:1–29

    Article  Google Scholar 

  19. Cardoso J, Miller J (2004) Quality of service for workflows and web service processes. J Web Semant 1:281–308

    Article  Google Scholar 

  20. Civicioglu P (2012) Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci 46:229–247

    Article  Google Scholar 

  21. Ruben Van den Bossche, Kurt Vanmechelen, Jan Broeckhove (2014) Optimizing IaaS reserved contract procurement using load prediction. In: 2014 IEEE 7th international conference on cloud computing, Anchorage, pp. 88–95

  22. Ali NH, Hassan GM (2014) Kalman filter tracking. Int J Comput Appl 89(9):15–18

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation Program of China (61572116, 61572117, 61502089).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anxiang Ma.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, A., Gao, Y., Huang, L. et al. Improved differential search algorithm based dynamic resource allocation approach for cloud application. Neural Comput & Applic 31, 3431–3442 (2019). https://doi.org/10.1007/s00521-017-3280-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-3280-5

Keywords

Navigation