Skip to main content
Springer Nature Link
Log in

An evolution model of composed service based on global dependence net

  • Original Research Paper
  • Published:
Service Oriented Computing and Applications Aims and scope Submit manuscript

Abstract

Web service composition is a major way of constructing SOA-based applications. However, as uses’ requirements change, web services have to be recomposed correspondingly once again from the scratch. It will be rather time-consuming, error-prone and mostly fussy. To tackle the widespread requirements changes, we propose a novel approach that can make an existing composed service automatically grade to reach another new composed service in an evolutionary manner according to user’s requirements. An evolution model called control structure net is built to formally represent composition structure of a certain composed service based on interface dependence. Furthermore, a global dependence net, which provides an evolution knowledge base, is constructed by modeling all available web services. Evolution process is presented in detail and evolution reasoning algorithms are given to automatically remove invalid paths and make up necessary paths. Experimental results show that our proposed approach can correctly evolve to target composed service, and its performance also greatly surpasses that of classic service composition approach.

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

Notes

  1. http://114.55.84.231:8080/CloudManu/elevator-tranction-services.html.

References

  1. Alonso G, Casati F, Kuno H, Machiraju V (2010) Web services: concepts architectures and applications. Springer, Berlin

    MATH  Google Scholar 

  2. Yang J (2003) Web service componentization. Commun ACM 46(10):35–40

    Article  Google Scholar 

  3. Stal M (2006) Using architectural patterns and blueprints for service-oriented architecture. IEEE Softw 23(2):54–61

    Article  Google Scholar 

  4. Hwang SY, Lim EP, Lee CH, Chen CH (2009) Dynamic web service selection for reliable web service composition. IEEE Trans Serv Comput 1(2):104–116

    Article  Google Scholar 

  5. Oh SC, Lee D, Kumara SRT (2008) Effective web service composition in diverse and large-scale service networks. IEEE Trans Serv Comput 1(1):15–32

    Article  Google Scholar 

  6. Reffad H, Alti A (2018) New approach for optimal semantic-based context-aware cloud service composition for ERP. New Gener Comput 36:307–347

    Article  Google Scholar 

  7. Sellami W, Kacem HH, Kacem AH (2020) Dynamic provisioning of service composition in a multi-tenant SaaS environment. Netw Syst Manag 28(2):367–397

    Article  Google Scholar 

  8. Bucchiarone A, Marconi A, Pistore M, Raik H (2017) A context-aware framework for dynamic composition of process fragments in the internet of services. Int Serv Appl 8(1):61–63

    Google Scholar 

  9. Wang HB, Li JJ, Yu Q, Hong TJ, Yan J, Zhao W (2020) Integrating recurrent neural networks and reinforcement learning for dynamic service composition. Future Gener Comput Syst 107:551–563

    Article  Google Scholar 

  10. Qi J, Xu B, Xue Y, Wang K, Sun YF (2018) Knowledge based differential evolution for cloud computing service composition. Ambient Intell Humaniz Comput 9:565–574

    Article  Google Scholar 

  11. Atampore F, Dingel J, Rudie K (2019) A controller synthesis framework for automated service composition. Discrete Event Dyn Syst 29:297–365

    Article  MathSciNet  MATH  Google Scholar 

  12. Boudries F, Sadouki S, Tari A (2019) A bio-inspired algorithm for dynamic reconfiguration with end-to-end constraints in web services composition. Serv Oriented Comput Appl 13:251–260

    Article  Google Scholar 

  13. Zhou JJ, Yao XF (2017) DE-CaABC: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing. Adv Manuf Technol 90:1085–1103

    Article  Google Scholar 

  14. Liu X, Bouguettaya A (2007) Managing top-down changes in service-oriented enterprises. In: Proceedings of IEEE conference on web services, pp 1072–1079

  15. Fayala M, Mezni H (2019) Web service recommendation based on time-aware users clustering and multi-valued QoS prediction. Concurr Comput Pract Exp 32(7):e5603

    Google Scholar 

  16. Ding ZJ, Wang S, Pan M (2020) QoS-constrained service selection for networked microservices. IEEE Access 8:39285–39299

    Article  Google Scholar 

  17. Tiwari RK, Kumar R (2021) G-TOPSIS: a cloud service selection framework using Gaussian TOPSIS for rank reversal problem. J Supercomput 77:523–562

    Article  Google Scholar 

  18. Zhao L, Tan WA, Xie N, Huang L (2020) An optimal service selection approach for service-oriented business collaboration using crowd-based cooperative computing. J Appl Soft Comput 92:1–16

    Article  Google Scholar 

  19. Serrai W, Abdelli A, Mokdad L, Hammal Y (2017) Towards an efficient and a more accurate web service selection using MCDM methods. J Comput Sci 22:253–267

    Article  Google Scholar 

  20. Eisa M, Younas M, Basu K, Awan I (2020) Modelling and simulation of QoS-aware service selection in cloud computing. Simul Model Pract Theory 103:1–17

    Article  Google Scholar 

  21. Nagasundari S, Ravimaran S, Uma GV (2020) Enhancement of the dynamic computation-offloading service selection framework in mobile cloud environment. Wireless Pers Commun 112:225–241

    Article  Google Scholar 

  22. Wang YC, He Q, Zhang XY, Ye DY, Yang Y (2020) Efficient QoS-aware service recommendation for multi-tenant service-based systems in cloud. IEEE Trans Serv Comput 3(6):1045–1058

    Google Scholar 

  23. Kemerer CF, Slaughter S (1999) An empirical approach to study software evolution. IEEE Trans Softw Eng 25(4):493–509

    Article  Google Scholar 

  24. Oreizy P, Medvidovic N, Taylor RN (1998) Architecture-based runtime software evolution. In: Proceedings of IEEE conference on software engineering, pp 177–186

  25. Salameh HB, Ahmad A, Aljammal A (2016) Software evolution visualization techniques and methods—a systematic review. In: Proceedings of IEEE conference on computer science and information technology, pp 1–6

  26. Andrikopoulos V, Benbernou S, Papazoglou MP (2012) On the evolution of services. IEEE Trans Softw Eng 38(3):609–628

    Article  Google Scholar 

  27. Hu Q, Zhao Z, Du JW (2017) A clustering method for isomorphic evolution of web services. Sci Program 8:1–11

    Google Scholar 

  28. Chaturvedi A, Tiwari A, Binkley D, Chaturvedi S (2020) Service evolution analytics: change and evolution mining of a distributed system. IEEE Trans Eng Manage 64(1):137–148

    Article  Google Scholar 

  29. Gao ZF, Fan YS, Li X, Gu L, Wu C, Zhang J (2019) Discovery and analysis about the evolution of service composition patterns. J Web Eng 18(7):579–625

    Article  Google Scholar 

  30. Peng HF, Huang W, Fan DJ, Jin-Bao XU (2015) Method for evolution impact analysis of service composition based on data flow. Sci Technol Eng 15(1):257–262

    Google Scholar 

  31. Wang Y, Yang J, Zhao W, Su J (2012) Change impact analysis in service-based business processes. Serv Oriented Comput Appl 6(2):131–149

    Article  Google Scholar 

  32. Zuo W, Amghar Y (2014) Change-centric model for web service evolution. In: Proceedings of IEEE conference on web services, pp 712–713

  33. Romano D, Pinzger M (2012) Analyzing the evolution of web services using fine-grained changes. In: proceedings of IEEE conference on web services, pp 392–399

  34. Song W, Ma X, Cheung SC, Hu H, Jian L (2010) Preserving data flow correctness in process adaptation. In: Proceedings of IEEE conference on services computing, pp 9–16

  35. Lv C, Jiang W, Hu S, Wang J, Lu G, Liu Z (2015) Efficient dynamic evolution of service composition. IEEE Trans Serv Comput 11(4):630–643

    Article  Google Scholar 

  36. Wang S, Higashino WA, Hayes M, Capretz MAM (2014) Service evolution patterns. In: Proceedings of IEEE conference on web services, pp 201–208

  37. Liu X, Bouguettaya A, Wu J, Zhou L (2013) Ev-LCS: a system for the evolution of long-term composed services. IEEE Trans Serv Comput 6(1):102–115

    Article  Google Scholar 

  38. Xiaoxuan W, Aihua B, Jiajia M, Ke D, Zhen W (2011) Research on the semantic web oriented method for the evolution of composite service. Comput Sci 38(2):138–143

    Google Scholar 

  39. Tang XF (2007) A PETRI net-based semantic web service automatic composition method. J Softw 18(12):2991–3000

    MATH  Google Scholar 

  40. Cao H, Jin H, Wu S, Ibrahim S (2013) PETRI net based grid workflow verification and optimization. J Supercomput 66(3):1215–1230

    Article  Google Scholar 

  41. Xu H, Luo L, Xu D, Li Y (2016) Evolution of service composition based on QoS under the cloud computing environment. Anal Comput Sci 66–69

  42. He F (2013) Several key technologies on semantic web services composition. Science Press, Beijing, pp 33–40

    Google Scholar 

  43. Zhang ZJ, Zhang YM, Lu JW, Gao F, Gang X (2018) CMfgIA: a cloud manufacturing application mode for industry alliance. Int J Adv Manuf Technol 98(10):2967–2985

    Article  Google Scholar 

Download references

Acknowledgements

The work is supported by the National Natural Science Foundation of China under Grant No. 61976193, and Basic Public Welfare Research Project of Zhejiang Province under Grant No. LY19F020034.

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Zhejiang, China

    Yuanming Zhang, Zhoushuai Xu, Jiawei Lu & Gang Xiao

Authors
  1. Yuanming Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zhoushuai Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jiawei Lu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Gang Xiao
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yuanming Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Xu, Z., Lu, J. et al. An evolution model of composed service based on global dependence net. SOCA 15, 339–351 (2021). https://doi.org/10.1007/s11761-021-00318-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11761-021-00318-0

Keywords