Advertisement

A field-based service management and discovery method in multiple clouds context

  • Shuai ZhangEmail author
  • Xinjun MaoEmail author
  • Fu Hou
  • Peini Liu
Research Article
  • 8 Downloads

Abstract

In diverse and self-governed multiple clouds context, the service management and discovery are greatly challenged by the dynamic and evolving features of services. How to manage the features of cloud services and support accurate and efficient service discovery has become an open problem in the area of cloud computing. This paper proposes a field model of multiple cloud services and corresponding service discovery method to address the issue. Different from existing researches, our approach is inspired by Bohr atom model. We use the abstraction of energy level and jumping mechanism to describe services status and variations, and thereby to support the service demarcation and discovery. The contributions of this paper are threefold. First, we propose the abstraction of service energy level to represent the status of services, and service jumping mechanism to investigate the dynamic and evolving features as the variations and re-demarcation of cloud services according to their energy levels. Second, we present user acceptable service region to describe the services satisfying users’ requests and corresponding service discovery method, which can significantly decrease services search scope and improve the speed and precision of service discovery. Third, a series of algorithms are designed to implement the generation of field model, user acceptable service regions, service jumping mechanism, and user-oriented service discovery.We have conducted an extensive experiments on QWS dataset to validate and evaluate our proposed models and algorithms. The results show that field model can well support the representation of dynamic and evolving aspects of services in multiple clouds context and the algorithms can improve the accuracy and efficiency of service discovery.

Keywords

service field service energy level service jumping service management service discovery multiple clouds 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 61532004 and 61379051).

Supplementary material

11704_2018_8012_MOESM1_ESM.ppt (410 kb)
Supplementary material, approximately 409 KB.

References

  1. 1.
    Armbrust M. Above the clouds: a berkeley view of cloud computing. Sciences, 2009, 53(4): 50–58Google Scholar
  2. 2.
    Foster I, Zhao Y, Raicu I, Lu S. Cloud computing and grid computing 360-degree compared. In: Proceedings of the Grid Computing Environments Workshop. 2008, 1–10Google Scholar
  3. 3.
    Galante G, Bona L C E D. A survey on cloud computing elasticity. In: Proceedings of the 5th IEEE International Conference on Utility and Cloud Computing. 2012, 263–270Google Scholar
  4. 4.
    Srirama S N, Iurii T, Viil J. Dynamic deployment and auto-scaling enterprise applications on the heterogeneous cloud. In: Proceedings of the 9th IEEE International Conference on Cloud Computing. 2016, 927–932Google Scholar
  5. 5.
    Ferrer A J, Hernández F, Tordsson J, Elmroth E, Ali-Eldin A. OPTIMIS: a holistic approach to cloud service provisioning. Future Generation Computer Systems, 2012, 28(1): 66–77CrossRefGoogle Scholar
  6. 6.
    Petcu D. Consuming resources and services from multiple clouds. Journal of Grid Computing, 2014, 12(2): 321–345CrossRefGoogle Scholar
  7. 7.
    Zielinnski K, Szydlo T, Szymacha R, Kosinski J, Kosinska J. Adaptive SOA solution stack. IEEE Transactions on Services Computing, 2012, 5(2): 149–163CrossRefGoogle Scholar
  8. 8.
    Shi M, Liu J, Zhou D, Tang M, Cao B. WE-LDA: a word embeddings augmented LDA model forWeb services clustering. In: Proceedings of the IEEE International Conference on Web Services. 2017, 9–16Google Scholar
  9. 9.
    Ngan L D, Kirchberg M, Kanagasabai R. Review of semantic Web service discovery methods. In: Proceedings of the 6th World Congress on Services. 2010, 176–177Google Scholar
  10. 10.
    Ahmed M, Liu L, Hardy J, Yuan B. An efficient algorithm for partially matchedWeb services based on consumer’s QoS requirements. In: Proceedings of the 7th IEEE/ACMInternational Conference on Utility and Cloud Computing. 2014, 859–864Google Scholar
  11. 11.
    Wang Y, He Q, Yang Y. QoS-aware service recommendation for multitenant SaaS on the cloud. In: Proceedings of the IEEE International Conference on Services Computing. 2015, 178–185Google Scholar
  12. 12.
    Kumara B T G S, Paik I, Siriweera T, Koswatte K R. QoS aware service clustering to bootstrap the Web service selection. In: Proceedings of the IEEE International Conference on Services Computing. 2017, 233–240Google Scholar
  13. 13.
    Sousa G, Rudametkin W, Duchien L. Automated setup of multi-cloud environments for microservices applications. In: Proceedings of the 9th IEEE International Conference on Cloud Computing. 2016, 327–334Google Scholar
  14. 14.
    Kritikos K, Plexousakis D. Multi-cloud application design through cloud service composition. In: Proceedings of the 8th IEEE International Conference on Cloud Computing. 2015, 686–693Google Scholar
  15. 15.
    Grozev N, Buyya R. Inter-cloud architectures and application brokering: taxonomy and survey. Software: Practice and Experience, 2014, 44(3): 369–390Google Scholar
  16. 16.
    Liu G, Shen H. Minimum-cost cloud storage service across multiple cloud providers. In: Proceedings of the 36th IEEE International Conference on Distributed Computing Systems. 2016, 129–138Google Scholar
  17. 17.
    Kritikos K, Plexousakis D. Multi-cloud application design through cloud service composition. In: Proceedings of the 8th IEEE International Conference on Cloud Computing. 2015, 686–693Google Scholar
  18. 18.
    Elshater Y, Elgazzar K, Martin P. Godiscovery: Web service discovery made efficient. In: Proceedings of the IEEE International Conference on Web Services. 2015, 711–716Google Scholar
  19. 19.
    Xie F, Liu J, Tang M, Cao B, Lyu S. Correlation search ofWeb services. In: Proceedings of Asia-Pacific Services Computing Conference. 2014, 107–114Google Scholar
  20. 20.
    Liu L, Yao X, Qin L, Zhang M. Ontology-based service matching in cloud computing. In: Proceedings of the IEEE International Conference on Fuzzy Systems. 2014, 2544–2550Google Scholar
  21. 21.
    Rodriguez J M, Zunino A, Mateos C, Segura F O, Rodriguez E. Improving REST service discovery with unsupervised learning techniques. In: Proceedings of the 9th International Conference on Complex, Intelligent, and Software Intensive Systems. 2015, 97–104Google Scholar
  22. 22.
    Sha C, Wang K, Zhang K, Wang X, Zhou A. Diversifying top-k service retrieval. In: Proceedings of the IEEE International Conference on Services Computing. 2014, 227–234Google Scholar
  23. 23.
    Gao W, Wu J. A novel framework for service set recommendation in mashup creation. In: Proceedings of the IEEE International Conference on Web Services. 2017, 65–72Google Scholar
  24. 24.
    Yang W, Zhang C, Li J. An effective service discovery approach based on field theory and contribution degree in unstructured P2P networks. In: Proceedings of the 34th IEEE International Performance Computing and Communications Conference. 2015, 1–2Google Scholar
  25. 25.
    Alfazi A, Sheng Q Z, Qin Y, Noor T H. Ontology-based automatic cloud service categorization for enhancing cloud service discovery. In: Proceedings of the 19th IEEE International Enterprise Distributed Object Computing Conference. 2015, 151–158Google Scholar
  26. 26.
    Margaris D, Georgiadis P, Vassilakis C. A collaborative filtering algorithm with clustering for personalized Web service selection in business processes. In: Proceedings of the IEEE International Conference on Research Challenges in Information Science. 2015, 169–180Google Scholar
  27. 27.
    Wang Y, He Q, Ye D, Yang Y. Service selection based on correlated QoS requirements. In: Proceedings of the IEEE International Conference on Services Computing. 2017, 241–248Google Scholar
  28. 28.
    Ding S, Li Y, Wu D, Zhang Y, Yang S. Time-aware cloud service recommendation using similarity-enhanced collaborative filtering and ARIMA model. Decision Support Systems, 2018, 107: 103–115CrossRefGoogle Scholar
  29. 29.
    Ding S, Wang Z, Wu D, Olson D L. Utilizing customer satisfaction in ranking prediction for personalized cloud service selection. Decision Support Systems, 2017, 93: 1–10CrossRefGoogle Scholar
  30. 30.
    Ding S, Yang S, Zhang Y, Liang C, Xia C. Combining QoS prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems. Knowledge-Based Systems, 2014, 56: 216–225CrossRefGoogle Scholar
  31. 31.
    Torres R, Salas R. Self-adaptive fuzzy QoS-driven Web service discovery. In: Proceedings of the IEEE International Conference on Services Computing. 2011, 64–71Google Scholar
  32. 32.
    Zhong Y, Fan Y, Huang K, Tan W, Zhang J. Time-aware service recommendation for mashup creation in an evolving service ecosystem. In: Proceedings of the IEEE International Conference on Web Services. 2014, 25–32Google Scholar
  33. 33.
    Sun L, Wang S, Li J, Sun Q, Yang F. QoS uncertainty filtering for fast and reliable Web service selection. In: Proceedings of the IEEE International Conference on Web Services. 2014, 550–557Google Scholar
  34. 34.
    Bohr N. On the constitution of atoms and molecules. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1913, 26(153): 476–502CrossRefzbMATHGoogle Scholar
  35. 35.
    Kragh H. Niels Bohr and the Quantum Atom: the Bohr Model of Atomic Structure 1913–1925. Oxford: Oxford University Press, 2012CrossRefzbMATHGoogle Scholar
  36. 36.
    Al-Masri E, Mahmoud Q H. QoS-based discovery and ranking of Web services. In: Proceedings of the 16th IEEE International Conference on Computer Communications and Networks. 2007, 529–534Google Scholar
  37. 37.
    Arthur D, Vassilvitskii S. K-means++: the advantages of careful seeding. In: Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms. 2015, 1027–1035Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.National Laboratory for Parallel and Distributed ProcessingNational University of Defense TechnologyChangshaChina

Personalised recommendations