Skip to main content
Springer Nature Link
Log in

A Multi-view Learning Approach for the Autonomic Management of Big Services

  • Conference paper
  • First Online:
Web Information Systems Engineering – WISE 2021 (WISE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13081))

Included in the following conference series:

Abstract

Big services have recently emerged as a solution to process, encapsulate and offer huge volumes of data as a service. However, its management operations are beyond the ability of human administrators, due to several challenges including big services’ large-scale nature and complexity, the heterogeneity of its components, the dynamicity and uncertainty of its hosting cloud environments. To cope with these challenges, we endow big services with self-* capabilities and we propose an autonomic computing architecture for big services. We also take advantage of two recent technologies called knowledge graphs and multi-view learning, to represent the managed big service’s information (service descriptions, services’ and data sources’ quality levels, management policies) as a heterogeneous information network. Finally, a decision mechanism to select and trigger the appropriate management policies is defined and validated through a set of experiments.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://shorturl.at/wOZ48.

  2. 2.

    https://wsdream.github.io/.

References

  1. Bhaskar, B., Jatoth, C., Gangadharan, G., Fiore, U.: A mapreduce-based modified grey wolf optimizer for qos-aware big service composition. Concurr. Comput. Pract. Exp. 32(8), e5351 (2020)

    Article  Google Scholar 

  2. Bruning, S., Weissleder, S., Malek, M.: A fault taxonomy for service-oriented architecture. In: 10th IEEE High Assurance Systems Engineering Symposium (HASE’07), pp. 367–368. IEEE (2007)

    Google Scholar 

  3. Chen, X., Jia, S., Xiang, Y.: A review: knowledge reasoning over knowledge graph. Expert Syst. Appl. 141, 112948 (2019)

    Article  Google Scholar 

  4. Cheng, Y., Leon-Garcia, A., Foster, I.: Toward an autonomic service management framework: a holistic vision of soa, aon, and autonomic computing. IEEE Commun. Mag. 46(5), 138–146 (2008)

    Article  Google Scholar 

  5. Ding, J., Zhang, D., Hu, X.H.: A framework for ensuring the quality of a big data service. In: International Conference on Services Computing (SCC), pp. 82–89. IEEE (2016)

    Google Scholar 

  6. E, X., Han, J., Wang, Y., Liu, L.: Big data-as-a-service: definition and architecture. In: 15th IEEE International Conference on Communication Technology, pp. 738–742 (2013)

    Google Scholar 

  7. Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs (2017). arXiv preprint arXiv:1706.02216

  8. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  Google Scholar 

  9. Huang, L., Zhao, Q., Li, Y., Wang, S., Sun, L., Chou, W.: Reliable and efficient big service selection. Inf. Syst. Front. 19(6), 1273–1282 (2017)

    Article  Google Scholar 

  10. Jatoth, C., Gangadharan, G., Fiore, U., Buyya, R.: Qos-aware big service composition using mapreduce based evolutionary algorithm with guided mutation. Future Gener. Comput. Syst. 86, 1008–1018 (2018)

    Article  Google Scholar 

  11. Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)

    Article  MathSciNet  Google Scholar 

  12. Kim, B., Kim, J., Chae, H., Yoon, D., Choi, J.W.: Deep neural network-based automatic modulation classification technique. In: International Conference on Information and Communication Technology Convergence, pp. 579–582. IEEE (2016)

    Google Scholar 

  13. Landset, S., Khoshgoftaar, T.M., Richter, A.N., Hasanin, T.: A survey of open source tools for machine learning with big data in the hadoop ecosystem. J. Big Data 2(1), 24 (2015)

    Article  Google Scholar 

  14. Lin, Z., et al.: A structured self-attentive sentence embedding (2017). arXiv preprint arXiv:1703.03130

  15. Liu, M., Tu, Z., Xu, X., Wang, Z.: A data-driven approach for constructing multilayer network-based service ecosystem models (2020). arXiv:2004.10383

  16. Mezni, H., Sellami, M., Aridhi, S., Ben Charrada, F.: Towards big services: a synergy between service computing and parallel programming. In: Computing, pp. 1–36 (2021)

    Google Scholar 

  17. Mukherjee, T., Nath, A.: Big data analytics with service-oriented architecture. In: Exploring Enterprise Service Bus in the Service-Oriented Architecture Paradigm, pp. 216–234. IGI Global (2017)

    Google Scholar 

  18. Papazoglou, M.P., Traverso, P., Dustdar, S., Leymann, F.: Service-oriented computing: state of the art and research challenges. Computer 40(11), 38–45 (2007)

    Article  Google Scholar 

  19. Sellami, M., Mezni, H., Hacid, M.S.: On the use of big data frameworks for big service composition. J. Netw. Comput. Appl. 166, 102732 (2020)

    Article  Google Scholar 

  20. Siddiqa, A., et al.: A survey of big data management: taxonomy and state-of-the-art. J. Netw. Comput. Appl. 71, 151–166 (2016)

    Article  Google Scholar 

  21. Sun, S.: A survey of multi-view machine learning. Neural Comput. Appl. 23(7–8), 2031–2038 (2013)

    Article  Google Scholar 

  22. Taherkordi, A., Eliassen, F., Horn, G.: From iot big data to iot big services. In: International Symposium on Applied Computers, pp. 485–491. ACM (2017)

    Google Scholar 

  23. Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)

    Article  Google Scholar 

  24. Wang, S., Su, W., Zhu, X., Zhang, H.: A hadoop-based approach for efficient web service management. J. Web Grid Serv. 9(1), 18–34 (2013)

    Article  Google Scholar 

  25. Wang, X., Yang, L.T., Feng, J., Chen, X., Deen, M.J.: A tensor-based big service framework for enhanced living environments. IEEE Cloud Comput. 3(6), 36–43 (2016)

    Article  Google Scholar 

  26. Xu, X., Sheng, Q.Z., Zhang, L.J., Fan, Y., Dustdar, S.: From big data to big service. Computer 48(7), 80–83 (2015)

    Article  Google Scholar 

  27. Yang, L.T., et al.: A multi-order distributed hosvd with its incremental computing for big services in cyber-physical-social systems. IEEE Trans. Big Data 6, 666–678 (2018)

    Google Scholar 

  28. Yang, Y., Xu, J., Xu, Z., Zhou, P., Qiu, T.: Quantile context-aware social iot service big data recommendation with d2d communication. IEEE Internet Things 7, 5533–5548 (2020)

    Article  Google Scholar 

  29. Zhou, J., et al.: Graph neural networks: a review of methods and applications (2018). arXiv preprint arXiv:1812.08434

Download references

Author information

Authors and Affiliations

  1. LIMTIC, Faculty of Sciences, Tunis Elmanar University, Tunis, Tunisia

    Fedia Ghedass & Faouzi Ben Charrada

Authors
  1. Fedia Ghedass
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Faouzi Ben Charrada
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia

    Wenjie Zhang

  2. Peking University, Beijing, China

    Lei Zou

  3. Zayed University, Dubai, United Arab Emirates

    Zakaria Maamar

  4. Swinburne University of Technology, Melbourne, VIC, Australia

    Lu Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ghedass, F., Ben Charrada, F. (2021). A Multi-view Learning Approach for the Autonomic Management of Big Services. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds) Web Information Systems Engineering – WISE 2021. WISE 2021. Lecture Notes in Computer Science(), vol 13081. Springer, Cham. https://doi.org/10.1007/978-3-030-91560-5_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-91560-5_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91559-9

  • Online ISBN: 978-3-030-91560-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics