Reconsidering the Relationship Between Cloud Computing and Cloud Manufacturing

  • Hélène Coullon
  • Jacques Noyé
Part of the Studies in Computational Intelligence book series (SCI, volume 762)


History shows many relations between computer science and manufacturing control, starting with the initial idea of “digital manufacturing” in the ’70s. Since then, advances in computer science have given birth to the Cloud Computing (CC) paradigm, where computing resources are seen as a service offered to various end-users. Of course, CC has been used as such to improve the IT infrastructure associated to a manufacturing control infrastructure, but its principles have also inspired a new manufacturing paradigm Cloud Manufacturing (CMfg) with the perspective of many benefits for both the manufacturers and their customers. However, despite the usefulness of CC for CMfg, we advocate that considering CC as a core enabling technology for CMfg, as is often put forth in the literature, is limited and should be reconsidered. This paper presents a new core-enabling vision toward CMfg, called Cloud Anything (CA). CA is based on the idea of abstracting low-level resources, beyond computing resources, into a set of core control building blocks providing the grounds on top of which any domain could be “cloudified”.


Cloud computing Cloud manufacturing Resource management IaaS MES 


  1. 1.
    Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, MCC ’12, pp. 13–16. ACM Press (2012)Google Scholar
  2. 2.
    Coullon, H., Pérez, C., Pertin, D.: Production deployment tools for IaaSes: an overall model and survey. In: 2017 IEEE International Conference on Future Internet of Things and Cloud (FiCloud), Prague, Czech Republic (2017)Google Scholar
  3. 3.
    Huebscher, M.C., McCann, J.A.: A survey of autonomic computing—degrees, models, and applications. ACM Comput. Surv. 40(3), 7:1–7:28 (2008)Google Scholar
  4. 4.
    Joy, A.M.: Performance comparison between linux containers and virtual machines. In: 2015 International Conference on Advances in Computer Engineering and Applications, pp. 342–346 (2015)Google Scholar
  5. 5.
    Kletti, J.: Manufacturing Execution System—MES, 1st edn. Springer (2010)Google Scholar
  6. 6.
    Kubler, S., Holmström, J., Främling, K., Turkama, P.: Technological theory of cloud manufacturing. In: Borangiu, T., Trentesaux, D., Thomas, A., McFarlane, D. (eds.) Service Orientation in Holonic and Multi-Agent Manufacturing, Studies in Computational Intelligence, vol. 640, pp. 267–276. Springer (2016)Google Scholar
  7. 7.
    Lebre, A., Pastor, J., Simonet, A., Desprez, F.: Revising OpenStack to operate fog/edge computing infrastructures. In: IEEE International Conference on Cloud Engineering, Vancouver, Canada (2017)Google Scholar
  8. 8.
    Lu, Y., Xu, X., Xu, J.: Development of a hybrid manufacturing cloud. J. Manuf. Syst. 33(4), 551–566 (2014)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Mahmud, R., Buyya, R.: Fog computing: A taxonomy, survey and future directions. CoRR (2016). arXiv:abs/1611.05539
  10. 10.
    Maurer, M., Breskovic, I., Emeakaroha, V.C., Brandic, I.: Revealing the MAPE loop for the autonomic management of cloud infrastructures. In: 2011 IEEE Symposium on Computers and Communications (ISCC), pp. 147–152 (2011)Google Scholar
  11. 11.
    Mell, P.M., Grance, T.: The NIST definition of cloud computing. Special Publication 800-145. National Institute of Standards & Technology, Gaithersburg, MD, United States (2011)Google Scholar
  12. 12.
    Morariu, O., Borangiu, T., Morariu, C.: From service oriented to cloud powered manufacturing systems. In: Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, UCC ’13, pp. 83–90 (2013)Google Scholar
  13. 13.
    Orgerie, A.C., de Assunção, M.D., Lefèvre, L.: A survey on techniques for improving the energy efficiency of large-scale distributed systems. ACM Comput. Surv. 46(4), 47:1–47:31 (2014)Google Scholar
  14. 14.
    Perera, C., Qin, Y., Estrella, J.C., Reiff-Marganiec, S., Vasilakos, A.V.: Fog computing for sustainable smart cities: a survey. ACM Comput. Surv. 50(3), 32:1–32:43 (2017)Google Scholar
  15. 15.
    Queiroz, J., Leitão, P., Oliveira, E.: Industrial Cyber Physical Systems Supported by Distributed Advanced Data Analytics, pp. 47–59, vol. 694 of Borangiu et al. [2] (2017)Google Scholar
  16. 16.
    Răileanu, S., Anton, F., Borangiu, T.: High Availability Cloud Manufacturing System Integrating Distributed MES Agents, pp. 11–23, vol. 694 of Borangiu et al. [2] (2017)Google Scholar
  17. 17.
    Rensin, D.K.: Kubernetes-Scheduling the Future at Cloud Scale. O’Reilly (2015)Google Scholar
  18. 18.
    Tao, F., Cheng, Y., Xu, L.D., Zhang, L., Li, B.H.: CCIoT-CMfg: Cloud computing and internet of things-based cloud manufacturing service system. IEEE Trans. Ind. Inf. 10(2), 1435–1442 (2014)CrossRefGoogle Scholar
  19. 19.
    Tao, F., Zhang, L., Venkatesh, V.C., Luo, Y., Cheng, Y.: Cloud manufacturing: a computing and service-oriented manufacturing model. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 225(10), 1969–1976 (2011)CrossRefGoogle Scholar
  20. 20.
    Wu, D., Greer, M.J., Rosen, D.W., Schaefer, D.: Cloud manufacturing: strategic vision and state-of-the-art. J. Manuf. Syst. 32(4), 564–579 (2013)CrossRefGoogle Scholar
  21. 21.
    Xavier, B., Ferreto, T., Jersak, L.: Time provisioning evaluation of KVM, Docker and unikernels in a cloud platform. In: 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 277–280 (2016)Google Scholar
  22. 22.
    Xu, X.: From cloud computing to cloud manufacturing. Robot. Comput.-Integr. Manuf. 28(1), 75–86 (2012)CrossRefGoogle Scholar
  23. 23.
    Zhang, L., Luo, Y., Tao, F., Li, B.H., Ren, L., Zhang, X., Guo, H., Cheng, Y., Hu, A., Liu, Y.: Cloud manufacturing: a new manufacturing paradigm. Enterp. Inf. Syst. 8(2), 167–187 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.IMT Atlantique, InriaLS2N, UBLNantesFrance

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