A Multidimensional Control Architecture for Combined Fog-to-Cloud Systems

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1129)


The fog/edge computing concept has set the foundations for the deployment of new services leveraging resources deployed at the edge paving the way for an innovative collaborative model, where end-users may collaborate with service providers by sharing idle resources at the edge of the network. Combined Fog-to-Cloud (F2C) systems have been recently proposed as a control strategy for managing fog and cloud resources in a coordinated way, aimed at optimally allocating resources within the fog-to-cloud resources stack for an optimal service execution. In this work, we discuss the unfeasibility of the deployment of a single control topology able to optimally manage a plethora of edge devices in future networks, respecting established SLAs according to distinct service requirements and end-user profiles. Instead, a multidimensional architecture, where distinct control plane instances coexist, is then introduced. By means of distinct scenarios, we describe the benefits of the proposed architecture including how users may collaborate with the deployment of novel services by selectively sharing resources according to their profile, as well as how distinct service providers may benefit from shared resources reducing deployment costs. The novel architecture proposed in this paper opens several opportunities for research, which are presented and discussed at the final section.


Fog computing Combined F2C systems Virtual control architecture 



This work was supported by the H2020 EU mF2C project, ref. 730929 and for UPC authors, also by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under contract RTI2018-094532-B-I00.


  1. 1.
    Ahmed, E., Yaqoob, I., Gani, A., Imran, M., Guizani, M.: Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges. IEEE Wirel. Commun. 23(5), 10–16 (2016). Scholar
  2. 2.
    Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29, 1645–1660 (2013). Scholar
  3. 3.
    Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: MCC 2012 Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16. ACM (2012).
  4. 4.
    Hu, P., Dhelim, S., Ning, H., Qiu, T.: Survey on fog computing: architecture, key technologies, applications and open issues. J. Netw. Comput. Appl. 98, 27–42 (2017)CrossRefGoogle Scholar
  5. 5.
    OpenFog Consortium: OpenFog Reference Architecture for Fog Computing, February 2017.
  6. 6.
    Masip-Bruin, X., Marín-Tordera, E., Tashakor, G., Jukan, A., Ren, G.J.: Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud computing systems. IEEE Wirel. Commun. 23(5), 120–128 (2016). Scholar
  7. 7.
    Masip-Bruin, X., Marín-Tordera, E., Jukan, A., Ren, G.J.: Managing resources continuity from the edge to the cloud: architecture and performance. Future Gener. Comput. Syst. 79, part 3, 777–785 (2018)CrossRefGoogle Scholar
  8. 8.
    Souza, V.B., Gómez, A., Masip-Bruin, X., Marin-Tordera, E., Garcia, J.: Towards a fog-to-cloud control topology for QoS-aware end-to-end communication. In: 2017 IEEE 25th International Symposium of Quality of Service (IWQoS), Vilanova i la Geltrú, Barcelona (2017)Google Scholar
  9. 9.
    Ramirez, W., Masip-Bruin, X., Marín-Tordera, E., Souza, V.B.C., Jukan, A., Ren, G.J., González de Dios, O.: Evaluating the benefits of combined and continuous fog-to-cloud architectures. Comput. Commun. 113, 43–52 (2017)CrossRefGoogle Scholar
  10. 10.
    Sood, K., Yu, S., Xiang, Y.: Software-defined wireless networking opportunities and challenges for Internet-of-Things: a review. IEEE Internet Things J. 3(4), 453–463 (2016). Scholar
  11. 11.
    Ku, I., Lu, Y., Gerla, M., Gomes, R.L., Ongaro, F., Cerqueira, E.: Towards software-defined VANET: architecture and services. In: 2014 13th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET), Piran, pp. 103–110 (2014).
  12. 12.
    Tomovic, S., Yoshigoe, K., Maljevic, I., Radusinovic, I.: Software-defined fog network architecture for IoT. Wireless Pers. Commun. 92(1), 181–196 (2017). Scholar
  13. 13.
    Xu, J., Palanisamy, B., Ludwig, B.H., Wang, Q.: Zenith: utility-aware resource allocation for edge computing. In: IEEE Edge 2017, Honolulu, Hawaii, 25–30 June 2017 (2017)Google Scholar
  14. 14.
    Yeganeh, S.H., Tootoonchian, A., Ganjali, Y.: On scalability of software-defined networking. IEEE Commun. Mag. 51(2), 136–141 (2013). Scholar
  15. 15.
    Hu, J., Lin, C., Li, X., Huang, J.: Scalability of control planes for Software defined networks: modeling and evaluation. In: 2014 IEEE 22nd International Symposium of Quality of Service (IWQoS), Hong Kong, pp. 147–152 (2014).
  16. 16.
    mF2C project. Accessed May 2019
  17. 17.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.CRAAX LabUniversitat Politecnica de CatalunyaVilanova i la GeltrúSpain
  2. 2.Universidade Federal de ViçosaViçosaBrazil
  3. 3.IBM Almaden Research CenterSan JoseUSA
  4. 4.Technischen Universität BraunschweigBrunswickGermany

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