An Architecture for Resource Management in a Fog-to-Cloud Framework

  • Souvik SenguptaEmail author
  • Jordi GarciaEmail author
  • Xavi Masip-BruinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11339)


Fog-to-cloud (F2C) platforms provide an excellent framework for the efficient resource management in the context of smart cities. In such a scenario, a vast number of heterogeneous resources, including computing devices and IoT sensors, are considered in coordination to provide the best facilities. One of the most critical and challenging tasks in this framework is appropriately managing the set of resources available in the smart city. Many devices with different features should be efficiently classified, organized, and selected, to fulfill the requirements during services execution. In this paper, we present the design of an architecture for resource management as part of a core module in an F2C system. In this architecture, we classify both, the system resources and services and, based on the users’ preferences and sharing policies; we discuss the process of resource selection according to a predefined cost model. The cost model could consider any cost dimension, such as performance, energy consumption, or any eventual business model associated with the F2C system.


Fog-to-Cloud (F2C) Internet of Things (IoT) Resource management 



This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund, under contract TEC2015-66220-R(MINECO/FEDER) and by the H2020 EU mF2C project reference 730929.


  1. 1.
    Aazam, M., Huh, E.N.: Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In: IEEE 29th International Conference on Advanced Information Networking and Applications. IEEE (2015)Google Scholar
  2. 2.
    Agarwal, S., Yadav, S., Yadav, A.K.: An efficient architecture and algorithm for resource provisioning in fog computing. Int. J. Inf. Eng. Electron. Bus. 8(1), 48–61 (2016)Google Scholar
  3. 3.
    Barker, J., Marxer, R., Vincent, E., Watanabe, S.: The third ‘chime’speech separation and recognition challenge: dataset, task and baselines. In: IEEE Workshop on Automatic Speech Recognition and Understanding. IEEE (2015)Google Scholar
  4. 4.
    Bessai, K., Youcef, S., Oulamara, A., Godart, C., Nurcan, S.: Bi-criteria workflow tasks allocation and scheduling in cloud computing environments. In: IEEE 5th International Conference on Cloud Computing. IEEE (2012)Google Scholar
  5. 5.
    Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog computing: A platform for internet of things and analytics. In: Bessis, N., Dobre, C. (eds.) Big Data and Internet of Things: A Roadmap for Smart Environments. SCI, vol. 546, pp. 169–186. Springer, Cham (2014). Scholar
  6. 6.
    Ding, L., Chang, L., Wang, L.: Online auction-based resource scheduling in grid computing networks. Int. J. Distrib. Sens. Netw. 12(10), 1550147716673930 (2016)CrossRefGoogle Scholar
  7. 7.
    Graves, T.: The Service-oriented Enterprise. Tetradian Books, Colchester (2009)Google Scholar
  8. 8.
    Gu, L., Zeng, D., Guo, S., Barnawi, A., Xiang, Y.: Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Trans. Emerg. Top. Comput. 5(1), 108–119 (2017)CrossRefGoogle Scholar
  9. 9.
    Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 29(7), 1645–1660 (2013)CrossRefGoogle Scholar
  10. 10.
    Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: iFogSim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Softw. Pract. Exp. 47(9), 1275–1296 (2017)CrossRefGoogle Scholar
  11. 11.
    Keller, M., Karl, H.: Response time-optimized distributed cloud resource allocation. In: Proceedings of the 2014 ACM SIGCOMM Workshop on Distributed Cloud Computing. ACM (2014)Google Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Mazrekaj, A., Shabani, I., Sejdiu, B.: Pricing schemes in cloud computing: an overview. Int. J. Adv. Comput. Sci. Appl. 7(2), 80–86 (2016)Google Scholar
  14. 14.
    Ni, L., Zhang, J., Jiang, C., Yan, C., Yu, K.: Resource allocation strategy in fog computing based on priced timed petri nets. IEEE Internet Things J. 4(5), 1216–1228 (2017)CrossRefGoogle Scholar
  15. 15.
    Riggins, F.J., Wamba, S.F.: Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics. In: 48th Hawaii International Conference on System Sciences. IEEE (2015)Google Scholar
  16. 16.
    Samimi, P., Teimouri, Y., Mukhtar, M.: A combinatorial double auction resource allocation model in cloud computing. Inf. Sci. 357, 201–216 (2016)CrossRefGoogle Scholar
  17. 17.
    Shekhar, S., Gokhale, A.: Dynamic resource management across cloud-edge resources for performance-sensitive applications. In: Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. IEEE Press (2017)Google Scholar
  18. 18.
    Shin, S., Kim, Y., Lee, S.: Deadline-guaranteed scheduling algorithm with improved resource utilization for cloud computing. In: 12th Annual IEEE Consumer Communications and Networking Conference. IEEE (2015)Google Scholar
  19. 19.
    de Souza, V.B.C., Ramírez, W., Masip-Bruin, X., Marín-Tordera, E., Ren, G., Tashakor, G.: Handling service allocation in combined fog-cloud scenarios. In: IEEE International Conference on Communications. IEEE (2016)Google Scholar
  20. 20.
    Wu, Z., Liu, X., Ni, Z., Yuan, D., Yang, Y.: A market-oriented hierarchical scheduling strategy in cloud workflow systems. J. Supercomput. 63(1), 256–293 (2013)CrossRefGoogle Scholar
  21. 21.
    Yi, S., Kondo, D., Andrzejak, A.: Reducing costs of spot instances via checkpointing in the amazon elastic compute cloud. In: IEEE 3rd International Conference on Cloud Computing. IEEE (2010)Google Scholar
  22. 22.
    Yu, L., Cai, Z.: Dynamic scaling of virtual clusters with bandwidth guarantee in cloud datacenters. In: The 35th Annual IEEE International Conference on Computer Communications. IEEE (2016)Google Scholar
  23. 23.
    Yu, L., Shen, H., Sapra, K., Ye, L., Cai, Z.: CoRE: cooperative end-to-end traffic redundancy elimination for reducing cloud bandwidth cost. IEEE Trans. Parallel Distrib. Syst. 28(2), 446–461 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Advanced Network Architectures Lab (CRAAX)UPC BarcelonaTechVilanova i la GeltrúSpain

Personalised recommendations