skip to main content
research-article

Cloud, Fog, or Mist in IoT? That Is the Question

Published:28 March 2019Publication History
Skip Abstract Section

Abstract

Internet of Things (IoT) has been commercially explored as Platforms as a Services (PaaS). The standard solution for this kind of service is to combine the Cloud computing infrastructure with IoT software, services, and protocols also known as CoT (Cloud of Things). However, the use of CoT in latency-sensitive applications has been shown to be unfeasible due to the inherent latency of cloud computing services. One proposal to solve this problem is the use of the computational resources available at the edge of the network, which is called Fog computing. Fog computing solves the problem of latency but adds complexity to the use of these resources due to the dynamism and heterogeneity of the IoT. An even more accentuated form of fog computing is Mist computing, where the use of the computational resources is limited to the close neighborhood of the client device. The decision of what computing infrastructure (Fog, Mist, or Cloud computing) is the best to provide computational resources is not always simple, especially in cases where latency requirements should be met by CoT. This work proposes an algorithm for selecting the best physical infrastructure to use the computational resource (Fog, Mist, or Cloud computing) based on cost, bandwidth, and latency criteria defined by the client device, resource availability, and topology of the network. The article also introduces the concept of feasible Fog that limits the growth of device search time in the neighborhood of the client device. Simulation results suggest the algorithm’s choice adequately attends the client’s device requirements and that the proposed method can be used in IoT environment located on the edge of the network.

References

  1. R. Khan, S. U. Khan, R. Zaheer, and S. Khan. 2012. Future internet: The internet of things architecture, possible applications and key challenges. In Proceedings of the 2012 10th International Conference FIT. IEEE, 257--260. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. Aazam and E. N. Huh. 2016. Fog computing: The cloud-IoT/IoE middleware paradigm. IEEE Potent. 35, 3 (May 2016), 40--44.Google ScholarGoogle ScholarCross RefCross Ref
  3. Shanhe Yi, Zijiang Hao, Zhengrui Qin, and Qun Li. 2015. Fog computing: Platform and applications. In Proceedings of the 3rd IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb’15). 73--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. D. R. Vasconcelos, F. L. R. Pimentel, R. M. C. Andrade, and J. N. Souza. 2017. Mathematical model for a collaborative indoor position system (IPS) and movement detection of devices within IoT environment. In Proceedings of the Symposium on Applied Computing (SAC’17). ACM, New York, NY, 602--608. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. S. Yi, Z. Hao, Z. Qin, and Q. Li. 2015. Fog computing: Platform and applications. In Proceedings of the 3rd IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb’15). IEEE, 73--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. T. Pflanzner and A. Kertesz. 2016. A survey of IoT cloud providers. In Proceedings of the 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO’16). IEEE, 730--735.Google ScholarGoogle Scholar
  7. Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. 2012. Fog computing and its role in the internet of things. In Proceedings of the 1st Edition of the MCC Workshop on Mobile Cloud Computing (MCC’12). ACM, New York, NY, 13--16. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. D. R. d. Vasconcelos, R. M. d. C. Andrade, and J. N. d. Souza. 2015. Smart shadow—An autonomous availability computation resource allocation platform for internet of things in the fog computing environment. In Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS’15). IEEE, 216--217. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. D. Bruneo, S. Distefano, F. Longo, G. Merlino, A. Puliafito, V. D’Amico, M. Sapienza, and G. Torrisi. 2016. Stack4Things as a fog computing platform for smart city applications. In Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM’16). IEEE, 848--853.Google ScholarGoogle Scholar
  10. Filipe Saraiva, Aldir Sousa, and Eduardo Asada. 2011. Implementação Distribuída do Algoritmo de Dijkstra Através de Sistemas Multiagentes. In Proceedings of the Anais do 10th Simpósio Brasileiro de Automação Inteligente. 51--56. Retrieved from http://www.sbai2011.ufsj.edu.br.Google ScholarGoogle Scholar
  11. Sudip Misra Subhadeep Sarkar. 2016. Theoretical modelling of fog computing: A green computing paradigm to support IoT applications. IET Netw. 5 (Mar. 2016), 23--29(6).Google ScholarGoogle Scholar
  12. L. M. Vaquero and L. Rodero-Merino. 2014. Finding your way in the fog: Towards a comprehensive definition of fog computing. SIGCOMM Comput. Commun. Rev. 44, 5 (Oct. 2014), 27--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. J. S. Preden, K. Tammemae, A. Jantsch, M. Leier, A. Riid, and E. Calis. 2015. The benefits of self-awareness and attention in fog and mist computing. Computer 48, 7 (July 2015), 37--45.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. H. Shi, N. Chen, and R. Deters. 2015. Combining mobile and fog computing: Using CoAP to link mobile device clouds with fog computing. In Proceedings of the IEEE International Conference on Data Science and Systems (DSS’15). IEEE, 564--571. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. X. Masip-Bruin, E. Marín-Tordera, G. Tashakor, A. Jukan, and G. J. Ren. 2016. Foggy clouds and cloudy fogs: A real need for coordinated management of fog-to-cloud computing systems. IEEE Wireless Commun. 23, 5 (Oct. 2016), 120--128. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Carlos Pereira, António Pinto, Duarte Ferreira, and Ana Aguiar. 2017. Experimental characterization of mobile iot application latency. IEEE IoT J. 4, 4 (2017), 1082--1094.Google ScholarGoogle Scholar
  17. K. Dolui and S. K. Datta. 2017. Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing. In Proceedings of the Global IoT Summit (GIoTS’17). IEEE, 1--6.Google ScholarGoogle Scholar
  18. Mohammad Aazam, Marc St-Hilaire, Chung-Horng Lung, and Ioannis Lambadaris. 2016. Pre-fog: IoT trace-based probabilistic resource estimation at fog. In Proceedings of the 13th IEEE Annual Consumer Communications 8 Networking Conference (CCNC’16). IEEE, 12--17.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. R. Deng, R. Lu, C. Lai, T. H. Luan, and H. Liang. 2016. Optimal workload allocation in fog-cloud computing towards balanced delay and power consumption. IEEE IoT J. 3, 6 (2016), 1171--1181.Google ScholarGoogle Scholar
  20. V. B. C. Souza, W. Ramírez, X. Masip-Bruin, E. Marín-Tordera, G. Ren, and G. Tashakor. 2016. Handling service allocation in combined Fog-cloud scenarios. In Proceedings of the IEEE International Conference on Communications (ICC’16). IEEE, 1--5.Google ScholarGoogle Scholar
  21. Xuan-Qui Pham and Eui-Nam Huh. 2016. Towards task scheduling in a cloud-fog computing system. In Proceeedings of the 18th Asia-Pacific Network Operations and Management Symposium (APNOMS’16). IEEE, 1--4.Google ScholarGoogle Scholar
  22. Adrian Klein, Fuyuki Ishikawa, and Shinichi Honiden. 2012. Towards network-aware service composition in the cloud. In Proceedings of the 21st International Conference on the World Wide Web (WWW’12). ACM, IEEE, 959--968. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Shangguang Wang, Ao Zhou, Fangchun Yang, and Rong N. Chang. 2016. Towards network-aware service composition in the cloud. IEEE Trans. Cloud Comput. (2016).Google ScholarGoogle Scholar
  24. N. J. Yadwadkar, B. Hariharan, J. E Gonzalez, B. Smith, and R. H. Katz. 2017. Selecting the best vm across multiple public clouds: A data-driven performance modeling approach. In Proceedings of the Symposium on Cloud Computing (SOCC’17). ACM, ACM, 452--465. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Shangguang Wang, Yali Zhao, Lin Huang, Jinliang Xu, and Ching-Hsien Hsu. 2017. QoS prediction for service recommendations in mobile edge computing. J. Parallel Distrib. Comput. (2017).Google ScholarGoogle Scholar
  26. Tao Lei, Shangguang Wang, Jinglin Li, and Fangchun Yang. 2016. AOM: Adaptive mobile data traffic offloading for M2M networks. Person. Ubiq. Comput. 20, 6 (2016), 863--873. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Mohammad Aazam and Eui-Nam Huh. 2015. Fog computing microdatacenter-based dynamic resource estimation and pricing model for IoT. In Proceedings of the IEEE 29th International Conference on Advanced Information Networking and Applications (AINA’15). IEEE, IEEE, 687--694.Google ScholarGoogle Scholar
  28. B. Awerbuch. 1985. A new distributed depth-first-search algorithm. Info. Process. Lett. 20, 3 (1985), 147--150.Google ScholarGoogle ScholarCross RefCross Ref
  29. Adam Dunkels, Bjorn Gronvall, and Thiemo Voigt. 2004. Contiki—A lightweight and flexible operating system for tiny networked sensors. In Proceedings of the 29th Annual IEEE Local Computer Networks International Conference (LCN’04). IEEE Computer Society, Washington, DC, 455--462. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Cloud, Fog, or Mist in IoT? That Is the Question

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Internet Technology
      ACM Transactions on Internet Technology  Volume 19, Issue 2
      Special Issue on Fog, Edge, and Cloud Integration
      May 2019
      288 pages
      ISSN:1533-5399
      EISSN:1557-6051
      DOI:10.1145/3322882
      • Editor:
      • Ling Liu
      Issue’s Table of Contents

      Copyright © 2019 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 28 March 2019
      • Revised: 1 January 2019
      • Accepted: 1 January 2019
      • Received: 1 November 2017
      Published in toit Volume 19, Issue 2

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format