Skip to main content

Distributed Task Placement in the Fog: A Positioning Paper

  • Conference paper
  • First Online:
Advances on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 96))

Abstract

As the Internet of Things (IoT) paradigm becomes omnipresent, so does fog computing, a paradigm aimed at bringing applications closer to the end devices, aiding in lowering stress over the network and improving latency. However, to efficiently place application tasks in the fog, task placement coordination is needed. In this paper, task placement in the fog and corresponding problems are addressed. We look at the fundamental issue of solving Multi-Objective Optimization problems and treat different techniques for distributed coordination. We review how this research can be used in a smart vehicle environment, and finish with some preliminary tests results.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Vinitsky, E., Parvate, K., Kreidieh, A., Wu, C., Bayen, A.: Lagrangian Control through Deep-RL: applications to bottleneck decongestion. In: IEEE Intelligent Transportation Systems Conference, pp. 759–765 (2018)

    Google Scholar 

  2. Cisco: Cisco Global Cloud Index: Forecast and Methodology 2014-2019 (white paper). Cisco, pp. 2016–2021 (2016)

    Google Scholar 

  3. Iorga, M., et al.: Fog computing conceptual model. NIST, Gaithersburg, MD, Technical report, March 2018

    Google Scholar 

  4. Xia, Y., et al.: Combining heuristics to optimize and scale the placement of IoT applications in the fog. In: 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC), pp. 153–163 (2018)

    Google Scholar 

  5. Huybrechts, T., Mercelis, S., Hellinckx, P.: A new hybrid approach on WCET analysis for real-time systems using machine learning, no. 5, pp. 1–5 (2018)

    Google Scholar 

  6. IDLab, Imec, and University of Antwerp: COBRA Framework. http://cobra.idlab.uantwerpen.be/

  7. Sharma, B., Chudnovsky, V., Hellerstein, J.L., Rifaat, R., Das, C.R.: Modeling and synthesizing task placement constraints in Google compute clusters. In: Proceedings of the 2nd ACM Symposium on Cloud Computing - SOCC 2011, pp. 1–14. ACM Press (2011)

    Google Scholar 

  8. Vanneste, S., et al.: Distributed uniform streaming framework: towards an elastic fog computing platform for event stream processing. In: Proceedings of the 13th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 426–436 (2019)

    Google Scholar 

  9. Abowd, G.D., et al.: Towards a better understanding of context and context-awareness. In: Lecture Notes in Computer Science, vol. 1707, pp. 304–307 (1999)

    Chapter  Google Scholar 

  10. Wen, Z., et al.: Fog orchestration for internet of things services. IEEE Internet Comput. 21(2), 16–24 (2017)

    Article  Google Scholar 

  11. Brogi, A., Forti, S.: QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J. 4(5), 1–8 (2017)

    Article  Google Scholar 

  12. Wang, S., Zafer, M., Leung, K.K.: Online placement of multi-component applications in edge computing environments. IEEE Access 5, 2514–2533 (2017)

    Article  Google Scholar 

  13. Eyckerman, R., Sharif, M., Mercelis, S., Hellinckx, P.: Context-aware distribution in constrained IoT environments, pp. 437–446 (2019)

    Google Scholar 

  14. Smith, R.G.: Communication and control in a distributed problem solver. IEEE Trans. Comput. C(12), 1104–1113 (1980)

    Article  Google Scholar 

  15. Talukdar, S., Baerentzen, L., Gove, A., De Souza, P.: Asynchronous teams: cooperation schemes for autonomous agents. J. Heurist. 4(4), 295–321 (1998)

    Article  Google Scholar 

  16. Barbucha, D., Je, P.: An agent-based approach to vehicle routing problem. Int. J. Appl. Math. Comput. Sci. 4(1), 36–41 (2007)

    Google Scholar 

  17. Barbucha, D.: Agent-based optimization. In: Agent-Based Optimization, vol. 456, pp. 55–75 (2013)

    Google Scholar 

  18. Casanova, H., Giersch, A., Legrand, A., Quinson, M., Suter, F.: Versatile, scalable, and accurate simulation of distributed applications and platforms. J. Parallel Distrib. Comput. 74(10), 2899–2917 (2014)

    Article  Google Scholar 

  19. Zeng, X., et al.: IOTSim: a simulator for analysing IoT applications. J. Syst. Architect. 72, 93–107 (2017)

    Article  Google Scholar 

  20. Marler, R., Arora, J.: Survey of multi-objective optimization methods for engineering. Struct. Multidisc. Optim. 26(6), 369–395 (2004)

    Article  MathSciNet  Google Scholar 

  21. Marler, R.T., Arora, J.S.: The weighted sum method for multi-objective optimization: new insights. Struct. Multidisc. Optim. 41, 853–862 (2010)

    Article  MathSciNet  Google Scholar 

  22. Kaufman, K.A., Michalski, R.S.: Learning from inconsistent and noisy data: the AQ18 approach. In: Symposium A Quarterly Journal In Modern Foreign Literatures, pp. 411–419 (1999)

    Google Scholar 

  23. Gavras, A., Karila, A., Fdida, S., May, M., Potts, M.: Future internet research and experimentation: the FIRE initiative. Sigcomm 37(3), 89–92 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Reinout Eyckerman .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Eyckerman, R., Mercelis, S., Marquez-Barja, J., Hellinckx, P. (2020). Distributed Task Placement in the Fog: A Positioning Paper. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2019. Lecture Notes in Networks and Systems, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-33509-0_63

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33509-0_63

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33508-3

  • Online ISBN: 978-3-030-33509-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics