Skip to main content
SpringerLink
Log in

Analyzing the Behavior of Real-Time Tasks in Fog-Cloud Architecture

  • Conference paper
  • First Online:
Advanced Network Technologies and Intelligent Computing (ANTIC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1534))

Abstract

The growing number of IoT devices generates a huge amount of data that are generally processed by the Cloud datacenter. However, it results in inordinate delay for time-critical applications due to network intricacies. Fog computing has evolved in recent which provides similar facilities as of Cloud though in a reduced manner. In order to provide the desired quality of service to the IoT users, it is essential to classify and allocate Fog-Cloud resources optimally to the time-critical requests. In this work, we have developed an analytical model focusing on the design mechanism approach and optimal policies for the allocation and offloading of real-time tasks that results in overall time minimization.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
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. He, J., Wei, J., Chen, K., Tang, Z., Zhou, Y., Zhang, Y.: Multitier fog computing with large-scale IoT data analytics for smart cities. IEEE Internet Things J. 5(2), 677–686 (2018)

    Article  Google Scholar 

  2. Nan, Y., et al.: Adaptive energy-aware computation offloading for cloud of things systems. IEEE Access 5, 23947–23957 (2017)

    Article  Google Scholar 

  3. Sarkar, S., Chatterjee, S., Misra, S.: Assessment of the suitability of fog computing in the context of internet of things. IEEE Trans. Cloud Comput. 6(1), 46–59 (2018)

    Article  Google Scholar 

  4. Travelling team: en-USThe Changing Face of World Missions - The Strategic Context. http://www.thetravelingteam.org/articles/the-changing-face-of-world-missions-the-strategic-context

  5. Yadav, P., Kar, S.: Evaluating the impact of region based content popularity of videos on the cost of CDN deployment. In: National Conference on Communications (NCC) 2020, pp. 1–6 (2020)

    Google Scholar 

  6. Fan, Q., Ansari, N.: Workload allocation in hierarchical cloudlet networks. IEEE Commun. Lett. 22(4), 820–823 (2018)

    Article  Google Scholar 

  7. Guo, S., Liu, J., Yang, Y., Xiao, B., Li, Z.: Energy-efficient dynamic computation offloading and cooperative task scheduling in mobile cloud computing. IEEE Trans. Mob. Comput. 18(2), 319–333 (2019)

    Article  Google Scholar 

  8. Deng, R., Lu, R., Lai, C., Luan, T.H., Liang, H.: Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J. 3(6), 1171–1181 (2016)

    Google Scholar 

  9. Yousefpour, A., Ishigaki, G., Gour, R., Jue, J.P.: On reducing IoT service delay via fog offloading. IEEE Internet Things J. 5(2), 998–1010 (2018). arXiv: 1804.07376

  10. Rahbari, D., Nickray, M.: enTask offloading in mobile fog computing by classification and regression tree. enPeer-to-Peer Netw. Appl. 13(1), 104–122 (2020). https://doi.org/10.1007/s12083-019-00721-7

  11. Liu, Y., Yu, F.R., Li, X., Ji, H., Leung, V.C.: Hybrid computation offloading in fog and cloud networks with non-orthogonal multiple access. In: IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 154–159, April 2018

    Google Scholar 

  12. Majeed, A.A., Kilpatrick, P., Spence, I., Varghese, B.: Modelling Fog Offloading Performance, arXiv:2002.05531 [cs], February 2020

  13. Zhang, G., Shen, F., Liu, Z., Yang, Y., Wang, K., Zhou, M.-T.: FEMTO: fair and energy-minimized task offloading for fog-enabled IoT networks. IEEE Internet Things J. 6(3), 4388–4400 (2019)

    Article  Google Scholar 

  14. Gu, L., Zeng, D., Guo, S., Barnawi, A., Xiang, Y.: enCost efficient resource management in fog computing supported medical cyber-physical system. enIEEE Trans. Emerg. Topics Comput. 5(1), 108–119 (2017). http://ieeexplore.ieee.org/document/7359164/

  15. Akbar, A., Ibrar, M., Jan, M.A., Bashir, A.K., Wang, L.: SDN-enabled adaptive and reliable communication in IoT-fog environment using machine learning and multiobjective optimization. IEEE Internet Things J. 8(5), 3057–3065 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

    Pratibha Yadav & Deo Prakash Vidyarthi

Authors
  1. Pratibha Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deo Prakash Vidyarthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Ryerson University, Toronto, ON, Canada

    Isaac Woungang

  2. Netaji Subhas University of Technology, New Delhi, India

    Sanjay Kumar Dhurandher

  3. ABV-Indian Institute of Information Technology and Management, Gwalior, India

    Kiran Kumar Pattanaik

  4. Banaras Hindu University, Varanasi, India

    Anshul Verma

  5. Indian Institute of Technology BHU, Varanasi, India

    Pradeepika Verma

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yadav, P., Vidyarthi, D.P. (2022). Analyzing the Behavior of Real-Time Tasks in Fog-Cloud Architecture. In: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (eds) Advanced Network Technologies and Intelligent Computing. ANTIC 2021. Communications in Computer and Information Science, vol 1534. Springer, Cham. https://doi.org/10.1007/978-3-030-96040-7_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-96040-7_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96039-1

  • Online ISBN: 978-3-030-96040-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation