Skip to main content

Cloud-Fog Based Smart Grid Paradigm for Effective Resource Distribution

  • Conference paper
  • First Online:
Advances in Network-Based Information Systems (NBiS 2018)

Abstract

Smart grid (SG) provides observable energy distribution where utility and consumers are enabled to control and monitor their production, consumption, and pricing in almost, real time. Due to increase in the number of smart devices complexity of SG increases. To overcome these problems, this paper proposes cloud-fog based SG paradigm. The proposed model comprises three layers: cloud layer, fog layer, and end user layer. The 1st layer consists of the cluster of buildings. The renewable energy source is installed in each building so that buildings become self-sustainable with respect to the generation and consumption. The second layer is fog layer which manages the user’s requests, network resources and acts as a middle layer between end users and cloud. Fog creates virtual machines to process multiple users request simultaneously, which increases the overall performance of the communication system. MG is connected with the fogs to fulfill the energy requirement of users. The top layer is cloud layer. All the fogs are connected with a central cloud. Cloud provides services to end users by itself or through the fog. For efficient allocation of fog resources, artificial bee colony (ABC) load balancing algorithm is proposed. Finally, simulation is done to compare the performance of ABC with three other load balancing algorithms, particle swarm optimization (PSO), round robin (RR) and throttled. While considering the proposed scenario, results of these algorithms are compared and it is concluded that performance of ABC is better than RR, PSO and throttled.

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. Fang, X., Misra, S., Xue, G., Yang, D.: Smart grid the new and improved power grid: a survey. IEEE Commun. Surv. Tutorials 14(4), 944–980 (2012). https://doi.org/10.1109/SURV.2011.101911.00087

    Article  Google Scholar 

  2. Jing, J., Qian, Y.: Distributed communication architecture for smart grid applications. IEEE Commun. Mag. 54(12), 60–67 (2016)

    Article  Google Scholar 

  3. Luo, F.: Cloud-Based information infrastructure for next-generation power grid: conception, architecture, and applications. IEEE Trans. Smart Grid 7(4), 1896–1912 (2016). https://doi.org/10.1109/TSG.2015.2452293

    Article  Google Scholar 

  4. Al Faruque, M.A., Vatanparvar, K.: Energy management-as-a-service over fog computing platform. IEEE Internet Things J. 3(2), 161–169 (2016). https://doi.org/10.1109/JIOT.2015.2471260

    Article  Google Scholar 

  5. Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the Internet of Things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13-16. ACM (2012)

    Google Scholar 

  6. Li, Y., Chen, M., Dai, W., Qiu, M.: Energy optimization with dynamic task scheduling mobile cloud computing. IEEE Syst. J. 11(1), 96–105 (2017)

    Article  Google Scholar 

  7. Zahoor, S., Javaid, N., Khan, A., Muhammad, F.J., Zahid, M., Guizani, M.: A cloud-fog- based smart grid model for efficient resource utilization. In: 14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC-2018) (2018)

    Google Scholar 

  8. Chekired, D.A., Khoukhi, L.: Smat grid solution for charging and discharging services based on cloud computing scheduling. IEEE Trans. Industr. Inf. 13(6), 3312–3321 (2017)

    Article  Google Scholar 

  9. Kumar, N., Vasilakos, A.V., Rodrigues, J.J.P.C.: A multi-tenant cloud-based DC nano grid for self-sustained smart buildings in smart cities. IEEE Commun. Mag. 55(3), 14–21 (2017). https://doi.org/10.1109/MCOM.2017.1600228CM

    Article  Google Scholar 

  10. Javaid, S., Javaid, N., Tayyaba, S., Sattar, N. A., Ruqia, B., Zahid, M.: Resource allocation using Fog-2-Cloud based environment for smart buildings. In: IEEE International Wireless Communications and Mobile Computing Conference (IWCMC-2018) (2018)

    Google Scholar 

  11. Fatima, I., Javaid, N., Iqbal, M.N., Shafi, I., Anjum, A., Memon, U.: Integration of cloud and fog based environment for effective resource distribution in smart buildings. In: IEEE International Wireless Communications and Mobile Computing Conference

    Google Scholar 

  12. Yasmeen, A., Javaid, N., Iftkhar, H., Rehman, O., Malik, M.F.: Efficient resource provisioning for smart buildings utilizing fog and cloud based environment. In: IEEE International Wireless Communications and Mobile Computing Conference (IWCMC 2018) (2018)

    Google Scholar 

  13. 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). https://doi.org/10.1109/JIOT.2016.2565516

    Article  Google Scholar 

  14. Chen, S.L., Chen, Y.Y., Kuo, S.H.: CLB: a novel load balancing architecture and algorithm for cloud services. https://doi.org/10.1016/j.compeleceng.2016.01.029

  15. Gu, C., Fan, L., Wu, W., Huang, H., Jia, X.: Greening cloud data centers in an economical way by energy trading with power grid. https://doi.org/10.1016/j.future.2016.12.029

  16. Goudarzi, M., Zamani, M., Haghighat, A.T.: A fast hybrid multi-site computation offloading for mobile cloud computing. https://doi.org/10.1016/j.jnca.2016.12.031

  17. Wickremasinghe, B., Buyya, R.: CloudAnalyst: a cloudsim-based tool for modeling and analysis of large scale cloud computing enviornments. MEDC project report 22, no. 6, pp. 433–659 (2009)

    Google Scholar 

  18. Idrissi, A., Zegrari, F.: A new approach for a better load balancing and a better distribution of resources in cloud computing. https://doi.org/10.14569/IJACSA.2015.061036

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nadeem Javaid .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ismail, M., Javaid, N., Zakria, M., Zubair, M., Saeed, F., Zaheer, M.A. (2019). Cloud-Fog Based Smart Grid Paradigm for Effective Resource Distribution. In: Barolli, L., Kryvinska, N., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-98530-5_20

Download citation

Publish with us

Policies and ethics