Skip to main content
SpringerLink
Log in

Efficient Resource Distribution in Cloud and Fog Computing

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

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 22))

Included in the following conference series:

Abstract

Smart Grid (SG) is a modern electrical grid with the combination of traditional grid and Information, Communication and Technology. SG includes various energy measures including smart meters and energy-efficient resources. With the increase in the number of Internet of Things (IoT) devices data storage and processing complexity of SG increases. To overcome these challenges cloud computing is used with SG to enhance the energy management services and provides low latency. To ensure privacy and security in cloud computing fog computing concept is introduced which increase the performance of cloud computing. The main features of fog are; location awareness, low latency and mobility. The fog computing decreases the load on the Cloud and provides same facilities as Cloud. In the proposed system, for load balancing we have used three different load balancing algorithms: Round Robin (RR), Throttled and Odds algorithm. To compare and examine the performance of the algorithms Cloud Analyst simulator is used.

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 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. Cao, Z.: Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Trans. Smart Grid 8(4), 1943–1955 (2017)

    Google Scholar 

  2. Kim, J.Y., Kim, Y.: Benefits of cloud computing adoption for smart grid security from security perspective. J. Supercomput. 72(9), 3522–3534 (2016)

    Article  Google Scholar 

  3. Faruque, A., Abdullah, M., Vatanparvar, K.: Energy management-as-a-service over fog computing platform. IEEE Internet Things J. 3(2), 161–169 (2016)

    Article  Google Scholar 

  4. Chiang, M., Zhang, T.: Fog and IoT: an overview of research opportunities. IEEE Internet Things J. 3(6), 854–864 (2016)

    Article  Google Scholar 

  5. Hussain, Md., Alam, M.S., Beg, M.M.: Fog Computing in IoT Aided Smart Grid Transition-Requirements, Prospects, Status Quos and Challenges. arXiv preprint arXiv:1802.01818 (2018)

  6. Ramadhan, G., Purboyo, T.W., Latuconsina, R.: Experimental model for load balancing in cloud computing using throttled algorithm. Int. J. Appl. Eng. Res. 13(2), 1139–1143 (2018)

    Google Scholar 

  7. Luo, F., Zhao, J., Dong, Z.Y., Chen, Y., Xu, Y., Zhang, X., Wong, K.P.: Cloud-based information infrastructure for next-generation power grid: Conception, architecture, and applications. IEEE Trans. Smart Grid 7(4), 1896–1912 (2016)

    Article  Google Scholar 

  8. Chiang, M., Zhang, T.: Fog and IoT: an overview of research opportunities. IEEE Internet Things J. 3(6), 854–864 (2016). https://doi.org/10.1109/JIOT.2016.2584538

    Article  Google Scholar 

  9. Bera, S., Misra, S., Rodrigues, J.: Cloud computing applications for smart grid: a survey. IEEE Trans. Parallel Distrib. Syst. (2014). https://doi.org/10.1109/TPDS.2014.2321378

  10. Branch, S.R., Rey, S.: Providing a load balancing method based on dragonfly optimization algorithm for resource allocation in cloud computing (2018)

    Google Scholar 

  11. Li, C., et al.: SSLB: self-similarity-based load balancing for large-scale fog computing. Arab. J. Sci. Eng., 1–12 (2018)

    Google Scholar 

  12. Chen, S.L., Chen, Y.Y., Kuo, S.H.: CLB: a novel load balancing architecture and algorithm for cloud services. Comput. Electric. Eng. 58, 154–160 (2017)

    Article  Google Scholar 

  13. Dam, S., et al.: An ant-colony-based meta-heuristic approach for load balancing in cloud computing. Appl. Comput. Intell. Soft Comput. Eng., 204–232 (2018)

    Google Scholar 

  14. Gabbar, H.A., Labbi, Y., Bower, L., Pandya, D.: Performance optimization of integrated gas and power within MG using hybrid PSOPS algorithm. Int. J. Energy Res. 40(7), 971–982 (2016)

    Article  Google Scholar 

  15. Varela Souto, A.: Optimization and Energy Management of a Microgrid Based on Frequency Communications (2016)

    Google Scholar 

  16. Armant, V., De Cauwer, M., Brown, K.N., O’Sullivan, B.: Semi-online task assignment policies for workload consolidation in cloud computing systems. Future Gener. Comput. Syst. (2018)

    Google Scholar 

  17. Chen, W., Xie, G., Li, R., Bai, Y., Fan, C., Li, K.: Efficient task scheduling for budget constrained parallel applications on heterogeneous cloud computing systems. Future Gener. Comput. Syst. 74, 1–11 (2017)

    Article  Google Scholar 

  18. Shi, Y., Chen, S., Xiang, X.: MAGA: a mobility-aware computation offloading decision for distributed mobile cloud computing. IEEE Internet Things J. 5(1), 164–174 (2018)

    Article  Google Scholar 

  19. Devi, D.C., Uthariaraj, V.R.: Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks. Sci. World J. 2016, 1–14 (2016)

    Article  Google Scholar 

  20. Wickremasinghe, B., Buyya, R.: CloudAnalyst: a cloudsim-based tool for modelling and analysis of large scale cloud computing environments. MEDC Project Rep. 22(6), 433–659 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. COMSATS University, Islamabad, 44000, Pakistan

    Mubashar Mehmood, Nadeem Javaid, Sadam Hussain Abbasi & Abdul Rahman

  2. National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan

    Junaid Akram

  3. COMSATS University Islamabad, Wah Campus, 47040, Pakistan

    Fahad Saeed

Authors
  1. Mubashar Mehmood
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadeem Javaid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junaid Akram
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sadam Hussain Abbasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abdul Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fahad Saeed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nadeem Javaid .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Faculty of Management, Comenius University in Bratislava, Bratislava, Slovakia

    Natalia Kryvinska

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  4. Department of Advanced Sciences, Hosei University, Tokyo, Japan

    Makoto Takizawa

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

Mehmood, M., Javaid, N., Akram, J., Abbasi, S.H., Rahman, A., Saeed, F. (2019). Efficient Resource Distribution in Cloud and Fog Computing. 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_18

Download citation

Publish with us

Policies and ethics

Search

Navigation