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Efficient Resource Distribution in Cloud and Fog Computing

  • Mubashar Mehmood
  • Nadeem Javaid
  • Junaid Akram
  • Sadam Hussain Abbasi
  • Abdul Rahman
  • Fahad Saeed
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 22)

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.

Keywords

Fog computing Cloud computing Load balancing Tasks scheduling Response time Smart Grid Microgrid Virtual machines 

References

  1. 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. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 4.
    Chiang, M., Zhang, T.: Fog and IoT: an overview of research opportunities. IEEE Internet Things J. 3(6), 854–864 (2016)CrossRefGoogle Scholar
  5. 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. 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. 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)CrossRefGoogle Scholar
  8. 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.2016.2584538CrossRefGoogle Scholar
  9. 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. 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. 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. 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)CrossRefGoogle Scholar
  13. 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. 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)CrossRefGoogle Scholar
  15. 15.
    Varela Souto, A.: Optimization and Energy Management of a Microgrid Based on Frequency Communications (2016)Google Scholar
  16. 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. 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)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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)CrossRefGoogle Scholar
  20. 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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mubashar Mehmood
    • 1
  • Nadeem Javaid
    • 1
  • Junaid Akram
    • 2
  • Sadam Hussain Abbasi
    • 1
  • Abdul Rahman
    • 1
  • Fahad Saeed
    • 3
  1. 1.COMSATS UniversityIslamabadPakistan
  2. 2.National University of Sciences and Technology (NUST)IslamabadPakistan
  3. 3.COMSATS University IslamabadWah CampusPakistan

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