A Cloud-Fog Based Smart Grid Model Using Max-Min Scheduling Algorithm for Efficient Resource Allocation

  • Sadia Rasheed
  • Nadeem JavaidEmail author
  • Saniah Rehman
  • Kanza Hassan
  • Farkhanda Zafar
  • Maria Naeem
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 22)


Cloud-fog infrastructure revolutionized the modern world, providing, low latency, high efficiency, better security, faster decision making, while lowering operational cost [1]. However, integration of Smart Grid (SGs) with cloud-fog platform provides high quality supply and secure generation, transmission and distribution of power; uninterrupted demand-supply chain management. In this paper, integration of SG uses cloud-fog based environment is proposed, for better resource distribution. Six fogs are considered in different geographical regions. Whereas, each fog is connected with clusters, each cluster consists of 500 smart homes. In order to fulfill energy demand of homes, fogs receive a number of requests, where different load balancing algorithms are used on Virtual Machines (VMs), in order to provide efficient Response Time (RT) and Processing Time (PT). However, in this paper, Max-Min algorithm is proposed, for load balancing with advanced service broker policy. Considering the proposed load balancing algorithm, results are compared with Round Robin (RR), from simulations, we conclude, proposed load balancing algorithms outperform than RR.


Cloud computing Fog computing Smart Grid Max-Min Round Robin 


  1. 1.
    Varshney, P., Simmhan, Y.: Demystifying fog computing: characterizing architectures, applications and abstractions. In: 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC). IEEE (2017)Google Scholar
  2. 2.
    Sadiku, M.N.O., Musa, S.M., Momoh, O.D.: Cloud computing: opportunities and challenges. IEEE Potentials 33(1), 34–36 (2014)CrossRefGoogle Scholar
  3. 3.
    Abbasi, B.Z., Shah, M.A.: Fog computing: security issues, solutions and robust practices. In: 2017 23rd International Conference on Automation and Computing (ICAC). IEEE (2017)Google Scholar
  4. 4.
  5. 5.
  6. 6.
    Muyeen, S.M., Rahman, S.: Communication, Control and Security Challenges for the Smart Grid. Institution of Engineering and Technology (2017)Google Scholar
  7. 7.
    Chekired, D.A., Khoukhi, L.: Smart grid solution for charging and discharging services based on cloud computing scheduling. IEEE Trans. Ind. Inform. 13(6), 3312–3321 (2017)CrossRefGoogle Scholar
  8. 8.
    Wang, L., et al.: Cloud computing: a perspective study. New Gener. Comput. 28(2), 137–146 (2010)CrossRefGoogle Scholar
  9. 9.
    Fang, X.: The new and improved power grid: a survey. IEEE Commun. Surv. Tutor. Smart grid 14(4), 944–980 (2012)CrossRefGoogle Scholar
  10. 10.
    Zahoor, S., Javaid, N.: A Cloud-fog based Smart Grid Model for Effective Information ManagementGoogle Scholar
  11. 11.
    Fatima, I., Javaid, N.: An Efficient Utilization of Fog Computing for an Optimal Resource Allocation in IoT based Smart Grid NetworkGoogle Scholar
  12. 12.
    Yasmeen, A., Javaid, N.: Exploiting Load Balancing Algorithms for Resource Allocation in Cloud and Fog Based InfrastructuresGoogle Scholar
  13. 13.
    Fatima, I., Javaid, N.: Integration of Cloud and Fog based Environment for Effective Resource Distribution in Smart BuildingsGoogle Scholar
  14. 14.
    Zahoor, S., Javaid, N., Khan, A., Ruqia, B., Muhammad, F.J., Guizani, M.: A cloud-fog-Based Smart Grid Model for Efficient Resource UtilizationGoogle Scholar
  15. 15.
    Cao, Z., et al.: Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Trans. Smart Grid 8(4), 1943–1955 (2017)Google Scholar
  16. 16.
    Domanal, S.G., Ram Mohana Reddy, G.: Optimal load balancing in cloud computing by efficient utilization of virtual machines. In: 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS), pp. 1–4. IEEE (2014)Google Scholar
  17. 17.
    Fang, Y., Wang, F., Ge, J.: A task scheduling algorithm based on load balancing in cloud computing. In: International Conference on Web Information Systems and Mining. Springer, Heidelberg (2010)Google Scholar
  18. 18.
    Pawar, N., Lilhore, U.K., Agrawal, N.: A Hybrid ACHBDF Load Balancing Method for Optimum Resource Utilization in Cloud Computing (2017)Google Scholar
  19. 19.
    Rastkhadiv, F., Zamanifar, K.: Task scheduling based on load balancing using artificial bee colony in cloud computing environment. Int. J. Adv. Biotech. Res. (IJBR) 7(5) (2016)Google Scholar
  20. 20.
    Nie, Q., Li, P.: An improved ant colony optimization algorithm for improving cloud resource utilization. In: 2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE (2016)Google Scholar
  21. 21.
    Stojkoska, B.L.R., Trivodaliev, K.V.: A review of internet of things for smart home: challenges and solutions. J. Clean. Prod. 140, 1454–1464 (2017)CrossRefGoogle Scholar
  22. 22.
    Moghaddam, M.H.Y., Leon-Garcia, A., Moghaddassian, M.: On the performance of distributed and cloud-based demand response in smart grid. IEEE Trans. Smart Grid (2017)Google Scholar
  23. 23.
    Bitam, S., Zeadally, S., Mellouk, A.: Fog computing job scheduling optimization based on bees swarm. Enterp. Inf. Syst. 12, 1–25 (2017)Google Scholar
  24. 24.
    Neto, E.C.P., Callou, G., Aires, F.: An algorithm to optimise the load distribution of fog environments. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE (2017)Google Scholar
  25. 25.
    Domanal, S.G., Ram Mohana Reddy, G.: An efficient cost optimized scheduling for spot instances in heterogeneous cloud environment. Future Gener. Comput. Syst. 84, 11–21 (2018)CrossRefGoogle Scholar
  26. 26.
    Wickremasinghe, B.: Cloud Analyst: A Cloud-Sim-Based Tool for Modeling And Analysis of Large Scale Cloud Computing Environments. MEDC Project (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sadia Rasheed
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Saniah Rehman
    • 1
  • Kanza Hassan
    • 1
  • Farkhanda Zafar
    • 1
  • Maria Naeem
    • 1
  1. 1.COMSATS UniversityIslamabadPakistan

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