Smart Grid Management Using Cloud and Fog Computing

  • Muhammad Hassaan Ashraf
  • Nadeem JavaidEmail author
  • Sadam Hussain Abbasi
  • Mubariz Rehman
  • Muhammad Usman Sharif
  • Faizan Saeed
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 22)


Cloud computing provides Internet-based services to its consumer. Multiple requests on cloud server simultaneously cause processing latency. Fog computing act as an intermediary layer between Cloud Data Centers (CDC) and end users, to minimize the load and boost the overall performance of CDC. For efficient electricity management in smart cities, Smart Grids (SGs) are used to fulfill the electricity demand. In this paper, a proposed system designed to minimize energy wastage and distribute the surplus energy among energy deficient SGs. A three-layered cloud and fog based architecture described for efficient and fast communication between SG’s and electricity consumers. To manage the SG’s requests, fog computing introduced to reduce the processing time and response time of CDC. For efficient scheduling of SG’s requests, proposed system compare three different load balancing algorithms: Round Robin (RR), Active Monitoring Virtual Machine (AMVM) and Throttled for SGs electricity requests scheduling on fog servers. Dynamic service broker policy is used to decide that which request should be routed on fog server. For evaluation of the proposed system, results performed in cloud analyst, which shows that AMVM and Throttled outperform RR by varying virtual machine placement cost at fog servers.


SG Management Load Balancing Algorithm Service Broker Policy Election Petitions Cloud Server 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Sotiriadis, S., Bessis, N., Buyya, R.: Self managed virtual machine scheduling in Cloud systems. Inf. Sci. 433, 381–400 (2017)Google Scholar
  2. 2.
    Osanaiye, O., Chen, S., Yan, Z., Lu, R., Choo, K.K.R., Dlodlo, M.: From cloud to fog computing: a review and a conceptual live VM migration framework. IEEE Access 5, 8284–8300 (2017)CrossRefGoogle Scholar
  3. 3.
    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: 14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC-2018) (2018)Google Scholar
  4. 4.
    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: 14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC-2018) (2018)Google Scholar
  5. 5.
    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
  6. 6.
    Khalid, A., Javaid, N., Mateen, A., Ilahi, M.: Smart homes coalition based on game theory. In 32-nd IEEE International Conference on Advanced Information Networking and Applications (AINA-2018) (2018)Google Scholar
  7. 7.
    Collotta, M., Pau, G.: An innovative approach for forecasting of energy requirements to improve a smart home management system based on BLE. IEEE Trans. Green Commun. Netw. 1(1), 112–120 (2017)CrossRefGoogle Scholar
  8. 8.
    Chen, S.L., Chen, Y.Y., Kuo, S.H.: CLB: a novel load balancing architecture and algorithm for cloud services. Comput. Electr. Eng. 58, 154–160 (2017)CrossRefGoogle Scholar
  9. 9.
    Gupta, H., Sahu, K.: Honey bee behavior based load balancing of tasks in cloud computing. Int. J. Sci. Res. 3(6) (2014)Google Scholar
  10. 10.
    Pham, N.M.N., Le, V.S.: Applying Ant Colony System algorithm in multi-objective resource allocation for virtual services. J. Inf. Telecommun. 1(4), 319–333 (2017)Google Scholar
  11. 11.
    Razzaghzadeh, S., Navin, A.H., Rahmani, A.M., Hosseinzadeh, M.: Probabilistic modeling to achieve load balancing in Expert Clouds. Ad Hoc Netw. 59, 12–23 (2017)CrossRefGoogle Scholar
  12. 12.
    Cao, Z., Lin, J., Wan, C., Song, Y., Zhang, Y., Wang, X.: Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Trans. Smart Grid 8(4), 1943–1955 (2017)Google Scholar
  13. 13.
    Singh, S.P., Sharma, A., Kumar, R.: Analysis of load balancing algorithms using cloud analyst. Int. J. Grid Distrib. Comput. 9(9), 11–24 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Muhammad Hassaan Ashraf
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Sadam Hussain Abbasi
    • 1
  • Mubariz Rehman
    • 1
  • Muhammad Usman Sharif
    • 1
  • Faizan Saeed
    • 1
  1. 1.COMSATS Univeristy IslamabadIslamabadPakistan

Personalised recommendations