Advertisement

Threshold Based Load Balancer for Efficient Resource Utilization of Smart Grid Using Cloud Computing

  • Mubariz Rehman
  • Nadeem Javaid
  • Muhammad Junaid Ali
  • Talha Saif
  • Muhammad Hassaan Ashraf
  • Sadam Hussain Abbasi
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 24)

Abstract

Cloud computing is infrastructure which provides services to end users and increases the efficiency of the system. Fog computing is the extension of cloud computing which distributes load of cloud servers on different fog servers and enhance the overall performance of cloud. Smart Grid (SG) is the combination of traditional grid and information,communication and technology. The purpose of integration of cloud-fog based system and smart grid in this paper is to enhance the energy management services. In this paper a four layered cloud-fog based architecture is proposed to reduce the load of power requests. Three different load balancing algorithms: Round Robin (RR), Particle Swarm Optimization (PSO) and Threshold Based Load Balancer (TBLB) are used for efficient resource utilization. The service broker policies used in this paper are: Dynamically Reconfigure with Load and Advanced Service Proximity. While comparing the results on both broker policies TBLB performs betters in term of Response Time (RT) and Processing Time (PT). However, Trade-off is comparative in Cost, RT and PT.

Keywords

Cloud computing Fog computing Response time Processing time Smart grid Micro grid Round Robin Particle swarm optimization Threshold based load balancer 

References

  1. 1.
    Gelazanskas, L., Gamage, K.A.A.: Demand side management in smart grid: a review and proposals for future direction. Sustain. Cities Soc. 11, 22–30 (2014)CrossRefGoogle Scholar
  2. 2.
    Kavis, M.J.: Architecting the Cloud: Design Decisions for Cloud Computing Service Models (SaaS, PaaS, and IaaS). Wiley, Hoboken (2014)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Okay, F.Y., Ozdemir, S.: A fog computing based smart grid model. In: 2016 International Symposium on Networks, Computers and Communications (ISNCC). IEEE (2016)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)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    Patel, R.: Cloud analyst: an insight of service broker policy. Int. J. Adv. Res. Comput. Commun. Eng. 4(1), 122–127 (2015)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.
    Islam, N., Waheed, S.: Fuzzy based efficient service broker policy for cloud. Int. J. Comput. Appl. 168(4) (2017)CrossRefGoogle Scholar
  10. 10.
    Okay, F.Y., Ozdemir, S.: A fog computing based smart grid model. In: 2016 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1–6. IEEE (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mubariz Rehman
    • 1
  • Nadeem Javaid
    • 1
  • Muhammad Junaid Ali
    • 1
  • Talha Saif
    • 1
  • Muhammad Hassaan Ashraf
    • 1
  • Sadam Hussain Abbasi
    • 1
  1. 1.COMSATS UniversityIslamabadPakistan

Personalised recommendations