A Hybrid HS-Mean Technique for Efficient Load Balancing in Cloud Computing

  • Kainat Ansar
  • Nadeem JavaidEmail author
  • Maheen Zahid
  • Komal Tehreem
  • Hamida Bano
  • Momina Waheed
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 25)


Cloud computing is concept of distributing computing for sharing, accessing and storing services through the Internet. It provides platform, software and hardware as service to end users. In this paper concept of fog environment is merged with smart grid (SG) for efficient allocation of resources. Major challenge in cloud computing environment is load balancing. In recent years, the authors have focused on implementing meta-heuristic techniques for load balancing in cloud computing. K-Mean clustering algorithms based on Unsupervised machine learning are designed and implemented for job scheduling in cloud environment. A hybrid scheme based on K-Mean and harmony search algorithm (HSA) algorithms has been designed and implemented in this paper. Performance parameters are optimized by the K-Mean, HSA and our proposed algorithm HS-Mean. We formulate delay problem as a single-objective optimization problem. We propose an hybrid algorithm to solve it. Finally, the results show the efficiency of our algorithm.


Cloud Computing Load Balancing Algorithm Harmony Search Algorithm (HSA) Service Broker Policy Proposed Hybrid 
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.
    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
  2. 2.
    McKenna, E., Richardson, I., Thomson, M.: Smart meter data: balancing consumer privacy concerns with legitimate applications. Energy Policy 41, 807–814 (2012)CrossRefGoogle Scholar
  3. 3.
    Zahoor, S., Javaid, S., Javaid, N., Ashraf, M., Ishmanov, F., Afzal, M.K.: CloudFog based smart grid model for efficient resource management. Sustainability 10(6), 1–21 (2018)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Fang, B., Yin, X., Tan, Y., Li, C., Gao, Y., Cao, Y., Li, J.: The contributions of cloud technologies to smart grid. Renew. Sustain. Energy Rev. 59, 1326–1331 (2016)CrossRefGoogle Scholar
  6. 6.
    Bharathi, C., Rekha, D., Vijayakumar, V.: Genetic algorithm based demand side management for smart grid. Wirel. Personal Commun. 93(2), 481–502 (2017)CrossRefGoogle Scholar
  7. 7.
    Reka, S.S., Ramesh, V.: A demand response modeling for residential consumers in smart grid environment using game theory based energy scheduling algorithm. Ain Shams Eng. J. 7(2), 835–845 (2016)CrossRefGoogle Scholar
  8. 8.
    Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H., Fan, Z.: An integer linear programming based optimization for home demand-side management in smart grid. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), pp. 1–5. IEEE (2012)Google Scholar
  9. 9.
    Reka, S.S., Ramesh, V.: Demand side management scheme in smart grid with cloud computing approach using stochastic dynamic programming. Perspect. Sci. 8, 169–171 (2016)CrossRefGoogle Scholar
  10. 10.
    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, pp. 2–6 (2018)Google Scholar
  11. 11.
    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), pp. 1292–1297. IEEE (2017)Google Scholar
  12. 12.
    Rastkhadiv, F., Zamanifar, K.: Task scheduling based on load balancing using artificial bee colony in cloud computing environment. Int. J. Adv. Biotechnol. Res. (IJBR) 7(5) (2016)Google Scholar
  13. 13.
    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
  14. 14.
    Bitam, S., Zeadally, S., Mellouk, A.: Fog computing job scheduling optimization based on bees swarm. Enterp. Inf. Syst. 12(4), 373–397 (2018)CrossRefGoogle Scholar
  15. 15.
    Zahoor, S., Javaid, N., Khan, A., Ruqia, B., Muhammad, F.J., Zahid, M.: A cloud-fog-based smart grid model for efficient resource utilization (2015)Google Scholar
  16. 16.
    Aslam, S., Munir, K., Javaid, S., Javaid, N., Alam, M.: A cloud to fog to consumer based framework for intelligent resource allocation in smart buildings (2018)Google Scholar
  17. 17.
    Javaid, N., Khalid, A., Rahim, M., Mateen, A.: Smart Homes Coalition based on Game Theory (2018)Google Scholar
  18. 18.
    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
  19. 19.
    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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kainat Ansar
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Maheen Zahid
    • 1
  • Komal Tehreem
    • 1
  • Hamida Bano
    • 1
  • Momina Waheed
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

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