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Hill Climbing Load Balancing Algorithm on Fog Computing

  • Maheen Zahid
  • Nadeem Javaid
  • Kainat Ansar
  • Kanza Hassan
  • Muhammad KaleemUllah Khan
  • Mohammad Waqas
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 24)

Abstract

Cloud Computing (CC) concept is an emerging field of technology. It provides shared resources through its own Data Centers (DC’s), Virtual Machines (VM’s) and servers. People now shift their data on cloud for permanent storage and online easily approachable. Fog is the extended version of cloud. It gives more features than cloud and it is a temporary storage, easily accessible and secure for consumers. Smart Grid (SG) is the way which fulfills the demand of electricity of consumers according to their requirements. Micro Grid (MG) is a part of SG. So there is a need to balance load of requests on fog using VM’s. Response Time (RT), Processing Time (PT) and delay are three main factors which, discussed in this paper with Hill Climbing Load Balancing (HCLB) technique with Optimize best RT service broker policy.

Keywords

Cloud Computing Fog Computing Virtual Machines Hill Climbing Load Balancing Technique Smart Grid 

References

  1. 1.
    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
  2. 2.
    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, May 2016Google Scholar
  3. 3.
    Barik, R.K., Gudey, S.K., Reddy, G.G., Pant, M., Dubey, H., Mankodiya, K., Kumar, V.: FogGrid: leveraging fog computing for enhanced smart grid network. arXiv preprint arXiv:1712.09645 (2017)
  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.
    Al Faruque, M.A., Vatanparvar, K.: Energy management-as-a-service over fog computing platform. IEEE Internet Things J. 3(2), 161–169 (2016)CrossRefGoogle Scholar
  6. 6.
    Li, Y., Chen, M., Dai, W., Qiu, M.: Energy optimization with dynamic task scheduling mobile cloud computing. IEEE Syst. J. 11(1), 96–105 (2017)CrossRefGoogle Scholar
  7. 7.
    Zahoor, S., Javaid, N., Khan, A., Ruqia, B., Muhammad, F.J., Zahid, 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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Melhem, F.Y., Moubayed, N., Grunder, O.: Residential energy management in smart grid considering renewable energy sources and vehicle-to-grid integration. In: 2016 IEEE Electrical Power and Energy Conference (EPEC), pp. 1–6. IEEE, October, 2016Google Scholar
  11. 11.
    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
  12. 12.
    Masip-Bruin, X., Marin-Tordera, E., Jukan, A., Ren, G.J.: Managing resources continuity from the edge to the cloud: architecture and performance. Futur. Gener. Comput. Syst. 79, 777–785 (2018)CrossRefGoogle Scholar
  13. 13.
    Tsai, C.W., Liu, S.J., Wang, Y.C.: A parallel metaheuristic data clustering framework for cloud. J. Parallel Distrib. Comput. 116, 39–49 (2017)CrossRefGoogle Scholar
  14. 14.
    Fan, Q., Ansari, N.: Application aware workload allocation for edge computing based IoT. IEEE Internet Things J. 5(3), 2146–2153 (2018)CrossRefGoogle Scholar
  15. 15.
    Yuan, H., Bi, J., Zhou, M., Sedraoui, K.: WARM: workload-aware multi-application task scheduling for revenue maximization in sdn-based cloud data center. IEEE Access 6, 645–657 (2018)CrossRefGoogle Scholar
  16. 16.
    Xue, Shengjun, Zhang, Yiyun, Xiaolong, Xu, Xing, Guowen, Xiang, Haolong, Ji, Sai: QET : a QoS-based energy-aware task scheduling method in cloud environment. Clust. Comput. 20(4), 3199–3212 (2017)CrossRefGoogle Scholar
  17. 17.
    Sharma, S.C.M., Rath, A.K.: Multi-rumen anti-grazing approach of load balancing in cloud network. Int. J. Inf. Technol. 9(2), 129–138 (2017)Google Scholar
  18. 18.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Maheen Zahid
    • 1
  • Nadeem Javaid
    • 1
  • Kainat Ansar
    • 1
  • Kanza Hassan
    • 1
  • Muhammad KaleemUllah Khan
    • 1
  • Mohammad Waqas
    • 2
  1. 1.COMSATS UniversityIslamabadPakistan
  2. 2.Buitems QuettaQuettaPakistan

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