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Effective Resource Allocation in Fog for Efficient Energy Distribution

  • Abdullah Sadam
  • Nadeem Javaid
  • Muhammad Usman Sharif
  • Abdul Wasi Zia
  • Muhammad Yousaf
  • Syed Muhammad Saleh Arfi
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 22)

Abstract

Fog computing is used to distribute the workload from cloud, decrease Network Latency (NL) and Service Response Time (SRT). Cloud have the capability to respond to too many requests from consumer side, however, the physical distance between a consumer and cloud is far than the consumer and fog. Fog is limited to a specific location, moreover, fog is meant to deal requests locally and helps out in processing the Consumer’s Requests (CRs) and provide efficient response. A fog holds the consumer’s data temporarily, processes it and provides response then sends it to cloud for permanent storage. Apart from this, it also sends the consumer’s data when Micro Grids (MGs) are not able to fulfill the consumer’s energy demand. Cloud communicates with Macro Grid. Fog and cloud computing concepts are integrated to create an environment for effective energy management of a building in a residential cluster. Fog deals with requests at the consumer’s end, because it is nearer to the consumer than a cloud. The theme of this paper is efficient allocation of Virtual Machines (VMs) in a fog, therefore, Insertion Sort Based Load Balancing Algorithm (ISBLBA) is used for this purpose. Simulations have been conducted, comparing ISBLBA to Round Robin (RR) technique and results regarding fog performance, cluster performance and cost are elucidated in the Sect. 5.

Keywords

Insertion Sort Based Load Balancing Algorithm (ISBLBA) Cloud computing Fog computing Micro Grid (MG) Macro Grid 

References

  1. 1.
    Melhem, F.Y., Moubayed, N., Grunder, O.: Residential energy management in smart grid considering renewable energy sources and vehicle-to-grid integration. In: IEEE Electrical Power and Energy Conference (EPEC), 12–14 October 2016, Ottawa, ON, Canada, pp. 1–6 (2016)Google Scholar
  2. 2.
    Yaghmaee, M.H., Moghaddassian, M., Garcia, A.L.: Power consumption scheduling for future connected smart homes using bi-level cost-wise optimization approach. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (2016)Google Scholar
  3. 3.
    Markovic, D.S., Zivkovic, D., Branovic, I., Popovic, R., Cvetkovic, D.: Smart power grid and cloud computing. Renew. Sustain. Energy Rev. 24, 566–577 (2013)CrossRefGoogle Scholar
  4. 4.
    Chen, S., Zhang, T., Shi, W.: Fog computing. IEEE Internet Comput. 21(2), 4–6 (2017)CrossRefGoogle Scholar
  5. 5.
    Javaid, S., Javaid, N., Aslam, S., Munir, K., Alam, M.: A cloud to fog to consumer based framework for intelligent resource allocation in smart buildings, pp. 1–10Google Scholar
  6. 6.
    Fatima, I., Javaid, N.: Integration of cloud and fog based environment for effective resource distribution in smart buildingsGoogle Scholar
  7. 7.
    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
  8. 8.
    Cao, Z., Lin, J., Wan, C., Song, Y., Zhang, Y., Wang, X.: Optimal cloud computing resource allocation for demand side management. IEEE Trans. Smart Grid 1–13 (2016).  https://doi.org/10.1109/TSG.2015.2512712
  9. 9.
    Faruque, M.A.A., Vatanparvar, K.: Energy management-as-a-service over fog computing platform. IEEE Internet Things J. 3(2), 161–169 (2016)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) (2016)Google Scholar
  11. 11.
    Wang, W.Y.C., Rashid, A., Chuang, H.M.: Toward the trend of cloud computing. Electron. Commer. Res. 12(4), 238–242 (2011)Google Scholar
  12. 12.
    Mondal, A., Misra S., Obaidat, M.: Storage in smart grid using game theory. IEEE Syst. J. 1–10 (2015)Google Scholar
  13. 13.
    Yu, M., Hong, S.H.: Supply - demand balancing for power management in smart grid: a Stackelberg game approach. Appl. Energy 164, 702–710 (2016)CrossRefGoogle Scholar
  14. 14.
    Hong, J.S., Kim, M.: Game-theory-based approach for energy routing in a smart grid network. J. Comput. Netw. Commun. 2016 (2016)Google Scholar
  15. 15.
    Ni, J., Ai, Q.: Economic power transaction using coalitional game strategy in micro-grids. IET Gener. Transm. Distrib. 10(1), 10–18 (2016)CrossRefGoogle Scholar
  16. 16.
    Mediwaththe, C.P., Stephens, E.R., Smith, D.B.: A dynamic game for electricity load management in neighborhood area networks. IEEE Trans. Smart Grid 7(3), 1–8 (2016)CrossRefGoogle Scholar
  17. 17.
    Alonso-Monsalve, S., García-Carballeira, F., Calderón, A.: A heterogeneous mobile cloud computing model for hybrid clouds. Future Gener. Comput. Syst. (2018).  https://doi.org/10.1016/j.future.2018.04.005
  18. 18.
    Tärneberg, W., Mehta, A., Wadbro, E., Tordsson, J., Eker, J., Kihl, M., Elmroth, E.: Dynamic application placement in the Mobile Cloud Network. Future Gener. Comput. Syst. 70, 163–177 (2017)CrossRefGoogle Scholar
  19. 19.
    Ramirez, W., Masip-Bruin, X., Marin-Tordera, E., Souza, V., Jukan, A., Ren, G., Gonzalez de Dios, O.: Evaluating the benefits of combined and continuous Fog-to-Cloud architectures. Comput. Commun. 113, 43–52 (2017)Google Scholar
  20. 20.
    Ciobanu, R.I., Negru, C., Pop, F., Dobre, C., Mavromoustakis, C.X., Mastorakis, G.: Drop computing: ad-hoc dynamic collaborative computing. Future Gener. Comput. Syst. (2017).  https://doi.org/10.1016/j.future.2017.11.044

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Abdullah Sadam
    • 1
  • Nadeem Javaid
    • 1
  • Muhammad Usman Sharif
    • 1
  • Abdul Wasi Zia
    • 1
  • Muhammad Yousaf
    • 1
  • Syed Muhammad Saleh Arfi
    • 1
  1. 1.COMSATS UniversityIslamabadPakistan

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