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Cloud Computing Based Resource Allocation by Random Load Balancing Technique

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Abstract

In this paper, present Cloud-fog computing platform which provide efficiently their services via the internet by using remote servers to the residential areas. The increasing number of Internet of Things (IoT) devices and applications cause large data traffic on the cloud system which increase the response time and cost. To overcome this situation, fog computing concept is introduced in this paper. It also reduce the load of cloud and the latency rate of response time to the energy consumption side. Fogs have less storage capacity as compare to cloud, however have all the services available as in cloud side. The Smart Grid (SG) is a modern electric grid like smart meters and smart appliances which efficiently manage the resources allocation. In this work, consider a large geographical residential area divided into six regions and each region has a fog server to manage the energy requests coming from the end users. Each fog has a number of Virtual Machines (VMs) to efficiently manage the different user requests in minimum time and cost. The Micro Grids (MG’s) are the small scale power grid which manage the energy consumption by reducing the time and cost of end users and are connected to the fog edges. Different load balancing and optimized techniques are used in cloud computing for the efficient resources allocation to the smart residential areas. In this paper an algorithm Random load balancing is used for reliable and efficient task scheduling to overcome the latency rate and cost of user in cloud computing environment.

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References

  1. Chen, S.L., Chen, Y.Y., Kuo, S.H.C.L.B.: A novel load balancing architecture and algorithm for cloud services. Comput. Electr. Eng. 58, 154–160 (2017)

    Article  Google Scholar 

  2. Faruque, A., Abdullah, M., Vatanparvar, K.: Energy management-as-a-service over fog computing platform. IEEE Internet Things J. 3(2), 161–169 (2016)

    Article  Google Scholar 

  3. Tayal, S.: Tasks scheduling optimization for the cloud computing systems. Int. J. Adv. Eng. Sci. Technol. (IJAEST) 5(2), 111–115 (2011). pp. 1–15. http://www.ijaest.iserp.org

  4. 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. COMSATS Institute of Information Technology, Islamabad (2018)

    Google Scholar 

  5. Zahoor, S., Javaid, N., Khan, A., Muhammad, F., Zahid, M., Guizani, M.: A cloud-fog-based smart grid model for efficient resource utilization. COMSATS Institute of Information Technology, Islamabad, Sardar Bahadur Khan Women University Quetta, Pakistan, Electrical and Computer Engineering Department, University of Idaho, USA (2018)

    Google Scholar 

  6. Javaid, N., Khalid, A., Rahim, M., Mateen, A.: Smart homes coalition based on game theory. COMSATS Institute of Information Technology, Islamabad (2018)

    Google Scholar 

  7. Javaid, S., Javaid, N., Aslam, S., Munir, K., Aslam, M.: Cloud to fog to consumer based framework for intelligent resource allocation in smart buildings. COMSATS Institute of Information Technology, Islamabad (2018)

    Google Scholar 

  8. Janet, J., Sreelatha, G., Manju, A.B.: A genetic algorithm based load balancing technique (GALBT) for application processing in cloud. ARPN J. Eng. Appl. Sci. 11(17) (2016)

    Google Scholar 

  9. Idrissi, A., Zegrari, F.: A new approach for a better load balancing and a better distribution of resources in cloud computing. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 6(10), 266 (2015). http://www.ijacsa.thesai.org

    Google Scholar 

  10. Mohamed, N., Al-Jaroodi, J., Jawhar, I., Lazarova-Molnar, S., Mahmoud, S.: SmartCityWare: a service-oriented middleware for cloud and fog enabled smart city services. IEEE Access 5, 17576–17588 (2017)

    Article  Google Scholar 

  11. Rani, E., Kaur, H.: Efficient load balancing task scheduling in cloud computing using raven roosting optimization algorithm. Int. J. Adv. Res. Comput. Sci. 8(5) (2017)

    Google Scholar 

  12. Rajarajeswari, R., Vijayakumar, K., Modi, A.: Demand side management in smart grid using optimization technique for residential, commercial and industrial load. Indian J. Sci. Technol. 9(43) (2016)

    Google Scholar 

  13. Sidhu, A.K., Kinger, S.: Analysis of load balancing techniques in cloud computing. Int. J. Comput. Technol. 4(2), 737–741 (2013). ISSN 2277-3061

    Google Scholar 

  14. Domanal, S.G., Ram Mohana Reddy, G.: Optimal load balancing in cloud computing by efficient utilization of virtual machines. In: 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS), pp. 1–4. IEEE (2014)

    Google Scholar 

  15. Javaid, S., Javaid, N., Khan Tayyaba, S., Abdul Sattar, N., Ruqia, B., Guizani, M.: Resource allocation using fog-2-cloud based Environment for Smart Buildings. In: IWCMC (2018)

    Google Scholar 

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Correspondence to Nadeem Javaid .

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Bano, H., Javaid, N., Tehreem, K., Ansar, K., Zahid, M., Nazar, T. (2019). Cloud Computing Based Resource Allocation by Random Load Balancing Technique. In: Barolli, L., Leu, FY., Enokido, T., Chen, HC. (eds) Advances on Broadband and Wireless Computing, Communication and Applications. BWCCA 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-030-02613-4_3

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  • DOI: https://doi.org/10.1007/978-3-030-02613-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02612-7

  • Online ISBN: 978-3-030-02613-4

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