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Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

Author

Listed:
  • Rasool Bukhsh

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan)

  • Nadeem Javaid

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan)

  • Zahoor Ali Khan

    (CIS, Higher Colleges of Technology, Fujairah 4114, UAE)

  • Farruh Ishmanov

    (Department of Electronics and Communication Engineering, Kwangwoon University, Seoul 01897, Korea)

  • Muhammad Khalil Afzal

    (Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantonment 47040, Pakistan)

  • Zahid Wadud

    (Faculty of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan)

Abstract

The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids . Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud–fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.

Suggested Citation

  • Rasool Bukhsh & Nadeem Javaid & Zahoor Ali Khan & Farruh Ishmanov & Muhammad Khalil Afzal & Zahid Wadud, 2018. "Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid," Energies, MDPI, vol. 11(12), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3345-:d:186781
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    References listed on IDEAS

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    1. Saman Zahoor & Sakeena Javaid & Nadeem Javaid & Mahmood Ashraf & Farruh Ishmanov & Muhammad Khalil Afzal, 2018. "Cloud–Fog–Based Smart Grid Model for Efficient Resource Management," Sustainability, MDPI, vol. 10(6), pages 1-21, June.
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    5. Omaji Samuel & Nadeem Javaid & Mahmood Ashraf & Farruh Ishmanov & Muhammad Khalil Afzal & Zahoor Ali Khan, 2018. "Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid," Energies, MDPI, vol. 11(6), pages 1-29, June.
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    7. Rasool Bakhsh & Nadeem Javaid & Itrat Fatima & Majid Iqbal Khan & Khaled. A. Almejalli, 2018. "Towards Efficient Resource Utilization Exploiting Collaboration between HPF and 5G Enabled Energy Management Controllers in Smart Homes," Sustainability, MDPI, vol. 10(10), pages 1-24, October.
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    Cited by:

    1. Gisliany Alves & Danielle Marques & Ivanovitch Silva & Luiz Affonso Guedes & Maria da Guia da Silva, 2019. "A Methodology for Dependability Evaluation of Smart Grids," Energies, MDPI, vol. 12(9), pages 1-23, May.
    2. Haghnegahdar, Lida & Chen, Yu & Wang, Yong, 2022. "Enhancing dynamic energy network management using a multiagent cloud-fog structure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    3. Jones Luís Schaefer & Julio Cezar Mairesse Siluk & Patrícia Stefan de Carvalho & José Renes Pinheiro & Paulo Smith Schneider, 2020. "Management Challenges and Opportunities for Energy Cloud Development and Diffusion," Energies, MDPI, vol. 13(16), pages 1-27, August.
    4. S. Sofana Reka & Prakash Venugopal & V. Ravi & Tomislav Dragicevic, 2023. "Privacy-Based Demand Response Modeling for Residential Consumers Using Machine Learning with a Cloud–Fog-Based Smart Grid Environment," Energies, MDPI, vol. 16(4), pages 1-16, February.

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