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A Fog-Cluster Based Load-Balancing Technique

Author

Listed:
  • Prabhdeep Singh

    (Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248002, India
    Department of Computer Science and Engineering, Punjabi University, Patiala 147001, India)

  • Rajbir Kaur

    (Department of Electronics & Communication Engineering, Punjabi University, Patiala 147001, India)

  • Junaid Rashid

    (Department of Computer Science and Engineering, Kongju National University, Cheonan 31080, Korea)

  • Sapna Juneja

    (Department of Computer Science, KIET Group of Institutions, Delhi NCR, Ghaziabad 201206, India)

  • Gaurav Dhiman

    (Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248002, India
    Department of Computer Science, Government Bikram College of Commerce, Patiala 147001, India
    University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India)

  • Jungeun Kim

    (Department of Computer Science and Engineering, Kongju National University, Cheonan 31080, Korea
    Department of Software, Kongju National University, Cheonan 31080, Korea)

  • Mariya Ouaissa

    (Department of Computer Science, Moulay Ismail University, Marjane 2, BP: 298, Meknes 50050, Morocco)

Abstract

The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework.

Suggested Citation

  • Prabhdeep Singh & Rajbir Kaur & Junaid Rashid & Sapna Juneja & Gaurav Dhiman & Jungeun Kim & Mariya Ouaissa, 2022. "A Fog-Cluster Based Load-Balancing Technique," Sustainability, MDPI, vol. 14(13), pages 1-14, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7961-:d:851867
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    References listed on IDEAS

    as
    1. Ibrahim Attiya & Laith Abualigah & Doaa Elsadek & Samia Allaoua Chelloug & Mohamed Abd Elaziz, 2022. "An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing," Mathematics, MDPI, vol. 10(7), pages 1-18, March.
    2. B. Sumathy & Arindam Chakrabarty & Sandeep Gupta & Sanil S. Hishan & Bhavana Raj & Kamal Gulati & Gaurav Dhiman, 2022. "Prediction of Diabetic Retinopathy Using Health Records With Machine Learning Classifiers and Data Science," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 11(2), pages 1-16, April.
    3. Gaurav Dhiman & Gaganpreet Kaur & Mohd Anul Haq & Mohammad Shabaz, 2021. "Requirements for the Optimal Design for the Metasystematic Sustainability of Digital Double-Form Systems," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-10, November.
    4. Annu Dhankhar & Sapna Juneja & Abhinav Juneja & Vikram Bali, 2021. "Kernel Parameter Tuning to Tweak the Performance of Classifiers for Identification of Heart Diseases," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 12(4), pages 1-16, July.
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