A Fog-Cluster Based Load-Balancing Technique

Prabhdeep Singh, Rajbir Kaur, Junaid Rashid, Sapna Juneja, Gaurav Dhiman, Jungeun Kim and Mariya Ouaissa
Additional contact information
Prabhdeep Singh: Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248002, 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
Jungeun Kim: Department of Computer Science and Engineering, Kongju National University, Cheonan 31080, Korea
Mariya Ouaissa: Department of Computer Science, Moulay Ismail University, Marjane 2, BP: 298, Meknes 50050, Morocco

Sustainability, 2022, vol. 14, issue 13, 1-14

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.

Keywords: load balancing; cloud computing; fog computing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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