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Design of Node Level Load Balancing in Hierarchical Fog Structure

  • Jai Geetha
  • , D. S. Jayalakshmi
  • , Chandrika Prasad
  • , N. N. Srinidhi*
  • , E. Naresh*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Fog computing refers to the operations performed in a distributed network of nodes on the edge of the network to provide faster output generation for emergent requests. The fog layer brings computation closer to the devices, thereby reducing latency in the network. However, in recent years, fog computing has been subjected to several problems, including load balancing. Load balancing ensures appropriate allocation and distribution of resources and workload in a fog network. That being said, it’s important to note that the nature of nodes in a fog network can be heterogeneous. In such a situation, it’s crucial to have a load balancing mechanism that routes requests to the appropriate node based on the type of the requests, the load on the nodes, and the total load on the system. One way to solve the issue would be applying the conventional load balancing algorithms, but traditional load balancing schemes don’t apply here since they tend to work amongst homogeneous sets of nodes with similar resources. In the proposed research, three load balancing schemes, namely- Highest Capacity Mode, Earliest Predicted Response Time, Equal Capacity Mode have been proposed. Finally, an optimal approach, a hybrid algorithm, and taps on the benefits of the 3 types of load balancing schemes have been proposed with the best performance among all the schemes.

Original languageEnglish
Pages (from-to)597-607
Number of pages11
JournalInternational Journal of Computing and Digital Systems
Volume16
Issue number1
DOIs
Publication statusPublished - 07-2024

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence
  • Management of Technology and Innovation

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