Optimal Scheduling using Advanced Cat Swarm Optimization Algorithm to Improve Performance in Fog Computing

Fog computing can be considered a decentralized computing approach that essentially extends the capabilities offered by cloud computing to the periphery of the network. In addition, due to its proximity to the user, fog computing proves to be highly efficient in minimizing the volume of data that needs to be transmitted, reducing overall network traffic, and shortening the distance that data must travel. But this technology, like other new technologies, has challenges, and scheduling and optimal allocation of resources is one of the most important of these challenges. Accordingly, this research aims to propose an optimal solution for efficient scheduling within the fog computing environment through the application of the advanced cat swarm optimization algorithm. In this solution, the two main behaviors of cats are implemented in the form of seek and tracking states. Accordingly, processing nodes are periodically examined and categorized based on the number of available resources; servers with highly available resources are prioritized in the scheduling process for efficient scheduling. Subsequently, the congested servers, which may be experiencing various issues, are migrated to alternative servers with ample resources using the virtual machine live migration technique. Ultimately, the effectiveness of the proposed solution is assessed using the iFogSim simulator, demonstrating notable reductions in execution time and energy consumption. So, the proposed solution has led to a 20% reduction in execution time while also improving energy efficiency by more than 15% on average. This optimization represents a trade-off between improving performance and reducing resource consumption.

@article{Huo2023,
  title     = {Optimal Scheduling using Advanced Cat Swarm Optimization Algorithm to Improve Performance in Fog Computing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {7},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Xiaoyan Huo and Xuemei Wang},
  doi       = {10.14569/IJACSA.2023.01407114},
  url       = {https://doi.org/10.14569/IJACSA.2023.01407114}
}