An HC-CSO Algorithm for Workflow Scheduling in Heterogeneous Cloud Computing System

Many scientists are using meta-heuristic techniques for dynamic workflow task scheduling in the area of cloud computing systems to get optimum solutions. Many swarm intelligent algorithms have been designed so far which are having many limitations as some get trapped in local optima, a few are having low convergence speed, some are having poor global search facilities, etc. Still, there is a requirement of designing a new algorithm or modification of existing algorithms to overcome the limitations of the existing techniques. A new Hybrid Cat Swarm Optimization algorithm named H-CSO was designed inspired by the HEFT algorithm and the initialization problem of the Cat Swarm Optimization was overcome. Still, that algorithm has a limitation of getting stuck in local minima. To overcome this algorithm a part of the Crow Search Algorithm has been integrated into H-CSO and described in this paper. After simulation, it was found that the new hybrid algorithm named HC-CSO outperforms CSO and H-CSO.

@article{Bhagwan2021,
  title     = {An HC-CSO Algorithm for Workflow Scheduling in Heterogeneous Cloud Computing System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {6},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Jai Bhagwan and Sanjeev Kumar},
  doi       = {10.14569/IJACSA.2021.0120655},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120655}
}