Cloud Task Scheduling using Particle Swarm Optimization and Capuchin Search Algorithms

Cloud providers offer heterogeneous virtual machines for the execution of a variety of tasks requested by users. These virtual machines are managed by the cloud provider, eliminating the need for users to set up and maintain their hardware. This makes accessing the computing resources necessary to run applications and services more accessible and cost-effective. The task scheduling problem can be expressed as a discrete optimization issue known as NP-hard. To address this problem, we propose a hybrid meta-heuristic algorithm using the Capuchin Search Algorithm (CapSA) and the Particle Swarm Optimization (PSO) algorithm. PSO excels in global exploration, while CapSA is adept at fine-tuning solutions through local search. We aim to achieve better convergence and solution quality by integrating both algorithms. Our proposed method's performance is thoroughly evaluated through extensive experimentation, comparing it to standalone PSO and CapSA approaches. The findings reveal that our hybrid algorithm outperforms the individual techniques in terms of both total execution time and total execution cost metrics. The novelty of our work lies in the synergistic integration of PSO and CapSA, addressing the limitations of traditional optimization methods for cloud task scheduling. The proposed hybrid approach opens up intriguing directions for future research in dynamic task scheduling, multi-objective optimization, adaptive algorithms, integration with emerging technologies, and real-world deployment scenarios.

@article{WANG2023,
  title     = {Cloud Task Scheduling using Particle Swarm Optimization and Capuchin Search Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {7},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Gang WANG and Jiayin FENG and Dongyan JIA and Jinling SONG and Guolin LI},
  doi       = {10.14569/IJACSA.2023.01407109},
  url       = {https://doi.org/10.14569/IJACSA.2023.01407109}
}