Optimum Task Scheduling in Cloud Computing Environments Utilizing a Hybrid Technique
DOI:
https://doi.org/10.46610/IJMCSE.2025.v01i01.003Keywords:
Ant colony optimization, Cloud computing, Hybrid algorithm, Particle swarm optimization, Task schedulingAbstract
The cloud computing environment accommodates an enormous quantity of users which results in a large number of tasks that need to be processed by the system. The research that is being done on cloud computing is shifting its focus to how jobs should be planned so that the system can efficiently perform service demands. Based on adaptive Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), this research proposes a hybrid task scheduling technique for use in cloud computing environments. After the algorithm has efficiently obtained the initial solution through the application of the adaptive particle swarm optimization strategy, the first pheromone distribution of ACO is produced. In order to arrive at the most optimal solution for job scheduling in the end, the ACO algorithm is employed. When compared with separate PSO and ACO approaches, it is discovered that the best make span is at its lowest in the proposed hybrid algorithm. This finding supports the hypothesis that the hybrid algorithm is superior. In comparison to the ACO method, this one is 7.9% less expensive. When compared to the PSO technique, the hybrid approach's overall cost requirements are 55% lower, making it the approach with the lowest total cost needed.
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