| dc.description.abstract | The increasing adoption of cloud computing has led to a widespread migration of applications
to cloud platforms in various fields. One of the major challenges in cloud computing is efficient
workflow scheduling, which involves ensuring the optimal execution of workflows while
considering constraints like deadlines and precedence. Since task scheduling in cloud
computing is an NP-hard problem, an effective metaheuristic technique is applied in this study
to address these problems. The main objective of this study is to allocate the proper resources
to jobs while satisfying quality of service standards such as processing time. The research
methodology involved testing the workflow on a small-scale data using CPLEX, followed by
the utilization of the PSO algorithm for larger-scale scenarios. The results of the analysis
demonstrated that the implementation of the PSO algorithm enabled the exploration of multiple
optimal solutions, providing greater flexibility in workflow scheduling. Based on the findings,
the conclusions drawn from this study highlight the potential implications on cloud computing.
The use of the PSO algorithm offers promising opportunities for improving workflow
scheduling efficiency. Furthermore, recommendations for future research and practice
emphasize the need for further exploration and refinement of the PSO algorithm in larger-scale
cloud computing environments. | en_US |
