Task Scheduling In Cloud Computing Using Metaheuristic Algorithm

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.