A novel strategy for deterministic workflow scheduling with load balancing using modified min-min heuristic in cloud computing environment

Cloud Computing Environment (CCE) has gained considerable attention in recent years because of scalability, flexibility, and cost-effectiveness. Workflow scheduling, a critical aspect of CCE, involves assigning tasks of a workflow to suitable resources to optimize various performance metrics. Load balancing plays an important role in achieving efficient resource utilization and reducing execution time in workflow scheduling. There are many scheduling algorithms are developed and Min-Min is out of them that uses independent tasks. However, the original Min-Min heuristic does not consider the load distribution among resources, which can lead to imbalanced resource utilization and increased execution time.To address this limitation, we introduce a modified Min-Min heuristic that incorporates load-balancing principles. Taking into consideration both task completion time and resource load, the method aims to achieve optimal load distribution and minimize the overall execution time of the workflow.To evaluate the effectiveness of the proposed load-balancing method, extensive simulations are performed using benchmark workflow datasets such as randomly generated workflows and Montage workflows. The results show that the modified Min-Min heuristic outperforms as compared to heuristics HEFT and PETS in terms of load balancing, makespan, speedup, efficiency,and resource utilization. The proposed method achieves more balanced resource allocation, reduces the completion time of the workflow, and improves overall system performance. The present study contributes to the area of workflow scheduling in CCE by presenting a load-balancing method that enhances the efficiency of resource allocation. The findings emphasize the importance of considering load-balancing principles in task scheduling to optimize performance in cloud computing environments. The proposed method can serve as a valuable tool for practitioners and researchers involved in workflow scheduling in CCE, offering improved resource utilization and reduced execution time.