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.

近年来，云计算环境（CCE）因其可扩展性、灵活性和成本效益而受到广泛关注。工作流调度是 CCE 的一个关键方面，涉及将工作流的任务分配给合适的资源以优化各种性能指标。负载均衡在工作流调度中实现资源高效利用、减少执行时间方面发挥着重要作用。人们开发了许多调度算法，其中 Min-Min 是使用独立任务的调度算法。然而，原始的 Min-Min 启发式方法没有考虑资源之间的负载分布，这可能导致资源利用率不平衡并增加执行时间。为了解决这个限制，我们引入了一种结合了负载平衡原理的改进的 Min-Min 启发式方法。考虑到任务完成时间和资源负载，该方法旨在实现最佳负载分配并最小化工作流的整体执行时间。为了评估所提出的负载平衡方法的有效性，使用基准工作流数据集进行了广泛的模拟，例如作为随机生成的工作流程和蒙太奇工作流程。结果表明，与启发式 HEFT 和 PETS 相比，改进的 Min-Min 启发式在负载平衡、完工时间、加速、效率和资源利用率方面优于启发式。该方法实现了更加均衡的资源分配，减少了工作流的完成时间，提高了系统的整体性能。本研究通过提出一种提高资源分配效率的负载平衡方法，为 CCE 中的工作流调度领域做出了贡献。研究结果强调了在任务调度中考虑负载平衡原则以优化云计算环境性能的重要性。所提出的方法可以作为参与 CCE 工作流调度的从业者和研究人员的宝贵工具，提高资源利用率并减少执行时间。