For measuring the efficiency of workflow scheduling, determining makespan and execution cost is essential. As estimating makespan and cost is difficult in a Cloud environment, designing an efficient computation of workflow scheduling remains a challenge. The Cloud resources are scaled up and down in accordance with user demand by following a scheduling policy. The scalability of the work environment is achieved through the virtualization process. Based on system experience, this paper proposes the priority-based backfilling backpropagation neural network (PBF-NN) hybrid scheduling algorithm for measuring makespan and execution cost accurately. The backfill algorithm is used to schedule tasks to the available resources. The percentage of migration is reduced when this algorithm is used compared to the First Come First Serve algorithm. Then, the Berger model is used to measure the fairness of resource allocation. The system decides task reallocation based on the fairness value. The backpropagation neural network handles the virtual machine placement process with necessary training and testing. The proposed algorithm dynamically allocates the tasks and reduces the utilization of resources. We use an experimental study to illustrate how the proposed system enables higher efficiency in cost, makespan, and performance.

为了衡量工作流程调度的效率，确定制造周期和执行成本至关重要。由于在云环境中估算制造期和成本很困难，因此设计有效的工作流调度计算仍然是一个挑战。遵循调度策略，可根据用户需求按比例缩放Cloud资源。通过虚拟化过程可以实现工作环境的可伸缩性。基于系统经验，提出了一种基于优先级的回填反向传播神经网络（PBF-NN）混合调度算法，用于精确测量制造周期和执行成本。回填算法用于将任务安排到可用资源。与“先到先得”算法相比，使用此算法时，迁移百分比降低了。然后，Berger模型用于衡量资源分配的公平性。系统根据公平值决定任务重新分配。反向传播神经网络通过必要的培训和测试来处理虚拟机放置过程。所提出的算法动态地分配任务并减少资源的利用。我们使用一项实验研究来说明所提出的系统如何在成本，制造期和性能方面实现更高的效率。