The complex and large-scale scientific workflow applications are effectively executes on the cloud. The performance of cloud computing highly depends on the task scheduling. Optimal workflow scheduling is still a challenge that needs to be addressed due to the conflicting objectives and increasing demand for quality of service. Task scheduling is an NP-hard problem due to its complexity. The newly introduced methods for resolving the problem of task scheduling are facing challenges to take the benefits of all aspects of cloud computing. In this article, we study the joint optimization of cost and makespan of scheduling workflows in infrastructure as a service clouds and propose a new workflow scheduling scheme using deep learning. In this scheme, a deep-Q learning-based heterogeneous earliest-finish-time (DQ-HEFT) algorithm is developed, which closely integrates the deep learning mechanism with the task scheduling heuristic HEFT. The workflowsim simulator is used for the experiment of the real-world and synthetic workflows. The experiment results demonstrate the efficiency of our proposed approach compared with existing algorithms. This technique can achieve significantly better makespan and speed metrics with a remarkably higher volume of data and can run faster compared with the existing workflow scheduling algorithms in cloud computing environment.

中文翻译：

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基于Deep-Q学习的云端科学工作流异构最早完成时间调度算法

复杂、大规模的科学工作流应用在云端有效执行。云计算的性能高度依赖于任务调度。由于目标冲突和对服务质量的需求不断增加，优化工作流调度仍然是一个需要解决的挑战。由于其复杂性，任务调度是一个 NP-hard 问题。新引入的解决任务调度问题的方法正面临挑战，以充分利用云计算的各个方面。在本文中，我们研究了基础设施即服务云中调度工作流的成本和制造跨度的联合优化，并提出了一种使用深度学习的新工作流调度方案。在该方案中，开发了一种基于深度Q学习的异构最早完成时间（DQ-HEFT）算法，它将深度学习机制与任务调度启发式 HEFT 紧密结合。workflowsim 模拟器用于真实世界和合成工作流的实验。实验结果证明了我们提出的方法与现有算法相比的效率。与云计算环境中现有的工作流调度算法相比，该技术可以通过显着更高的数据量实现显着更好的制造时间和速度指标，并且可以更快地运行。