Workflow scheduling is a key issue and remains a challenging problem in cloud computing. Faced with the large number of virtual machine (VM) types offered by cloud providers, cloud users need to choose the most appropriate VM type for each task. Multiple task scheduling sequences exist in a workflow application. Different task scheduling sequences have a significant impact on the scheduling performance. It is not easy to determine the most appropriate set of VM types for tasks and the best task scheduling sequence. Besides, the idle time slots on VM instances should be used fully to increase resources' utilization and save the execution cost of a workflow. This paper considers these three aspects simultaneously and proposes a cloud workflow scheduling approach which combines particle swarm optimization (PSO) and idle time slot-aware rules, to minimize the execution cost of a workflow application under a deadline constraint. A new particle encoding is devised to represent the VM type required by each task and the scheduling sequence of tasks. An idle time slot-aware decoding procedure is proposed to decode a particle into a scheduling solution. To handle tasks' invalid priorities caused by the randomness of PSO, a repair method is used to repair those priorities to produce valid task scheduling sequences. The proposed approach is compared with state-of-the-art cloud workflow scheduling algorithms. Experiments show that the proposed approach outperforms the comparative algorithms in terms of both of the execution cost and the success rate in meeting the deadline.

中文翻译：

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一种结合 PSO 和空闲时隙感知规则的有效云工作流调度方法


工作流调度是云计算中的一个关键问题，并且仍然是一个具有挑战性的问题。面对云提供商提供的大量虚拟机（VM）类型，云用户需要为每个任务选择最合适的VM类型。工作流应用程序中存在多个任务调度序列。不同的任务调度顺序对调度性能有显着影响。确定最适合任务的 VM 类型集和最佳任务调度顺序并不容易。此外，应充分利用虚拟机实例上的空闲时间段，以提高资源利用率并节省工作流的执行成本。本文同时考虑了这三个方面，并提出了一种结合粒子群优化（PSO）和空闲时隙感知规则的云工作流调度方法，以最小化截止日期约束下工作流应用程序的执行成本。设计了一种新的粒子编码来表示每个任务所需的VM类型以及任务的调度顺序。提出了一种空闲时隙感知解码过程来将粒子解码为调度解决方案。针对PSO随机性导致的任务优先级无效的问题，采用修复方法修复这些优先级，产生有效的任务调度序列。将所提出的方法与最先进的云工作流调度算法进行比较。实验表明，所提出的方法在执行成本和按时完成的成功率方面都优于对比算法。