We investigate a workflow scheduling problem with stochastic task arrival times and fuzzy task processing times and due dates. The problem is common in many real-time and workflow-based applications, where tasks with fixed stage number and linearly dependency are executed on scalable cloud resources with multiple price options. The challenges lie in proposing effective, stable, and robust algorithms under stochastic and fuzzy tasks. A triangle fuzzy number-based model is formulated. Two metrics are explored: the cost and the degree of satisfaction. An iterated heuristic framework is proposed to periodically schedule tasks, which consists of a task collection and a fuzzy task scheduling phases. Two task collection strategies are presented and two task prioritization strategies are employed. In order to achieve a high satisfaction degree, deadline constraints are defined at both job and task levels. By designing delicate experiments and applying sophisticated statistical techniques, experimental results show that the proposed algorithm is more effective and robust than the two existing methods.

中文翻译：

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对弹性云资源具有模糊性的周期性多阶段作业调度

我们研究了具有随机任务到达时间和模糊任务处理时间和截止日期的工作流调度问题。这个问题在许多实时和基于工作流的应用程序中很常见，其中具有固定阶段数和线性依赖性的任务在具有多种价格选项的可扩展云资源上执行。挑战在于在随机和模糊任务下提出有效、稳定和健壮的算法。建立了一个基于三角形模糊数的模型。探索了两个指标：成本和满意度。提出了一种迭代启发式框架来定期调度任务，该框架由任务集合和模糊任务调度阶段组成。提出了两种任务收集策略，并采用了两种任务优先级策略。为了达到高满意度，截止日期约束在工作和任务级别定义。通过设计精细的实验和应用复杂的统计技术，实验结果表明，所提出的算法比现有的两种方法更有效和鲁棒。