With the advent of cloud computing and the availability of data collected from increasingly powerful scientific instruments, workflows have become a prevailing mean to achieve significant scientific advances at an increased pace. Scheduling algorithms are crucial in enabling the efficient automation of these large-scale workflows, and considerable effort has been made to develop novel heuristics tailored for the cloud resource model. The majority of these algorithms focus on coarse-grained billing periods that are much larger than the average execution time of individual tasks. Instead, our work focuses on emerging finer-grained pricing schemes (e.g., per-minute billing) that provide users with more flexibility and the ability to reduce the inherent wastage that results from coarser-grained ones. We propose a scheduling algorithm whose objective is to optimize a workflow’s execution time under a budget constraint; quality of service requirement that has been overlooked in favor of optimizing cost under a deadline constraint. Our proposal addresses fundamental challenges of clouds such as resource elasticity, abundance, and heterogeneity, as well as resource performance variation and virtual machine provisioning delays. The simulation results demonstrate our algorithm’s responsiveness to environmental uncertainties and its ability to generate high-quality schedules that comply with the budget constraint while achieving faster execution times when compared to state-of-the-art algorithms.

中文翻译：

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IaaS 云中科学工作流的预算驱动调度，具有细粒度的计费周期

随着云计算的出现和从越来越强大的科学仪器收集的数据的可用性，工作流已成为以更快的速度实现重大科学进步的普遍手段。调度算法对于实现这些大规模工作流的高效自动化至关重要，并且已经做出了相当大的努力来开发为云资源模型量身定制的新型启发式算法。这些算法中的大多数都专注于比单个任务的平均执行时间长得多的粗粒度计费周期。相反，我们的工作侧重于新兴的更细粒度的定价方案（例如，按分钟计费），这些方案为用户提供了更大的灵活性，并能够减少由粗粒度定价导致的固有浪费。我们提出了一种调度算法，其目标是在预算约束下优化工作流的执行时间；服务质量要求被忽视，有利于在期限约束下优化成本。我们的提议解决了云的基本挑战，例如资源弹性、丰富度和异构性，以及资源性能变化和虚拟机供应延迟。仿真结果证明了我们的算法对环境不确定性的响应能力，以及与最先进的算法相比，它能够生成符合预算约束的高质量计划，同时实现更快的执行时间。服务质量要求被忽视，有利于在期限约束下优化成本。我们的提议解决了云的基本挑战，例如资源弹性、丰富度和异构性，以及资源性能变化和虚拟机供应延迟。仿真结果证明了我们的算法对环境不确定性的响应能力，以及与最先进的算法相比，它能够生成符合预算约束的高质量计划，同时实现更快的执行时间。服务质量要求被忽视，有利于在期限约束下优化成本。我们的提议解决了云的基本挑战，例如资源弹性、丰富度和异构性，以及资源性能变化和虚拟机供应延迟。仿真结果证明了我们的算法对环境不确定性的响应能力，以及与最先进的算法相比，它能够生成符合预算约束的高质量计划，同时实现更快的执行时间。