The flexibility and the variety of computing resources offered by the cloud make it particularly attractive for executing user workloads. However, IaaS cloud environments pose non-trivial challenges in the case of workflow scheduling under deadlines and monetary cost constraints. Indeed, given the typical uncertain performance behavior of cloud resources, scheduling algorithms that assume deterministic execution times may fail, thus requiring probabilistic approaches. However, existing probabilistic algorithms are computationally expensive, mainly due to the greater complexity of the workflow scheduling problem in its probabilistic form, and they hardily scale with the size of the problem instance. In this article, we propose EPOSS, a novel workflow scheduling algorithm for IaaS cloud environments based on a probabilistic formulation. Our solution blends together the low execution latency of state-of-the-art scheduling algorithms designed for the case of deterministic execution times and the capability to enforce probabilistic constraints.Designed with computational efficiency in mind, EPOSS achieves one to two orders lower execution times in comparison with existing probabilistic schedulers. Furthermore, it ensures good scaling with respect to workflow size and number of heterogeneous virtual machine types offered by the IaaS cloud environment. We evaluated the benefits of our algorithm via an experimental comparison over a variety of workloads and characteristics of IaaS cloud environments.

翻译：云平台提供的灵活性与多样化计算资源使其特别适合执行用户工作负载。然而，在截止时间和成本约束条件下，IaaS云环境中的工作流调度面临严峻挑战。鉴于云资源通常具有不确定的性能表现，采用确定性执行时间假设的调度算法可能失效，因此需要概率化方法。然而，现有概率算法计算开销巨大——这主要源于概率形式下工作流调度问题复杂度的显著增加，且难以随问题规模扩展。本文提出EPOSS，一种基于概率建模的新型IaaS云工作流调度算法。该方案融合了确定性执行时间场景下先进调度算法的低执行延迟特性与概率约束保障能力。EPOSS以计算效率为核心设计目标，相比现有概率调度器可实现一至两个数量级的执行时间降低，同时能良好适应工作流规模及IaaS云环境提供的异构虚拟机类型数量的扩展。我们通过在不同工作负载和IaaS云环境特性下的实验对比，验证了该算法的优势。