论文标题
学习在云计算环境中动态选择最佳的成本调度程序
Learning to Dynamically Select Cost Optimal Schedulers in Cloud Computing Environments
论文作者
论文摘要
云计算平台的运营成本是调度程序最重要的服务质量（QoS）标准之一，对于跟上日益增长的计算需求至关重要。近年来，已经提出了几种基于数据驱动的深神经网络（DNN）的调度程序，即通过为动态工作负载提供可扩展有效的资源管理来优于替代方法。但是，最新的调度程序依赖于具有高计算要求的高级DNN，这意味着高度调度成本。在非平稳环境中，可能并不总是需要最复杂的调度程序，并且依靠低成本调度程序可以暂时节省运营成本，这可能足够。在这项工作中，我们提出了Metanet，这是一种替代模型，可以预测大量基于DNN的调度程序的运营成本和计划间接费用，并选择一个即时选择一个以共同优化工作计划和执行成本。与最先进的方法相比，这有助于改善执行成本，能源使用和服务水平协议的违反高达11％，43％和13％的行为。
The operational cost of a cloud computing platform is one of the most significant Quality of Service (QoS) criteria for schedulers, crucial to keep up with the growing computational demands. Several data-driven deep neural network (DNN)-based schedulers have been proposed in recent years that outperform alternative approaches by providing scalable and effective resource management for dynamic workloads. However, state-of-the-art schedulers rely on advanced DNNs with high computational requirements, implying high scheduling costs. In non-stationary contexts, the most sophisticated schedulers may not always be required, and it may be sufficient to rely on low-cost schedulers to temporarily save operational costs. In this work, we propose MetaNet, a surrogate model that predicts the operational costs and scheduling overheads of a large number of DNN-based schedulers and chooses one on-the-fly to jointly optimize job scheduling and execution costs. This facilitates improvements in execution costs, energy usage and service level agreement violations of up to 11%, 43% and 13% compared to the state-of-the-art methods.
