论文标题
metanet：在云环境中的调度策略的自动化动态选择
MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments
论文作者
论文摘要
在优化云计算环境的服务质量（QoS）的背景下，任务调度是一个充分研究的问题。为了维持计算需求的快速增长，云调度程序最重要的QoS指标之一是执行成本。在这方面，近年来提出了基于数据驱动的深神经网络（DNN）调度程序，以允许在动态工作负载设置中进行可扩展有效的资源管理。但是，最佳调度通常取决于具有较高计算需求的复杂DNN，这意味着更高的执行成本。此外，即使在非平稳环境中，也可能并不总是需要复杂的调度程序，我们可以短暂依靠低成本调度程序，以实现成本效益。因此，这项工作旨在使用称为Metanet的替代模型来解决调度策略的在线动态选择的非平凡元问题。与具有固定调度策略的传统解决方案不同，Metanet可以从基于DNN的大量方法中选择调度程序，以优化任务计划和执行成本。与最先进的DNN调度程序相比，这可以改善执行成本，能源消耗，响应时间和服务水平协议的违反，分别提高了11％，43％，8％和13％。
Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments. In order to sustain the rapid growth of computational demands, one of the most important QoS metrics for cloud schedulers is the execution cost. In this regard, several data-driven deep neural networks (DNNs) based schedulers have been proposed in recent years to allow scalable and efficient resource management in dynamic workload settings. However, optimal scheduling frequently relies on sophisticated DNNs with high computational needs implying higher execution costs. Further, even in non-stationary environments, sophisticated schedulers might not always be required and we could briefly rely on low-cost schedulers in the interest of cost-efficiency. Therefore, this work aims to solve the non-trivial meta problem of online dynamic selection of a scheduling policy using a surrogate model called MetaNet. Unlike traditional solutions with a fixed scheduling policy, MetaNet on-the-fly chooses a scheduler from a large set of DNN based methods to optimize task scheduling and execution costs in tandem. Compared to state-of-the-art DNN schedulers, this allows for improvement in execution costs, energy consumption, response time and service level agreement violations by up to 11, 43, 8 and 13 percent, respectively.
