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.

翻译：在优化云计算环境服务质量(Qos)方面,任务时间安排是一个研究周全的问题。为了保持计算需求的快速增长,云调度员最重要的Qos标准之一是执行成本。在这方面,近年来提出了若干数据驱动的深神经网络(DNNs)基于时间表表,以便在动态工作量环境中进行可扩展和高效的资源管理。但是,最佳时间安排往往依赖于复杂的、计算需求高的DNN,这意味着执行费用较高。此外,即使在非静止环境中,也不一定需要复杂的时间表表,为了提高成本效益,我们也可以暂时依赖低成本的进度表。因此,这项工作旨在解决使用代名词MetaNet模型在线动态选择时间安排政策的非三重元问题。与固定时间安排政策的传统解决方案不同,MetaNet的飞行从大量基于DNNN的方法中选择一个时间表,以优化同步的任务时间安排和执行费用。与州一级时间表相比,43NNN服务级的改进成本和13D服务级的升级,允许执行成本和13NN服务级的升级。