Since its release in 2014, Kubernetes has become a popular choice for orchestrating containerized workloads at scale. In order to determine the most appropriate node to host a given workload, Kubernetes makes use of a scheduler that takes into account a set of hard and soft constraints defined by workload owners and cluster administrators. Despite being highly configurable, the default Kubernetes scheduler cannot fully meet the requirements of emerging applications, such as machine/deep learning workloads and edge computing applications. This has led to different proposals of custom Kubernetes schedulers that focus on addressing the requirements of the aforementioned applications. Since the related literature is growing in this area, we aimed, in this survey, to provide a classification of the related literature based on multiple criteria, including scheduling objectives as well as the types of considered workloads and environments. Additionally, we provide an overview of the main approaches that have been adopted to achieve each objective. Finally, we highlight a set of gaps that could be leveraged by the academia or industry to drive further research and development activities in the area of custom scheduling in Kubernetes.

自 2014 年发布以来，Kubernetes 已成为大规模编排容器化工作负载的流行选择。为了确定最适合托管给定工作负载的节点，Kubernetes 使用了一个调度程序，该调度程序考虑了一组由工作负载所有者和集群管理员定义的硬约束和软约束。尽管具有高度可配置性，但默认的 Kubernetes 调度程序无法完全满足新兴应用程序的需求，例如机器/深度学习工作负载和边缘计算应用程序。这导致了定制 Kubernetes 调度程序的不同建议，这些调度程序专注于解决上述应用程序的需求。由于该领域的相关文献越来越多，因此在本次调查中，我们旨在根据多个标准对相关文献进行分类，包括调度目标以及所考虑的工作负载和环境的类型。此外，我们概述了实现每个目标所采用的主要方法。最后，我们强调了学术界或工业界可以利用的一系列差距，以推动 Kubernetes 自定义调度领域的进一步研究和开发活动。