Apache YuniKorn is a lightweight, universal resource scheduler for running batch workloads in Kubernetes. YuniKorn extends the scheduling capabilities of Kubernetes and supports a wide variety of workloads such as batch processing, data analytics, and machine learning tasks, with robust support for frameworks such as Spark, Flink, and TensorFlow.

YuniKorn 1.0.0 was released in 2022 and contained many major features, e.g. App-aware scheduling, Hierarchy Resource Queues, Gang Scheduling. Since then, the project has continued to grow, and until the latest release, 1.5.0 (released on March 14, 2024), many new features have been included, such as Preemption, User Quota Enforcement, and an Event System.

In this talk, we will provide a basic introduction to the project and focus on the changes and improvements that have occurred in recent releases. Besides this, we also want to look into the future and highlight some topics the community is currently working on and give an insight into the roadmap for planned features.

标题：Apache YuniKorn 的现状

Apache YuniKorn 是一个轻量级的、通用的资源调度器，用于在 Kubernetes 中运行批处理工作负载。YuniKorn 扩展了 Kubernetes 的调度能力，并支持各种工作负载，如批处理、数据分析和机器学习任务，同时对诸如 Spark、Flink 和 TensorFlow 等框架提供了强大的支持。

YuniKorn 1.0.0 于 2022 年发布，包含了许多重要功能，例如应用感知调度、层次化资源队列和队列调度。在最新的 1.5.0 版本（于 2024 年 3 月 14 日发布），新增了许多特性，如抢占、用户配额执行和事件系统。

在本次演讲中，我们将对该项目进行基本介绍，并重点讲述近期版本中的变化和改进。此外，我们还将展望未来，重点介绍社区目前正在关注的一些主题，并对计划特性的路线图提供一些见解。