KubeCon + CloudNativeCon + Open Source Summit + AI_dev China 2024

Currently, various Kubernetes-based task schedulers popular in the community have limited performance capabilities, which restricts the cluster scale they can handle. Due to the limitation of cluster scale, it is difficult to improve resource utilization through large-scale colocation, and more clusters also bring greater operational burdens. 1. Due to the bottleneck of the scheduler and related components, the maximum cluster scale cannot exceed 5k nodes; 2. In clusters with more than 5k Nodes, scheduling throughput cannot exceed 100 Pods/s. Godel Scheduler is a distributed high-performance scheduler based on Kubernetes, and it is now open-sourced. In this talk, we will go deep into the performance optimization methods of godel scheduler: 1. Optimize scheduling algorithms and do data structures refactor; 2. Implement optimistic concurrency under multi-shard architecture to achieve parallel computation; 3. Abstract "batch" scheduling to fully reuse scheduling computation results.

目前，社区中流行的基于Kubernetes的各种任务调度器在性能方面存在一定限制，这限制了它们能处理的集群规模。由于集群规模的限制，通过大规模的共存难以提高资源利用率，而且更多的集群也会带来更大的运维负担。1. 由于调度器及相关组件的瓶颈，最大集群规模无法超过5k个节点；2. 在超过5k个节点的集群中，调度吞吐量无法超过100个Pod/s。 Godel Scheduler是一个基于Kubernetes的分布式高性能调度器，现已开源。在本次演讲中，我们将深入探讨godel调度器的性能优化方法：1. 优化调度算法并进行数据结构重构；2. 在多分片架构下实现乐观并发以实现并行计算；3. 抽象“批量”调度以充分重用调度计算结果。