| 娄杰,段宏键,曹华伟,叶笑春.NUMA感知的云平台负载调度系统[J].高技术通讯(中文),2025,35(1):20~36 |
| NUMA感知的云平台负载调度系统 |
| NUMA-aware cloud platform workload scheduling system |
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| DOI：10. 3772 / j. issn. 1002-0470. 2025. 01. 003 |
| 中文关键词: 云计算; 容器云平台; 负载调度; 非统一存储访问; 资源划分 |
| 英文关键词: cloud computing, container cloud platform, workload scheduling, non-uniform memory access（NUMA）, resource partitioning |
| 基金项目: |
| 作者 | 单位 | | 娄杰 | (处理器芯片全国重点实验室(中国科学院计算技术研究所) 北京 100190)
(中国科学院大学计算机科学与技术学院 北京 100049) | | 段宏键 | | | 曹华伟 | | | 叶笑春 | |
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| 中文摘要: |
| 随着互联网的高速发展,云计算逐渐走向了云原生时代。在云原生领域中,对容器进行调度与编排的标准系统是 Kubernetes。 Kubernetes 有着开源、可扩展、部署难度低等诸多优点,然而,随着容器化应用的多样化和底层资源的多元化,Kubernetes 在以非统一存储访问(non-uniform memory access,NUMA)资源为代表的细粒度资源调度方面仍然存在不足,集群中计算资源利用率低、使用不均衡、系统关键资源争用等情况常常发生。本文以 Kubernetes 系统为基础,探究以 NUMA 为代表的细粒度资源的优化调度机制,具体研究点如下:(1)建立缓存管理器,对集群中基于容器的典型应用进行性能的建模与特征分析;(2)设计 NUMA 管理器,实现细粒度资源划分;(3)优化面向细粒度资源调度的算法,细粒度分配 NUMA 资源。 通过 NUMA 感知的调度优化,本文所提方案提高了系统的关键资源利用率,提升了应用的运行速度,减少了集群中资源的争用以及资源使用上不均衡的现象。 |
| 英文摘要: |
| With the rapid growth of the Internet, cloud computing is transitioning into the cloud-native era. In the cloud-native landscape, Kubernetes serves as a standard system for container scheduling and orchestration. Kubernetes offers numerous advantages such as open-source, scalability, and easiness of deployment. However, as containerized applications diversify and underlying resources become more heterogeneous, Kubernetes still faces challenges in fine-grained resource scheduling, particularly regarding non-uniform memory access（NUMA） resources. Uneven utilization of computing resources and contention for critical system resources are common in clusters. Based on the Kubernetes system, this article explores an optimized scheduling mechanism for fine-grained resources, represented by NUMA. The specific research areas are as follows. (1) Establishing a cache manager to model and analyze the performance of typical container-based applications in the cluster. (2) Designing a NUMA manager to implement fine-grained resource partitioning. (3) Optimizing algorithms for fine-grained resource scheduling and allocating fine-grained NUMA resources. Through NUMA-aware scheduling optimization, the proposed scheme enhances the utilization of critical resources, improves application performance, and reduces resource contention as well as imbalances in the cluster. |
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