Abstract:

The efficient utilization of resources across nodes in a heterogeneous cluster is pivotal for enhancing the overall resource utilization of the cluster. One of the primary functions of Kubernetes, the preferred system for container orchestration, is to schedule pods onto appropriate nodes. A well-designed scheduling strategy can reduce resource fragmentation, enhance the utilization of node resources, and minimize job waiting times. In the context of resource scheduling within heterogeneous clusters, the default Central Processing Unit (CPU)- and memory scheduling algorithms of Kubernetes have become inadequate for addressing the demands of multi-dimensional resource job scheduling. To address this challenge, a multi-criteria scheduling algorithm integrating an Analytic Hierarchy Process (AHP) and binpacking scheduling concepts is proposed. This algorithm considers not only traditional CPU and memory resource indicators but also incorporates various other resource indicators such as storage, GPU-like accelerator cards, and images, enabling a more comprehensive resource assessment. It also combines subjective user decisions with quantified objective node resource constraints to select the most suitable node from multiple candidates for job scheduling. A custom scheduler is designed based on the Kubernetes scheduling framework. The use of a custom scheduler with a multi-criteria scheduling algorithm can more efficiently utilize node resources and reduce the job waiting time caused by resource fragmentation. Experiments on mixed deployments of different types of jobs in heterogeneous clusters demonstrate that the multi-criteria scheduling algorithm can effectively increase cluster resource utilization and reduce Pod scheduling wait times by 49.79%. This provides an important reference for optimizing scheduling strategies for future large-scale cloud platforms and data centers.

Key words: heterogeneous cluster, container orchestration, Kubernetes scheduling strategy, binpacking scheduling, Analytic Hierarchy Process (AHP), scheduling framework

摘要：

异构集群中充分利用各节点资源是提高集群资源利用率的关键。Kubernetes作为容器编排领域的首选系统, 其主要功能之一是将作业调度到合适节点。合理调度策略能够减少资源碎片的产生, 提高节点资源利用率, 减少作业等待时间。在异构集群资源调度场景下, Kubernetes默认的中央处理器(CPU)与内存调度算法已难以有效应对多维资源作业调度需求。面对这一挑战, 提出一种融合层次分析法(AHP)和装箱调度思想的多指标调度算法, 不仅考虑了传统的CPU和内存资源指标, 还融入了存储、类GPU加速卡、镜像等多种资源指标, 实现了更全面的资源评估, 同时将用户主观决策与节点客观资源限制量化相结合, 从多个节点中决策出最符合作业需求的节点进行调度, 并基于Kubernetes调度框架设计了自定义调度器。使用多指标调度算法的自定义调度器能够更高效利用节点资源, 减少因资源碎片化造成的作业等待。在异构集群中对不同类型作业的混合部署进行实验, 实验结果表明, 多指标调度算法能够有效提高集群资源利用率, 减少了49.79%的Pod等待调度时间, 这为未来大型云平台和数据中心在调度策略上的优化提供了重要参考。

关键词: 异构集群, 容器编排, Kubernetes调度策略, 装箱调度, 层次分析法, 调度框架