基于Kubernetes的AI调度引擎平台

文中介绍了基于Kubernetes的AI调度引擎平台的设计与实现, 针对当前人工智能调度系统中存在的服务配置复杂, 集群中各节点计算资源利用率不均衡以及系统运维成本高等问题, 本文提出了基于Kubernetes实现容器调度和服务管理的解决方案. 结合AI调度引擎平台的需求, 从功能实现和平台架构等方面设计该平台的各个模块. 同时, 针对Kubernetes无法感知GPU资源的问题, 引入device plugin收集集群中每个节点上的GPU信息并上报给调度器. 此外, 针对Kubernetes调度策略中优选算法只考虑节点本身的资源使用率和均衡度, 未考虑不同类型的应用对节点资源的需求差异, 提出了基于皮尔逊相关系数 (Pearson correlation coefficient, PCC)的优选算法, 通过计算容器资源需求量与节点资源使用率的互补度来决定Pod的调度, 从而保证调度完成后各节点的资源均衡性.

AI Scheduling Engine Platform Based on Kubernetes

The design and realization of the AI scheduling engine platform based on Kubernetes is introduced in this paper. To tackle the problems of complex service configuration, the unbalanced utilization rate of computing resources of each node in the cluster and the high cost of system operation and maintenance in the current AI scheduling system, this study proposes a solution based on Kubernetes to implement container scheduling and service management. Combined with the requirements of the AI scheduling engine platform, the various modules of the platform are designed from such aspects as function implementation and platform architecture. At the same time, given the problem that Kubernetes cannot perceive GPU resources, Device Plugin is introduced to collect GPU information on each node in the cluster and report it to the scheduler. In addition, as priority algorithms in Kubernetes scheduling strategy only considers the resource utilization rate and balance degree of the node itself, disregarding the differences in the demand of different types of applications for node resources, priority algorithms based on Pearson correlation coefficient (PCC) is put forward. The scheduling of Pod is determined by calculating the complementary degree of container resources demand and node resource utilization rate, thus ensuring the resource balance of each node after the scheduling.