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December 9-10
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Thursday, December 9 • 12:10 - 12:45
扩展 OpenYurt 用于视频分析工作负载的端到端部署 | Extending OpenYurt for End-to-End Deployment of Video Analytics Workloads - Shao Qiang & Linda Yu, Intel

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OpenYurt 是一个云边缘解决方案,适合在视频分析、云游戏等边缘环境中部署具有低延迟和大数据特性的工作负载。要以端到端和高度可扩展的方式部署工作负载,应将一些关键组件扩展到 OpenYurt,它们是:1) 入口操作符:用于将请求分派到目标工作负载 2) 应用程序负载平衡器 (ALB) 操作符:用于将请求分派到正确的普通旧数据 (POD) 3) 端到端部署操作符:为了便于用户在本演示文稿中轻松部署用例,Linda 和 Shaoqiang 将描述添加到 OpenYurt 组件的详细信息,包括将 ingress-nginx controller 扩展到 OpenYurt Nodepool(OpenYurt 的边缘),从头开始为 Nodepool 实现应用程序负载平衡器,以及端到端部署操作符,通过提供简单的应用程序接口 (API) 方便部署。视频分析用例被用作示例工作负载。

OpenYurt is a Cloud Edge solution appropriate to deploy workloads with properties of low latency and big data in edge environments such as Video Analytics, Cloud Gaming, etc. To deploy the workloads in an end-to-end and highly scalable manner, some critical components should be augmented to OpenYurt, which are: 1) Ingress Operator: for dispatching requests to the targeted workloads 2) Application Load Balancer (ALB) Operator: for dispatching requests to the right PODs 3) End-to-End deployment Operator: for user to easily deploy use cases In this presentation Linda and Shaoqiang would describe the details of the components added to OpenYurt, which include extending ingress-nginx controller to OpenYurt Nodepool (OpenYurt's edge), implementing from scratch an Application Load Balancer for Nodepool, and End-to-End deployment operator to facilitate deployment by providing an easy API. A Video Analytics use case is employed as an example workload.

avatar for Shao Qiang

Shao Qiang

Shaoqiang Chen is a software engineer of Intel. His technical interests include Kubernetes, AI and heterogenous computation. He is working on Video Analytics workloads and their easy and scalable deployment in edge environments such as OpenYurt.

Thursday December 9, 2021 12:10 - 12:45 CST
Kubecon + CloudNativeCon 演讲厅