Experiment 6: K-Means November 27, 2018 1 Description In this exercise, you will use K-means to compress an image by reducing the number of colors it contains. To begin, download data6.zip and unpack RGB values of the 16 colors present in the image. In this exercise, you will use K-means to reduce the color count to k = 16. That is, you will compute 16 colors as the cluster centroids and replace each instead run K-means on the 128×128 image bird small.tiff. Once you have computed the cluster centroids on the small image, you will then use the 16 colors to replace the pixels in the large image. 3 K-means

Lecture 7: K-Means Feng Li Shandong University fli@sdu.edu.cn December 28, 2021 Feng Li (SDU) K-Means December 28, 2021 1 / 46 Outline 1 Clustering 2 K-Means Method 3 K-Means Optimization Problem Problem 4 Kernel K-Means 5 Hierarchical Clustering Feng Li (SDU) K-Means December 28, 2021 2 / 46 Clustering Usually an unsupervised learning problem Given: N unlabeled examples {x1, · · · , xN}; no no. of desired partitions K Goal: Group the examples into K “homogeneous” partitions Loosely speaking, it is classification without ground truth labels A good clustering is one that achieves: High within-cluster

Sebastian Toader & Zsolt Varga 2021-Feb-26 Apache Kafka with Istio on K8s 2 • Scalability • Resiliency • Security • Observability • Disaster recovery Production grade Apache Kafka on Kubernetes certificate attached automatically by Istio Proxy sidecar container • Client certificate includes the K8s service account of the Kafka client application • SPIFE:///ns//sa/

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A. 准备主机/虚拟机 安装 k8s 集群需要至少 4 台主机/虚拟机，下面是参考配置： 1 台作为 k8s master CPU：2 核, 内存：8GB, 系统盘：40GB, docker 数据盘：80GB 3 台作为 k8s node CPU：2 核, 内存：16GB, 系统盘：40GB, docker 数据盘：40GB, ceph 数据盘：1TB *下面是 vSphere vSphere 上创建虚拟机的步骤： A1. 创建 k8s-master CPU：2 核, 内存：8GB，系统盘：40GB，docker 数据盘：80GB step1. 从模板上新建虚拟机 Step2. 配置虚拟机网络 打开虚拟机的控制台： 设置主机名： hostnamectl set-hostname k8s-master 设置网络： cd /etc/s /dev/mapper/centos-docker df –Th A2. 创建 k8s-node1, k8s-node2, k8s-node3 内存：16GB, 系统盘：40GB，docker 数据盘：40GB，ceph 数据盘：200GB 【注】所有节点(k8s-master, k8s-node1, k8s-node2, k8s-node3)均需做以下 B~D： B. 升级&配置 centos7

Technology | United States Product and Service K3s Advancing the Tactical Edge with K3s and SUSE RGS 2 www.susergs.com Advancing the Tactical Edge with K3s and SUSE RGS Introducing Booz Allen Hamilton give warfighters the information edge on the battlefield. Capitalizing on open source solutions like K3s, Booz Allen’s SmartEdge solution allows bat- talions to make real-time, data-driven decisions which and increase the probability of mission success. 3 www.susergs.com Advancing the Tactical Edge with K3s and SUSE RGS Working in collaboration with Brandon Gul- la, Tim Nicklas, Chris Nuber and the team

Zabbix高级应用 --K8s集群监控 ����� ��������FiberHome 演讲主题： • CactiFans • Zabbix • Microservice • K8s • Golang https://blog.cactifans.com/ 无边界监控 应用架构发展趋势 数据采集 RESTful 接口 LLD – 低级别发现 低级发现提供了一种监控主机上变化实体，并 对采集器采集的原始数据通过管道进行处理，最终达到 数据展现和存储的要求。 例如 SNMP采集交换机内存 15MB string? 图形？触发器？ Kubernetes 架构 k8s node k8s pod k8s master 应用 Zabbix Agent HTTP Agent CPU/内存/磁盘/网络 服务/日志/... 状态/CPU/内存 Kubernetes 监控 Kubernetes metrics-server ① ② ③ Token Pods https://K8S_API_SERVER:6443/apis/metrics.k8s.io/v1beta1/pods Kubernetes Metrics Server API Nodes https://K8S_API_SERVER:6443/apis/metrics.k8s.io/v1beta1/nodes Zabbix HTTP Agent

k8s操作手册 前言： 1.蓝色字体表示命令行命令，正式执行时不要复制前面的#号，#号只是提示应 该使用root权限操作 2.绿色字体表示注释，有时注释太多就不用绿色表示了 3.注意：本文档的所有操作请先在测环境进行实践，请不要直接在真实的服务 器中操作！ 版权声明： 本文档以开源的形式发布，所有条款如下： （1）无担保：作者不保证文档内容的准确无误，亦不承担由于使用此文档所导致的任何后果 更新日期：2023-12-29 ★第0章、K8S集群搭建准备工作 相关单词原义： docker 码头工人 pod 集装箱 kubernetes 舵手，领航员 helm 舵轮，驾驶盘 chart 图表，海图 ①k8s对系统要求 linux内核在3.10及以上 ②规划主机名及ip k8s的服务器使用固定ip地址，配置主机名，要求能解析相应的主机名（master 结点）到对应的ip地址，可以使用内网集群的dns服务器或写入/etc/hosts文件 里。如： 主机名 ip地址 k8s-master1.cof-lee.com 10.99.1.51 k8s-master2.cof-lee.com 10.99.1.52 k8s-master3.cof-lee

Scaling a Multi-Tenant k8s Cluster in a Telco Pablo Moncada October 28, 2020 About MasMovil group ● 4th telecom company in Spain ● Provides voice and broadband services to +12M customers ● Several engineers ● Reduce costs ● Security issues ● Scalability issues Namespaces +400 Pods +10k Services +3k CPU +2k Mem +5TB Nodes +300 kube-proxy replacement NetworkPolicy logging Multi-cluster