北京时间2024年1月23日，KubeEdge发布1.16版本。新版本新增多个增强功能，在集群升级、集群易用性、边缘设备管理等方面均有大幅提升。

KubeEdge v1.16 新增特性：​

• 集群升级：支持云边组件自动化升级
• 支持边缘节点的镜像预下载
• 支持使用Keadm安装Windows边缘节点
• 增加多种容器运行时的兼容性测试
• EdgeApplication中支持更多Deployment对象字段的Override
• 支持基于Mapper-Framework的Mapper升级
• DMI数据面内置集成Redis与TDEngine数据库
• 基于Mapper-Framework的USB-Camera Mapper实现
• 易用性提升：基于Keadm的部署能力增强
• 升级K8s依赖到v1.27

新特性概览​

集群升级：支持云边组件自动化升级​

随着KubeEdge社区的持续发展，社区版本不断迭代；用户环境版本升级的诉求亟需解决。针对升级步骤难度大，边缘节点重复工作多的问题，v1.16.0版本的 KubeEdge 支持了云边组件的自动化升级。用户可以通过Keadm工具一键化升级云端，并且可以通过创建相应的Kubernetes API，批量升级边缘节点。

• 云端升级

  云端升级指令使用了三级命令与边端升级进行了区分，指令提供了让用户使用更便捷的方式来对云端的KubeEdge组件进行升级。当前版本升级完成后会打印ConfigMap历史配置，如果用户手动修改过ConfigMap，用户可以选择通过历史配置信息来还原配置文件。我们可以通过help参数查看指令的指导信息：

  keadm upgrade cloud --help
  Upgrade the cloud components to the desired version, it uses helm to upgrade the installed release of cloudcore chart, which includes all the cloud components
  
  Usage:
    keadm upgrade cloud [flags]
  
  Flags:
        --advertise-address string    Please set the same value as when you installed it, this value is only used to generate the configuration and does not regenerate the certificate. eg: 10.10.102.78,10.10.102.79
    -d, --dry-run                     Print the generated k8s resources on the stdout, not actual execute. Always use in debug mode
        --external-helm-root string   Add external helm root path to keadm
        --force                       Forced upgrading the cloud components without waiting
    -h, --help                        help for cloud
        --kube-config string          Use this key to update kube-config path, eg: $HOME/.kube/config (default "/root/.kube/config")
        --kubeedge-version string     Use this key to set the upgrade image tag
        --print-final-values          Print the final values configuration for debuging
        --profile string              Sets profile on the command line. If '--values' is specified, this is ignored
        --reuse-values                reuse the last release's values and merge in any overrides from the command line via --set and -f.
        --set stringArray             Sets values on the command line (can specify multiple or separate values with commas: key1=val1,key2=val2)
        --values stringArray          specify values in a YAML file (can specify multiple)
  

  升级指令样例：

  keadm upgrade cloud --advertise-address=<init时设置的值> --kubeedge-version=v1.16.0
  

• 边端升级

  v1.16.0版本的KubeEdge支持通过NodeUpgradeJob的Kubernetes API进行边缘节点的一键化、批量升级。API支持边缘节点的升级预检查、并发升级、失败阈值、超时处理等功能。对此，KubeEdge支持了云边任务框架。社区开发者将无需关注任务控制、状态上报等逻辑实现，只需聚焦云边任务功能本身。

  升级API样例：

  apiVersion: operations.kubeedge.io/v1alpha1
  kind: NodeUpgradeJob
  metadata:
    name: upgrade-example
    labels:
      description: upgrade-label
  spec:
    version: "v1.16.0"
    checkItems:
      - "cpu"
      - "mem"
      - "disk"
    failureTolerate: "0.3"
    concurrency: 2
    timeoutSeconds: 180
    labelSelector:
      matchLabels:
        "node-role.kubernetes.io/edge": ""
        node-role.kubernetes.io/agent: ""
  

• 兼容测试

  KubeEdge社区提供了完备了版本兼容性测试，用户在升级时仅需要保证云边版本差异不超过2个版本，就可以避免升级期间云边版本不一致带来的问题。

更多信息可参考:

https://github.com/kubeedge/kubeedge/pull/5330 https://github.com/kubeedge/kubeedge/pull/5229 https://github.com/kubeedge/kubeedge/pull/5289

支持边缘节点的镜像预下载​

新版本引入了镜像预下载新特性，用户可以通过ImagePrePullJob的Kubernetes API提前在边缘节点上加载镜像，该特性支持在批量边缘节点或节点组中预下载多个镜像，帮助减少加载镜像在应用部署或更新过程，尤其是大规模场景中，带来的失败率高、效率低下等问题。

镜像预下载API示例：

apiVersion: operations.kubeedge.io/v1alpha1
kind: ImagePrePullJob
metadata:
  name: imageprepull-example
  labels:
    description:ImagePrePullLabel
spec:
  imagePrePullTemplate：
    images:
      - image1
      - image2
    nodes：
      - edgenode1
      - edgenode2
    checkItems:
      - "disk"
    failureTolerate: "0.3"
    concurrency: 2
    timeoutSeconds: 180
    retryTimes: 1

更多信息可参考:

https://github.com/kubeedge/kubeedge/pull/5310 https://github.com/kubeedge/kubeedge/pull/5331

支持使用Keadm安装Windows边缘节点​

KubeEdge 1.15版本实现了在Windows上运行边缘节点，在新版本中，我们支持使用安装工具Keadm直接安装Windows边缘节点，操作命令与Linux边缘节点相同，简化了边缘节点的安装步骤。

更多信息可参考:

https://github.com/kubeedge/kubeedge/pull/4968

增加多种容器运行时的兼容性测试​

新版本中新增了多种容器运行时的兼容性测试，目前已集成了containerd，docker，isulad和cri-o 4种主流容器运行时，保障KubeEdge版本发布质量，用户在安装容器运行时过程中也可以参考该PR中的适配安装脚本。

更多信息可参考:

https://github.com/kubeedge/kubeedge/pull/5321

EdgeApplication中支持更多Deployment对象字段的Override​

在新版本中，我们扩展了EdgeApplication中的差异化配置项（overriders），主要的扩展有环境变量、命令参数和资源。当您不同区域的节点组环境需要链接不同的中间件时，就可以使用环境变量（env）或者命令参数（command, args）去重写中间件的链接信息。或者当您不同区域的节点资源不一致时，也可以使用资源配置（resources）去重写cpu和内存的配置。

更多信息可参考:

https://github.com/kubeedge/kubeedge/pull/5262 https://github.com/kubeedge/kubeedge/pull/5370

支持基于Mapper-Framework的Mapper升级​

1.16版本中，基于Mapper开发框架Mapper-Framework构建了Mapper组件的升级能力。新框架生成的Mapper工程以依赖引用的方式导入原有Mapper-Framework的部分功能，在需要升级时，用户能够以升级依赖版本的方式完成，简化Mapper升级流程。

• Mapper-Framework代码解耦:

  1.16版本中将Mapper-Framework中的代码解耦为用户层和业务层。用户层功能包括设备驱动及与之强相关的部分管理面数据面能力，仍会随Mapper-Framework生成在用户Mapper工程中，用户可根据实际情况修改。业务层功能包括Mapper向云端注册、云端下发Device列表等能力，会存放在kubeedge/mapper-framework子库中。

• Mapper升级框架:

  1.16版本Mapper-Framework生成的用户Mapper工程通过依赖引用的方式使用kubeedge/mapper-framework子库中业务层功能，实现完整的设备管理功能。后续用户能够通过升级依赖版本的方式达到升级Mapper的目的，不再需要手动修改大范围代码。

更多信息可参考:

https://github.com/kubeedge/kubeedge/pull/5308 https://github.com/kubeedge/kubeedge/pull/5326

DMI数据面内置集成Redis与TDEngine数据库​

1.16版本中进一步增强DMI数据面中向用户数据库推送数据的能力，增加Redis与TDengine数据库作为内置数据库。用户能够直接在device-instance配置文件中定义相关字段，实现Mapper自动向Redis与TDengine数据库推送设备数据的功能，相关数据库字段定义为：

type DBMethodRedis struct {
    // RedisClientConfig of redis database
    // +optional
    RedisClientConfig *RedisClientConfig `json:"redisClientConfig,omitempty"`
}
type RedisClientConfig struct {
    // Addr of Redis database
    // +optional
    Addr string `json:"addr,omitempty"`
    // Db of Redis database
    // +optional
    DB int `json:"db,omitempty"`
    // Poolsize of Redis database
    // +optional
    Poolsize int `json:"poolsize,omitempty"`
    // MinIdleConns of Redis database
    // +optional
    MinIdleConns int `json:"minIdleConns,omitempty"`
}

type DBMethodTDEngine struct {
    // tdengineClientConfig of tdengine database
    // +optional
    TDEngineClientConfig *TDEngineClientConfig `json:"TDEngineClientConfig,omitempty"`
}
type TDEngineClientConfig struct {
    // addr of tdEngine database
    // +optional
    Addr string `json:"addr,omitempty"`
    // dbname of tdEngine database
    // +optional
    DBName string `json:"dbName,omitempty"`
}

更多信息可参考:

https://github.com/kubeedge/kubeedge/pull/5064

基于Mapper-Framework的USB-Camera Mapper实现​

基于KubeEdge的Mapper-Framework，新版本提供了USB-Camera的Mapper样例，该Mapper根据USB协议的Camera开发，用户可根据该样例和Mapper-Framework更轻松地开发具体业务相关的Mapper。

在样例中提供了helm chart包，用户可以通过修改usbmapper-chart/values.yaml部署UBS-Camera Mapper，主要添加USB-Camera的设备文件, nodeName, USB-Camera的副本数，其余配置修改可根据具体情况而定，通过样例目录中的Dockerfile制作Mapper镜像。

global:
  replicaCounts:
......
    cameraUsbMapper:
      replicaCount: 2  #USB-Camera的副本数
      namespace: default
......
  nodeSelectorAndDevPath:
    mapper:
       - edgeNode: "edgenode02"  #USB-Camera连接的缘节点nodeName
         devPath: "/dev/video0"  #USB-Camera的设备文件
       - edgeNode: "edgenode1"
         devPath: "/dev/video17"
......

USB-Camera Mapper的部署命令如下：

helm install usbmapper-chart ./usbmapper-chart

更多信息可参考:

https://github.com/kubeedge/mappers-go/pull/122

易用性提升：基于Keadm的部署能力增强​

• 添加云边通信协议配置参数

  在KubeEdge v1.16.0中，使用keadm join边缘节点时，支持使用--hub-protocol配置云边通信协议。目前KubeEdge支持websocket和quic两种通信协议，默认为websocket协议。

  命令示例：

  keadm join --cloudcore-ipport <云节点ip>:10001 --hub-protocol=quic --kubeedge-version=v1.16.0 --token=xxxxxxxx
  

  说明：当--hub-protocol设置为quic时，需要将--cloudcore-ipport的端口设置为10001，并需在CloudCore的ConfigMap中打开quic开关，即设置modules.quic.enable为true。

  操作示例：使用kubectl edit cm -n kubeedge cloudcore，将quic的enable属性设置成true，保存修改后重启CloudCore的pod。

  modules：
  ......
    quic:
      address: 0.0.0.0
      enable: true  #quic协议开关
      maxIncomingStreams: 10000
      port: 10001
  

  更多信息可参考:

  https://github.com/kubeedge/kubeedge/pull/5156

• keadm join与CNI插件解耦

  在新版本中，keadm join边缘节点时，不需要再提前安装CNI插件，已将边缘节点的部署与CNI插件解耦。同时该功能已同步到v1.12及更高版本，欢迎用户使用新版本或升级老版本。

  说明：如果部署在边缘节点上的应用程序需要使用容器网络，则在部署完edgecore后仍然需要安装CNI插件。

  更多信息可参考:

  https://github.com/kubeedge/kubeedge/pull/5196

升级K8s依赖到v1.27​

新版本将依赖的Kubernetes版本升级到v1.27.7，您可以在云和边缘使用新版本的特性。

更多信息可参考:

https://github.com/kubeedge/kubeedge/pull/5121

版本升级注意事项​

新版本我们使用DaemonSet来管理边端的MQTT服务Eclipse Mosquitto了，我们能够通过云端Helm Values配置来设置是否要开启MQTT服务。使用DaemonSet管理MQTT后，我们可以方便的对边端MQTT进行统一管理，比如我们可以通过修改DaemonSet的配置将边端MQTT替换成EMQX。

但是如果您是从老版本升级到最新版本，则需要考虑版本兼容问题，同时使用原本由静态Pod管理的MQTT和使用新的DaemonSet管理的MQTT会产生端口冲突。兼容操作步骤参考：

1. 您可以在云端执行命令，将旧的边缘节点都打上自定义标签

kubectl label nodes --selector=node-role.kubernetes.io/edge without-mqtt-daemonset=""

2. 您可以修改MQTT DaemonSet的节点亲和性

nodeAffinity:
  requiredDuringSchedulingIgnoredDuringExecution:
    nodeSelectorTerms:
      - matchExpressions:
          - ...
          - key: without-mqtt-daemonset
            operator: Exists

3. 将节点MQTT改为由DaemonSet管理

# ------ 边端 ------
# 修改/lib/systemd/system/edgecore.service，将环境变量DEPLOY_MQTT_CONTAINER设置成false
# 这步可以放在更新EdgeCore前修改，这样就不用重启EdgeCore了
sed -i '/DEPLOY_MQTT_CONTAINER=/s/true/false/' /etc/systemd/system/edgecore.service

# 停止EdgeCore
systemctl daemon-reload && systemctl stop edgecore

# 删除MQTT容器，Containerd可以使用nerdctl替换docker
docker ps -a | grep mqtt-kubeedge | awk '{print $1}' | xargs docker rm -f

# 启动EdgeCore
systemctl start edgecore

# ------ 云端 ------
# 删除节点标签
kubectl label nodes <NODE_NAME> without-mqtt-daemonset

新版本的keadm join命令会隐藏with-mqtt参数，并且将默认值设置成false，如果您还想使用静态Pod管理MQTT，您仍然可以设置参数--with-mqtt来使其生效。with-mqtt参数在v1.18版本中将会被移除。

KubeEdge 社区已于 2022 年 7 月份完成整个 KubeEdge 项目的第三方安全审计，已发布云原生边缘计算安全威胁分析和防护白皮书，并根据安全威胁模型和安全审计的建议，对 KubeEdge 软件供应链进行持续安全加固。经过社区的不断努力，我们很兴奋地宣布，在 2023 年 1 月 18 日发布的 v1.13.0 版本中，KubeEdge 项目已达到 SLSA L3 等级（包括二进制和容器镜像构件），成为 CNCF 社区首个达到 SLSA L3 等级的项目。

为什么说达到 SLSA L3 等级对 KubeEdge 项目十分重要​

软件供应链完整性攻击（对软件包的未经授权的修改）在过去三年中呈上升趋势，KubeEdge 实现 SLSA L3 等级标准后，可以端到端的从源码构建到发布流程进行安全加固，保障用户获取到的二进制或容器镜像产物不被恶意篡改。基于 SLSA 安全框架，可以潜在地加强软件构件的完整性。SLSA 提供端到端的指导原则，可以作为软件的一组防御措施，并防止对组成软件产品的软件包的篡改或任何类型的未经授权的修改。采用 SLSA 框架可以保护项目软件免受常见的供应链攻击。

关于 SLSA​

什么是 SLSA（Supply chain Levels for Software Artifacts 软件构件的供应链级别）：

Google 提出的用于保证整个软件供应链完整性的框架 SLSA，是一套基于行业共识的安全准则，也是一个安全框架、一份标准和控制清单，用于防止篡改、提高完整性以及保护项目、业务或企业中的软件包和基础设施。它不是一个单一的工具，而是一个逐步采用的大纲，以防止工件被篡改和被篡改的工件被使用，并在更高层次上强化构成供应链的平台。生产商遵循 SLSA 准则使他们的软件更加安全，用户则根据软件包的安全状况来做出决策。

截止目前，SLSA 标准处于 alpha 阶段，相关的定义可能会发生变化。

下图描述了软件供应链中已知的攻击点。更多详细描述，可参考https://slsa.dev/

SLSA 框架引入了许多新的工具和概念，例如：

• Artifact（软件制品）：由构建流水线生成的任何制品文件，如容器镜像、语言包、编译的二进制文件等；
• Provenance （来源证据链）： 构建的元数据包括构建过程、构建源和依赖关系；
• Digest （数字摘要）：加密哈希函数的结果，该函数生成唯一标识工件的固定大小值，例如容器镜像的 SHA-256 哈希；
• Attestation （证书）：一个加密签名的文件，记录当时生成软件产物的来源；
• Immutable references（不可变引用-一种标识符）：，保证始终指向相同的、不可变的软件制品，如特定的容器图像或语言包；
• Build integrity（构建完整性）：验证构建流水线的输出完整性。

KubeEdge 项目如何达到 SLSA L3​

截止目前，SLSA 评估等级共分为 4 个等级 L1~L4，安全性由低到高，每个等级有不同的达标要求，详细的达标要求可参考 SLSA 详细标准（https://slsa.dev/spec/v0.1/requirements）。

在去年 7 月发布的第三方安全审计报告中，KubeEdge 项目在软件供应链 SLSA Provenance 维度暂未达到 L3 等级，经过 SIG-Security 的持续安全加固，在今年 1 月发布的 v1.13.0 版本中，KubeEdge 项目在所有的 SLSA 维度中均达到 L3 等级。以下表格展示了 KubeEdge 在Source、Build、Provenance、Common中的达标情况（Y 表示 KubeEdge 已达标，空格表示 SLSA 在该等级下未要求）。

SLSA 评估表格及达标情况

本章节将着重介绍 KubeEdge 如何达成 SLSA L3 等级在 Build、Provenance 维度的要求。Build/Provenance Requirements 及 KubeEdge 相应的解决方案如下。

Build Requirements：

a) 通过脚本构建：所有的构建步骤都是通过脚本自动化执行。

b) 通过构建服务进行构建：所有的构建步骤由构建服务完成，不在开发者本地环境。构建服务如 GitHub Actions、第三方云平台提供的构建服务等。

c) 作为源码构建：构建服务执行的构建定义文件和配置文件来源于版本控制系统中的文本文件，并且是可验证的。

d) 构建环境临时性：构建服务确保构建步骤在临时环境中运行，例如容器或 VM，仅为此构建提供，而不是复用先前的构建 。

e) 构建的隔离性：构建服务确保构建步骤在隔离的环境中运行，不受其他构建实例(无论是先前的还是并发的)的影响。

f) 无用户自定义参数：除了构建入口点和初始源配置之外，构建输出不会受到用户参数的影响。换句话说，构建完全是通过构建脚本定义的，而不是其他。

g) 封闭性：所有可传递的构建步骤、源和依赖项都使用不可变引用预先完全声明，并且构建步骤在没有网络访问的情况下运行。

解决方案：

KubeEdge 项目所有的构建流程均在 GitHub 上由脚本自动化执行，GitHub Actions 作为构建服务（相关的定义文件和配置文件保存在.github/workflows 目录下），可保障构建过程的可回溯、可验证以及构建环境的临时性、隔离性、构建参数和依赖项不可篡改。

Provenance Requirements：

a) 可用性：Provenance 通过用户可接受的格式提供。应该满足 SLSA Provenance 格式，但如果生产商和用户都同意，并且满足所有其他要求，可以使用另一种格式。

b) 可验证：Provenance 的真实性和完整性可以由用户验证。这应该通过来自私钥的数字签名来实现，只有生成 Provenance 的服务才能访问私钥。

c) 通过构建服务生成：Provenance 中的数据必须从构建服务中获得。

d) 不可伪造：构建服务的用户不能伪造 Provenance。

e) 第三方依赖的完整性：Provenance 记录运行构建步骤时可用的所有构建依赖项。包括构建的机器、VM 或容器的初始状态。

解决方案：

在 KubeEdge 版本发布的产物中，包括二进制文件和容器镜像 2 种格式，通过集成 SLSA 官方的 GitHub 构建项目 slsa-github-generator 来实现 SLSA L3 等级。

在 KubeEdge 版本发布的流程（.github/workflows/release.yml）中，集成了 slsa-framework/slsa-github-generator/.github/workflows/generator_generic_slsa3.yml 和 slsa-framework/slsa-github-generator/.github/workflows/generator_container_slsa3.yml，可保障构建和发布产物（包括二进制文件和容器镜像）的流程满足 SLSA L3 等级的要求。

更多关于 slsa-github-generator 的详细说明请见https://github.com/slsa-framework/slsa-github-generator

关于 Provenance​

Provenance 是构建的元数据包括构建过程、构建源和依赖关系，是软件构建和发布执行流程的一种证明，并且是可以被验证的，包括构建的源码仓库、代码分支、配置文件等信息。在 SLSA L3 级别，Provenance 内容是真实的、防篡改的，并且不会被项目维护者更改。二进制发布产物的 Provenance 文件随发布软件包一起发布，名称为 multiple.intoto.jsonl，容器镜像的 Provenance 文件随镜像文件一起上传到 KubeEdge dockerhub 公开仓库中。具体的 Provenance 格式说明，请参考https://github.com/slsa-framework/slsa-github-generator/blob/main/internal/builders/generic/README.md#provenance-format。

如何校验 KubeEdge 发布产物是否满足 SLSA L3 等级​

详细步骤描述请见https://github.com/kubeedge/kubeedge/pull/4285

校验示例如下：

$ COSIGN_EXPERIMENTAL=1 cosign verify-attestation --type slsaprovenance --policy policy.cue kubeedge/cloudcore:v1.13.0

{
    "_type": "https://in-toto.io/Statement/v0.1",
    "predicateType": "https://slsa.dev/provenance/v0.2",
    "subject": [{
            "name": "index.docker.io/kubeedge/cloudcore",
            "digest": {
                "sha256": "825642e63ab5b924e2fa0661cd14d544d0be151c4bdba6f3f42796c977fbe211"
            }
        }
    ],
    "predicate": {
        "builder": {
            "id": "https://github.com/slsa-framework/slsa-github-generator/.github/workflows/generator_container_slsa3.yml@refs/tags/v1.4.0"
        },
        "buildType": "https://github.com/slsa-framework/slsa-github-generator/container@v1",
        "invocation": {
            "configSource": {
                "uri": "git+https://github.com/kubeedge/kubeedge@refs/tags/v1.13.0",
                "digest": {
                    "sha1": "ee357a0d5341241143e88d45df99fde865c987de"
                },
                "entryPoint": ".github/workflows/release.yml"
            },
...

SLSA GitHub generator 签名和验证原理​

使用 OpenID Connect (OIDC) 向外部服务 (Sigstore) 证明工作流的身份。OpenID Connect (OIDC) 是身份提供商在网络上使用的标准，用于为第三方证明用户的身份。 GitHub 现在在其工作流程中支持 OIDC。每次运行工作流程时，运行者都可以从 GitHub 的 OIDC 提供商处创建一个唯一的 JWT 令牌。令牌包含工作流身份的可验证信息，包括调用者存储库、提交哈希、触发器以及当前（可重用）工作流路径和引用。

使用 OIDC，工作流向 Sigstore 的 Fulcio 根证书颁发机构证明其身份，后者充当外部验证服务。 Fulcio 签署了一份短期证书，证明运行器中生成的临时签名密钥并将其与工作负载身份相关联。签署出处的记录保存在 Sigstore 的透明日志 Rekor 中。用户可以使用签名证书作为信任方来验证来源是否经过身份验证且不可伪造；它必须是在受信任的构建器中创建的。流程图如下所示。

值得一提的是，SLSA GitHub generator 获得 sigstore 社区 2022 年度徽章 Best User Adopter。

总结​

SLSA 在 KubeEdge 项目软件供应链安全中发挥着重要作用。基于 sigstore 社区提供的能力，从源码到发布产物，对软件供应链端到端的整个流程进行签名和校验，确保 KubeEdge 软件供应链安全。

相关参考：

https://slsa.dev/

https://www.sigstore.dev/

https://github.com/slsa-framework/slsa-github-generator

https://slsa.dev/blog/2022/08/slsa-github-workflows-generic-ga

https://security.googleblog.com/2022/04/improving-software-supply-chain.html

https://blog.sigstore.dev/sigstore-november-roundup-8a852cec10fc/

Abstract​

The population of KubeEdge brings in community interests in the scalability and scale of KubeEdge. Now, Kubernetes clusters powered by KubeEdge, as fully tested, can stably support 100,000 concurrent edge nodes and manage more than one million pods. This report introduces the metrics used in the test, the test procedure, and the method to connect to an ocean of edge nodes.

Background​

Fast growing technologies, such as 5G networks, industrial Internet, and AI, are giving edge computing an important role in driving digital transformation. Cities, transportation, healthcare, manufacturing, and many other fields are becoming smart thanks to edge computing. According to Gartner, by 2023, the number of intelligent edge devices may be more than 20 times that of traditional IT devices. By 2028, the embedding of sensors, storage, computing, and advanced AI functions in edge devices will grow steadily. IoT devices are of various types and in large quantities. The increasing connected IoT devices are challenging management and O&M.

At the same time, users in the KubeEdge community are expecting large-scale edge deployment. There are already some successful use cases for KubeEdge. In unmanned toll stations across China, there are nearly 100,000 edge nodes and more than 500,000 edge applications in this project, and the numbers keep growing. Another case is a vehicle-cloud collaboration platform, the industry-first cloud-edge-device system. It enables fast software upgrade and iteration for software-defined vehicles. On this platform, each vehicle is connected as an edge node, and the number of edge nodes will reach millions.

Introduction to KubeEdge​

KubeEdge is the industry's first cloud native edge computing framework designed for edge-cloud collaboration. Complementing Kubernetes for container orchestration and scheduling, KubeEdge allows applications, resources, data, and devices to collaborate between edges and the cloud. Devices, edges, and the cloud are now fully connected in edge computing.

In the KubeEdge architecture, the cloud is a unified control plane, which includes native Kubernetes management components and KubeEdge-developed CloudCore components. It listens to cloud resource changes and provides reliable, efficient cloud-edge messaging. At the edge side lie the EdgeCore components, including Edged, MetaManager, and EdgeHub. They receive messages from the cloud and manage the lifecycle of containers. The device mapper and event bus are responsible for device access.

Based on the Kubernetes control plane, KubeEdge allows nodes to be deployed more remotely and thereby extends edge-cloud collaboration. Kubernetes supports 5,000 nodes and 150,000 pods, which are far from enough for edge computing in Internet of Everything (IoE). The access of a large number of edge devices demands a scalable, centralized edge computing platform. To help users cost less and manage more in an easier way, KubeEdge, fully compatible with Kubernetes, optimizes the cloud-edge messaging and provides access support for mass edge nodes.

SLIs/SLOs​

Scalability and performance are important features of Kubernetes clusters. Before performing the large-scale performance test, we need to define the measurement metrics. The Kubernetes community defines the following SLIs (Service Level Indicators) and SLOs (Service Level Objectives) to measure the cluster service quality.

1. API Call Latency

2. Pod Startup Latency

The community also defines indicators such as in-cluster network programming latency (latency for Service updates or changes in ready pods to be reflected to iptables/IPVS rules), in-cluster network latency, DNS programming latency (latency for Service updates or changes in ready pods to be reflected to the DNS server), and DNS latency. These indicators have not yet been quantified. This test was conducted to satisfy all SLIs/SLOs in the official state.

Kubernetes Scalability Dimensions and Thresholds​

Kubernetes scalability does not just mean the number of nodes (Scalability != #Nodes). Other dimensions include the number of namespaces, pods, Services, secrets, and ConfigMaps. Configurations that Kubernetes supports create the Scalability Envelope (which keeps evolving):

Obviously, it is impossible for a Kubernetes cluster to expand resource objects without limitation while satisfying SLIs/SLOs. Therefore, the industry defines the upper limits of Kubernetes resource objects.

1. Pods/node 30
2. Backends <= 50k & Services <= 10k & Backends/service <= 250
3. Pod churn 20/s
4. Secret & configmap/node 30
5. Namespaces <= 10k & Pods <= 150k & Pods/namespace <= 3k
6. ​    …..

Dimensions are sometimes not independent. As you move farther along one dimension, your cross-section wrt other dimensions gets smaller. For example, if 5000 nodes are expanded to 10,000 nodes, the specifications of other dimensions will be affected. A heavy workload is required if all scenarios are tested. In this test, we focus on the typical scenarios. We manage to host 100k edge nodes and 1000k pods in a single cluster while satisfying the SLIs/SLOs.

Test Tools​

ClusterLoader2​

ClusterLoader2 is an open source Kubernetes cluster performance test tool. It can test the Kubernetes SLIs/SLOs to check whether the cluster meets the service quality standards. It also visualizes data for locating cluster problems and optimizing cluster performance. After the test, users get a performance report with detailed test results.

Clusterloader2 performance metrics:

• APIResponsivenessPrometheusSimple
• APIResponsivenessPrometheus
• CPUProfile
• EtcdMetrics
• MemoryProfile
• MetricsForE2E
• PodStartupLatency
• ResourceUsageSummary
• SchedulingMetrics
• SchedulingThroughput
• WaitForControlledPodsRunning
• WaitForRunningPods

Edgemark​

Edgemark is a performance test tool similar to Kubemark. It simulates deploying KubeEdge edge nodes in the KubeEdge cluster scalability test to build ultra-large Kubernetes clusters, powered by KubeEdge, with limited resources. The objective is to expose the cluster control plane downsides that occur only in large-scale deployments. The following figure illustrates the Edgemark deployment:

• K8s master: the master node of the Kubernetes cluster
• Edgemark master: the master node of the simulated Kubernetes cluster
• CloudCore: the KubeEdge cloud management component, which is responsible for edge node access
• hollow pod: a pod started in the actual cluster. It registers with the Edgemark master as a virtual edge node by starting Edgemark in it. The Edgemark master can schedule pods to this virtual edge node.
• hollow edgeNode: a virtual node in the simulated cluster, registered from a hollow pod

Cluster Deployment Scheme for the Test​

The Kubernetes control plane is deployed with one master node. The etcd, kube-apiserver, kube-scheduler, and kube-controller are deployed as single-instance. The KubeEdge control plane is deployed with five CloudCore instances and connects to the kube-apiserver through the IP address of the master node. Hollow EdgeNodes are exposed by a load balancer and randomly connect to a CloudCore instance based on the round-robin policy of the load balancer.

Test Environment Information​

Control Plane OS Version

CentOS 7.9 64bit 3.10.0-1160.15.2.el7.x86_64

Kubernetes Version

Major:"1", Minor:"23", GitVersion:"v1.23.4", GitCommit:"e6c093d87ea4cbb530a7b2ae91e54c0842d8308a", GitTreeState:"clean", BuildDate:"2022-02-16T12:38:05Z", GoVersion:"go1.17.7", Compiler:"gc", Platform:"linux/amd64"

KubeEdge Version

KubeEdge v1.11.0-alpha.0

Master Node Configurations

• CPU

Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                128
On-line CPU(s) list:   0-127
Thread(s) per core:    2
Core(s) per socket:    32
Socket(s):             2
NUMA node(s):          2
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 106
Model name:            Intel(R) Xeon(R) Platinum 8378A CPU @ 3.00GHz
Stepping:              6
CPU MHz:               2999.998

• MEMORY

Total online memory:   256G

• ETCD DISK

Type:   SAS_SSD
Size:   300GB

CloudCore Node Configurations

• CPU

Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                12
On-line CPU(s) list:   0-11
Thread(s) per core:    2
Core(s) per socket:    6
Socket(s):             1
NUMA node(s):          1
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 106
Model name:            Intel(R) Xeon(R) Platinum 8378A CPU @ 3.00GHz
Stepping:              6
CPU MHz:               2999.998

• MEMORY

Total online memory:   48G

Component Parameter Configurations​

1. kube-apiserver

--max-requests-inflight=2000
--max-mutating-requests-inflight=1000

2. kube-controller-manager

--kube-api-qps=100
--kube-api-burst=100

3. kube-scheduler

--kube-api-qps=200
--kube-api-burst=400

4. CloudCore

apiVersion: cloudcore.config.kubeedge.io/v1alpha1
kind: CloudCore
kubeAPIConfig:
  kubeConfig: ""
  master: ""
  qps: 60000
  burst: 80000
modules:
  cloudHub:
    advertiseAddress:
      - xx.xx.xx.xx
    nodeLimit: 30000
    tlsCAFile: /etc/kubeedge/ca/rootCA.crt
    tlsCertFile: /etc/kubeedge/certs/server.crt
    tlsPrivateKeyFile: /etc/kubeedge/certs/server.key
    unixsocket:
      address: unix:///var/lib/kubeedge/kubeedge.sock
      enable: false
    websocket:
      address: 0.0.0.0
      enable: true
      port: 10000
  cloudStream:
    enable: false
  deviceController:
    enable: false
  dynamicController:
    enable: false
  edgeController:
    buffer:
      configMapEvent: 102400
      deletePod: 10240
      endpointsEvent: 1
      podEvent: 102400
      queryConfigMap: 10240
      queryEndpoints: 1
      queryNode: 10240
      queryPersistentVolume: 1
      queryPersistentVolumeClaim: 1
      querySecret: 10240
      queryService: 1
      queryVolumeAttachment: 1
      ruleEndpointsEvent: 1
      rulesEvent: 1
      secretEvent: 1
      serviceEvent: 10240
      updateNode: 15240
      updateNodeStatus: 30000
      updatePodStatus: 102400
    enable: true
    load:
      deletePodWorkers: 5000
      queryConfigMapWorkers: 1000
      queryEndpointsWorkers: 1
      queryNodeWorkers: 5000
      queryPersistentVolumeClaimWorkers: 1
      queryPersistentVolumeWorkers: 1
      querySecretWorkers: 1000
      queryServiceWorkers: 1
      queryVolumeAttachmentWorkers: 1
      updateNodeStatusWorkers: 10000
      updateNodeWorkers: 5000
      updatePodStatusWorkers: 20000
      ServiceAccountTokenWorkers: 10000
    nodeUpdateFrequency: 60
  router:
    enable: false
  syncController:
    enable: true

Density Test​

Test Execution​

Before using ClusterLoader2 to perform the performance test, we defined the test policy using the configuration file. In this test, we used the official Kubernetes density case. The configuration file we used can be obtained here:

https://github.com/kubernetes/perf-tests/blob/master/clusterloader2/testing/density/config.yaml

The following table describes the detailed Kubernetes resource configurations:

For details about the test method and procedure, see the following links:

https://github.com/kubeedge/kubeedge/tree/master/build/edgemark

https://github.com/kubernetes/perf-tests/blob/master/clusterloader2/docs/GETTING_STARTED.md

Test Results​

APIResponsivenessPrometheusSimple：

1. mutating API latency（threshold=1s）：

   

2. Read-only API call latency(scope=resource, threshold=1s)

   

3. Read-only API call latency(scope=namespace, threshold=5s)

   

4. Read-only API call latency(scope=cluster, threshold=30s)

   

PodStartupLatency：

Note: Theoretically, the latency should always be greater than 0. Because kube-apiserver does not support RFC339NANO, the timestamp precision can only be seconds. Therefore, when the latency is low, some values collected by ClusterLoader2 are 0 due to precision loss.

Conclusion and Analysis​

From the preceding test results, the API call latency and pod startup latency meet the SLIs/SLOs defined by the Kubernetes community. Therefore, the KubeEdge-powered Kubernetes clusters can stably support 100,000 concurrent edge nodes and more than one million pods. In production, the network between edge nodes and the cloud is connected according to O&M requirements due to reasons such as network security and partition management. Therefore, the number of edge nodes that can be managed by a single cluster can increase proportionally based on the proportion of offline edge nodes to online ones. In addition, data fragmentation is used on the Kubernetes control plane to store different resources to the corresponding etcd space, which allows for a larger service deployment scale.

KubeEdge's Support for Large-Scale Edge Node Access​

1. Efficient Cloud-Edge Messaging​

List-watch is a unified mechanism for asynchronous messaging of Kubernetes components. The list operation calls the list API of a resource to obtain full resource data through non-persistent HTTP connections. The watch operation calls the watch API of a resource to monitor resource change events and obtain incremental change data through persistent HTTP connections and block-based transmission encoding. In Kubernetes, in addition to the list-watch of a node, pods allocated to the node, and full service metadata, kubelet must also watch (by default) the running pods mounted with secrets and ConfigMaps as data volumes. The number of list-watch operations could explode with increasing nodes and pods, which heavily burdens kube-apiserver.

KubeEdge uses the two-way multiplexing edge-cloud message channel and supports the WebSocket (default) and QUIC protocols. EdgeCore at the edge initiates a connection request to CloudCore on the cloud. CloudCore list-watches Kubernetes resource changes, and delivers metadata to the edge through this two-way channel. EdgeCore uploads the metadata, such as edge node status and application status, to CloudCore through this channel. CloudCore reports the received metadata to kube-apiserver.

CloudCore aggregates the upstream and downstream data. kube-apiserver processes only several list-watch requests from CloudCore. It can be effectively unburdened and the cluster performance gets improved.

Memory usage when the native Kubernetes kube-apiserver is used under the same node and pod scales:

Memory usage when kube-apiserver is used in a KubeEdge-powered Kubernetes cluster:

2. Reliable Incremental Cloud-Edge Data Transmission​

In the case of complex edge network topology or poor networking quality, cloud-edge communication may be compromised by high network latency, intermittent/frequent disconnection, and other issues. When the network recovers and edge nodes want to reconnect to the cloud, a large number of full list requests will be generated, pressuring kube-apiserver. Large-scale deployments may amplify this challenge to system stability. To solve it, KubeEdge records the version of the metadata successfully sent to the edge. When the cloud-edge network is reconnected, the cloud sends incremental metadata starting from the recorded metadata version.

3. Lightweight Edge + Edge-Cloud Messaging Optimization​

EdgeCore removes native kubelet features that are not used in edge deployments, such as in-tree volume and cloud-provider, trims the status information reported by nodes, and optimizes resource usage of edge agent software. EdgeCore can run with a minimum of 70 MB memory on edge devices whose memory is as minimum as 100 MB. The WebSocket channel, edge-cloud message combination, and data trim greatly reduces the communication pressure on the edge and cloud and the access pressure on the control plane. They ensure that the system can work properly even in the case of high latency and jitter.

When 100,000 edge nodes are connected, the number of ELB connections is 100,000.

When 100,000 edge nodes and more than 1,000,000 pods are deployed, the inbound rate of the ELB network is about 3 MB/s, and the average uplink bandwidth to each edge node is about 0.25 kbit/s.

Next Steps​

Targeted tests will be performed on edge devices, edge-cloud messaging, and edge service mesh. In addition, for some edge scenarios, such as large-scale node network disconnection and reconnection, high latency of edge networks, and intermittent disconnection, new SLIs/SLOs need to be introduced to measure the cluster service quality and perform large-scale tests.

As the first cloud-native edge computing community, KubeEdge provides solutions for cloud-edge synergy and has been widely adopted in industries including Transportation, Energy, Internet, CDN, Manufacturing, Smart campus, etc. With the accelerated deployment of KubeEdge in this area based on cloud-edge synergy, the community will improve the security of KubeEdge continuously in cloud-native edge computing scenarios.

The KubeEdge community attaches great importance to security and has set up Sig Security and Security Team to design KubeEdge system security and quickly respond to and handle security vulnerabilities. To conduct a more comprehensive security assessment of the KubeEdge project, the KubeEdge community cooperates with Ada Logics Ltd. and The Open Source Technology Improvement Fund performed a holistic security audit of KubeEdge and output a security auditing report, including the security threat model and security issues related to the KubeEdge project. Thank you to experts Adam Korczynski and David Korczynski of Ada Logics for their professional and comprehensive evaluation of the KubeEdge project, which has important guiding significance for the security protection of the KubeEdge project. Thank you Amir Montazery and Derek Zimmer of OSTIF and Cloud Native Computing Foundation (CNCF) who helped with this engagement.

The discovered security issues have been fixed and patched to the latest three minor release versions (v1.11.1, v1.10.2, v1.9.4) by KubeEdge maintainers according to the kubeedge security policy. Security advisories have been published here.

For more details of the threat model and the mitigations, Please check KubeEdge Threat Model And Security Protection Analysis: https://github.com/kubeedge/community/tree/master/sig-security/sig-security-audit/KubeEdge-threat-model-and-security-protection-analysis.md.

References:​

Audit report: https://github.com/kubeedge/community/tree/master/sig-security/sig-security-audit/KubeEdge-security-audit-2022.pdf

OSTIF Blogpost: https://ostif.org/our-audit-of-kubeedge-is-complete-multiple-security-issues-found-and-fixed

CNCF Blogpost:

KubeEdge Threat Model And Security Protection Analysis: https://github.com/kubeedge/community/tree/master/sig-security/sig-security-audit/KubeEdge-threat-model-and-security-protection-analysis.md

KubeEdge is an open source system extending native containerized application orchestration and device management to hosts at the Edge. It is built upon Kubernetes and provides core infrastructure support for networking, application deployment and metadata synchronization between cloud and edge. It also supports MQTT and allows developers to author custom logic and enable resource constrained device communication at the Edge.

On December 6th, the KubeEdge community is proud to announce the availability of KubeEdge 1.9. This release includes a major upgrade for Custom HTTP Request Routing from Edge to Cloud through ServiceBus for Applications, CloudCore run independently of the Kubernetes Master host and containerized deployment using Helm, EdgeMesh add tls and encryption security, and compiled into rpm package, which includes:

• Custom HTTP Request Routing from Edge to Cloud through ServiceBus for Applications

• CloudCore run independently of the Kubernetes Master host

• EdgeMesh add tls and encryption security

• Enhance the ease of use of EdgeMesh

• Support containerized deployment of CloudCore using Helm

• Support compiled into rpm package and installed on OS such as openEuler using yum package manager

• 40+ bug fixes and enhancements.

Please refer to CHANGELOG v1.9 for a full list of features in this release

Release Highlights​

Support Custom HTTP Request Routing from Edge to Cloud through ServiceBus for Applications​

A HTTP server is added to ServiceBus, to support custom http request routing from edge to cloud for applications. This simplifies the rest api access with http server on the cloud while client is in the edge.

Refer to the links for more details. (#3254, #3301)

Support CloudCore to run independently of the Kubernetes Master host​

CloudCore now supports to run independently of the Kubernetes Master host, iptablesmanager has been added as an independent component, users only need to deploy the iptablesmanager to Kubernetes Master host, which now can add the iptable rules for Cloud-Edge tunnel automatically

Refer to the links for more details. (#3265)

EdgeMesh add tls and encryption security​

EdgeMesh's tunnel module adds tls and encryption security capabilities. These features bring more secure protection measures to the user's edgemesh-server component and reduce the risk of edgemesh-server being attacked.

Refer to the links for more details. (EdgeMesh#127)

Enhanced the ease of use of EdgeMesh​

EdgeMesh has many improvements in ease of use. Now users can easily deploy EdgeMesh's server and agent components with a single command of helm. At the same time, the restriction on service port naming is removed, and the docker0 dependency is removed, making it easier for users to use EdgeMesh.

Refer to the links for more details. (EdgeMesh#123, EdgeMesh#126, EdgeMesh#136, EdgeMesh#175)

Support containerized deployment of CloudCore using Helm​

CloudCore now supports containerized deployment using Helm, which provides better containerized deployment experience.

Refer to the links for more details. (#3265)

Support compiled into rpm package and installed on OS such as openEuler using yum package manager​

KubeEdge now supports compiled into rpm package and installed on OS such as openEuler using yum package manager.

Refer to the links for more details. (#3089, #3171)

In addition to the above new features, KubeEdge v1.9 also includes the following enhancements:

• Rpminstaller: add support for openEuler (#3089)

• Replaced 'kubeedge/pause' with multi arch image (#3114)

• Make meta server addr configurable (#3119)

• Added iptables to Dockerfile and made cloudcore privileged (#3129)

• Added CustomInterfaceEnabled and CustomInterfaceName for edgecore (#3130)

• Add experimental feature (#3131)

• Feat(edge): node ephemeral storage info (#3157)

• Support envFrom configmap in edge pods (#3176)

• Update golang to 1.16 (#3190)

• Metaserver: support shutdown server graceful (#3239)

• Support labelselector for metaserver (#3262)

Future Outlook​

With the release of v1.9, KubeEdge supports custom HTTP request routing from Edge to Cloud through ServiceBus for applications, supports CloudCore running independently of the Kubernetes Master host, supports containerized deployment of CloudCore using Helm, supports tls and encryption security and the ease of use of EdgeMesh. Thanks to Huawei, China Unicom, DaoCloud, Zhejiang University SEL Lab, ARM and other organizations for their contributions, as well as all community contributors for their support!

The community plans to further improve the user experience and the stability of KubeEdge in subsequent versions and create the best “open source” intelligent edge computing platform for everyone to freely use.

For more details regarding KubeEdge, please follow and join us here:

https://kubeedge.io