Gardener implements the automated management and operation of Kubernetes clusters as a service and provides a fully validated extensibility framework that can be adjusted to any programmatic cloud or infrastructure provider.
Gardener is 100% Kubernets-native and exposes its own Cluster API to create homogeneous clusters on all supported infrastructures. This API differs from SIG Cluster Lifecycle's Cluster API that only harmonizes how to get to clusters, while Gardener's Cluster API goes one step further and also harmonizes the make-up of the clusters themselves. That means, Gardener gives you homogeneous clusters with exactly the same bill of material, configuration and behavior on all supported infrastructures, which you can see further down below in the section on our K8s Conformance Test Coverage.
In 2020, SIG Cluster Lifecycle's Cluster API made a huge step forward with v1alpha3
and the newly added support for declarative control plane management. This made it possible to integrate managed services like GKE or Gardener. We would be more than happy, if the community would be interested, to contribute a Gardener control plane provider. For more information on the relation between Gardener API and SIG Cluster Lifecycle's Cluster API, please see here.
Gardener's main principle is to leverage Kubernetes concepts for all of its tasks.
In essence, Gardener is an extension API server that comes along with a bundle of custom controllers. It introduces new API objects in an existing Kubernetes cluster (which is called garden cluster) in order to use them for the management of end-user Kubernetes clusters (which are called shoot clusters). These shoot clusters are described via declarative cluster specifications which are observed by the controllers. They will bring up the clusters, reconcile their state, perform automated updates and make sure they are always up and running.
To accomplish these tasks reliably and to offer a high quality of service, Gardener controls the main components of a Kubernetes cluster (etcd, API server, controller manager, scheduler). These so-called control plane components are hosted in Kubernetes clusters themselves (which are called seed clusters). This is the main difference compared to many other OSS cluster provisioning tools: The shoot clusters do not have dedicated master VMs. Instead, the control plane is deployed as a native Kubernetes workload into the seeds (the architecture is commonly referred to as kubeception or inception design). This does not only effectively reduce the total cost of ownership but also allows easier implementations for "day-2 operations" (like cluster updates or robustness) by relying on all the mature Kubernetes features and capabilities.
Gardener reuses the identical Kubernetes design to span a scalable multi-cloud and multi-cluster landscape. Such familiarity with known concepts has proven to quickly ease the initial learning curve and accelerate developer productivity:
Please find more information regarding the concepts and a detailed description of the architecture in our Gardener Wiki and our blog posts on kubernetes.io: Gardener - the Kubernetes Botanist (17.5.2018) and Gardener Project Update (2.12.2019).
Gardener takes part in the Certified Kubernetes Conformance Program to attest its compatibility with the K8s conformance testsuite. Currently Gardener is certified for K8s versions up to v1.20, see the conformance spreadsheet.
Continuous conformance test results of the latest stable Gardener release are uploaded regularly to the CNCF test grid:
[1] Conformance tests are still executed and validated, unfortunately no longer shown in TestGrid.
Besides the conformance tests, over 400 additional e2e tests are executed on a daily basis. Get an overview of the test results at testgrid.
See our documentation in the /docs
repository, please find the index here.
The quickest way to test drive Gardener is to install it virtually onto an existing Kubernetes cluster, just like you would install any other Kubernetes-ready application. You can do this with our Gardener Helm Chart.
Alternatively you can use our garden setup project to create a fully configured Gardener landscape which also includes our Gardener Dashboard.
Feedback and contributions are always welcome!
All channels for getting in touch or learning about our project are listed under the community section. We are cordially inviting interested parties to join our bi-weekly meetings.
Please report bugs or suggestions about our Kubernetes clusters as such or the Gardener itself as GitHub issues or join our Slack channel #gardener (please invite yourself to the Kubernetes workspace here).
Please find further resources about our project here:
重要声明:为防止爬虫和盗版贩卖,文章中的核心代码和数据集可凭【CSDN订阅截图或公z号付费截图】私信免费领取,一律不认其他渠道付费截图! 上一篇主要对Gardener定时算法进行了MATLAB仿真,其中插值控制字 W ( n ) W(n) W(n)作为环路滤波器的输出,采用迭代的方式进行更新,这也是《数字调制解调技术的MATLAB与FPGA实现》书中代码所采用的的方法! 对于这一点,此次采用另一种
original link - http://codeforces.com/problemset/problem/1220/F 题意: 对出一个序列,你可以对其循环左移,问循环左移几次后,这个序列构造出的笛卡尔树层数最少。 解析: 我们可以先拎起最小的点,那么左边 x x x,右边 n − 1 − x n-1-x n−1−x。一边的长度为 i i i的树可以从长度为 i − 1 i-1 i−1的树
引言 重要声明:为防止爬虫和盗版贩卖,文章中的核心代码和数据集可凭【CSDN订阅截图或公z号付费截图】私信免费领取,一律不认其他渠道付费截图! 在前面的博客中 【定时同步系列8】QPSK基带调制+Gardener定时误差检测+解调误码率曲线之MATLAB并行仿真姊妹篇一 【定时同步系列9】QPSK基带调制+Gardener定时误差检测+解调误码率曲线之MATLAB并行仿真姊妹篇二 主要以QPSK调
给出一个数组, vali..n 求一个该数组排列, ord1..n ,满足 ∀i∈[1,n),valordi≤valordi+1 ,并且使得 ∑i=1n−1abs(ordi−ordi+1) 最小 首先很容易的就能想因为要满足排列之后数非降,我们能调整的只有 val 相同的位置的顺序 然后一个能想到的想法是对输入的数进行离散化,然后建立 m(m是离散化之后的数的数量) 层的图,每一层的点满足 val
前言 传送门 : 一开始想到了拓扑排序 但是拓扑是对一个点,所以还是想着中心根深度继续走 思路 我们对于每一个叶子节点 对其连接的节点的度数 − 1 -1 −1 进行拓扑操作 然后我们在操作的时候 同时记录一下,这是第几轮被删除的节点 最后对节点进行一遍 前缀和 输出即可 CODE #include <bits/stdc++.h> using namespace std; #define ned
2072. Kirill the Gardener 3 Time limit: 2.0 second Memory limit: 64 MB Kirill the gardener has got a new task. He has to water the flowers growing on the huge flowerbed! It should be mentioned that th
2072. Kirill the Gardener 3 Time limit: 2.0 second Memory limit: 64 MB Kirill the gardener has got a new task. He has to water the flowers growing on the huge flowerbed! It should be mentioned that th
题意:有n朵花,要求按照权值递增的顺序浇水,浇水一次话一个时间,走一个单位长度花费一个单位时间。 一共n朵花,那么可以先计算出走路用的时间,然后加上n就是总时间,对于权值相同的所有花,就可以只考虑两个端点,这样中间的一定会路过,先走左端点还是先走右端点是不确定的,可以先按照权值大小进行排序,然后按照权值递增处理成多个不同的区间,之后用动态规划处理,dp[i][0]表示走到第i个区间左端点再走右端点