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crazylion lee

Hammerspoon - 0 views

www.hammerspoon.org

automation lua osx mac

shared by crazylion lee on 24 Nov 16 - No Cached
  • crazylion lee on 24 Nov 16
     
    "This is a tool for powerful automation of OS X. At its core, Hammerspoon is just a bridge between the operating system and a Lua scripting engine. What gives Hammerspoon its power is a set of extensions that expose specific pieces of system functionality, to the user."
crazylion lee

vuvuzela/vuvuzela: Private messaging system that hides metadata - 0 views

github.com/vuvuzela

private messaging system metadata

shared by crazylion lee on 06 Jan 17 - No Cached
  • crazylion lee on 06 Jan 17
     
    "Private messaging system that hides metadata https://vuvuzela.io"
crazylion lee

Evolutionary Database Design - 0 views

martinfowler.com/...evodb.html

database design

shared by crazylion lee on 30 Jan 17 - Cached
  • crazylion lee on 30 Jan 17
     
    "Over the last decade we've developed and refined a number of techniques that allow a database design to evolve as an application develops. This is a very important capability for agile methodologies. The techniques rely on applying continuous integration and automated refactoring to database development, together with a close collaboration between DBAs and application developers. The techniques work in both pre-production and released systems, in green field projects as well as legacy systems."
crazylion lee

Raft Consensus Algorithm - 0 views

raft.github.io

raft algorithm algorithms consensus

shared by crazylion lee on 10 Jun 16 - No Cached
  • crazylion lee on 10 Jun 16
     
    " Raft is a consensus algorithm that is designed to be easy to understand. It's equivalent to Paxos in fault-tolerance and performance. The difference is that it's decomposed into relatively independent subproblems, and it cleanly addresses all major pieces needed for practical systems. We hope Raft will make consensus available to a wider audience, and that this wider audience will be able to develop a variety of higher quality consensus-based systems than are available today."
張 旭

How To Install and Use Docker: Getting Started | DigitalOcean - 0 views

www.digitalocean.com/...and-use-docker-getting-started

docker system deploy

shared by 張 旭 on 26 Oct 14 - No Cached
  • docker as a project offers you the complete set of higher-level tools to carry everything that forms an application across systems and machines - virtual or physical - and brings along loads more of great benefits with it
  • docker daemon: used to manage docker (LXC) containers on the host it runs
  • docker CLI: used to command and communicate with the docker daemon
  • ...20 more annotations...
  • containers: directories containing everything-your-application
  • images: snapshots of containers or base OS (e.g. Ubuntu) images
  • Dockerfiles: scripts automating the building process of images
  • Docker containers are basically directories which can be packed (e.g. tar-archived) like any other, then shared and run across various different machines and platforms (hosts).
  • Linux Containers can be defined as a combination various kernel-level features (i.e. things that Linux-kernel can do) which allow management of applications (and resources they use) contained within their own environment
  • Each container is layered like an onion and each action taken within a container consists of putting another block (which actually translates to a simple change within the file system) on top of the previous one.
  • Each docker container starts from a docker image which forms the base for other applications and layers to come.
  • Docker images constitute the base of docker containers from which everything starts to form
  • a solid, consistent and dependable base with everything that is needed to run the applications
  • As more layers (tools, applications etc.) are added on top of the base, new images can be formed by committing these changes.
  • a Dockerfile for automated image building
  • Dockerfiles are scripts containing a successive series of instructions, directions, and commands which are to be executed to form a new docker image.
  • As you work with a container and continue to perform actions on it (e.g. download and install software, configure files etc.), to have it keep its state, you need to “commit”.
  • Please remember to “commit” all your changes.
  • When you "run" any process using an image, in return, you will have a container.
  • When the process is not actively running, this container will be a non-running container. Nonetheless, all of them will reside on your system until you remove them via rm command.
  • To create a new container, you need to use a base image and specify a command to run.
  • you can not change the command you run after having created a container (hence specifying one during "creation")
  • If you would like to save the progress and changes you made with a container, you can use “commit”
  • turns your container to an image
crazylion lee

ledger, a powerful command-line accounting system - 0 views

www.ledger-cli.org/index.html

accounting system linux cmd

shared by crazylion lee on 08 Nov 15 - No Cached
  • crazylion lee on 08 Nov 15
     
    "Ledger is a powerful, double-entry accounting system that is accessed from the UNIX command-line. Ledger, begun in 2003, is written by John Wiegley and released under the BSD license. It has also inspired several ports to other languages."
crazylion lee

4 system monitoring tools for Linux | Opensource.com - 0 views

opensource.com/...source-tools-system-monitoring

linux monitoring sysadmin top

shared by crazylion lee on 01 Mar 16 - No Cached
  • crazylion lee on 01 Mar 16
     
    "4 open source tools for Linux system monitoring"
crazylion lee

AppImage | Linux apps that run anywhere - 1 views

appimage.org

linux app software appimage

shared by crazylion lee on 20 Mar 16 - No Cached
  • crazylion lee on 20 Mar 16
     
    製作像是window,mac上的執行檔 ""As a user, I want to download an application from the original author, and run it on my Linux desktop system just like I would do with a Windows or Mac application." "As an application author, I want to provide packages for Linux desktop systems, without the need to get it 'into' a distribution and without having to build for gazillions of different distributions.""
張 旭

Introduction - Packer by HashiCorp - 0 views

www.packer.io/index.html

vmware system packer devops virtualization

shared by 張 旭 on 03 Apr 19 - No Cached
  • A machine image is a single static unit that contains a pre-configured operating system and installed software which is used to quickly create new running machines.
  • 張 旭 on 03 Apr 19
     
    "A machine image is a single static unit that contains a pre-configured operating system and installed software which is used to quickly create new running machines."
張 旭

MongoDB Performance - MongoDB Manual - 0 views

docs.mongodb.com/...analyzing-mongodb-performance

database mongodb performance

shared by 張 旭 on 20 Apr 21 - No Cached
  • MongoDB uses a locking system to ensure data set consistency. If certain operations are long-running or a queue forms, performance will degrade as requests and operations wait for the lock.
  • performance limitations as a result of inadequate or inappropriate indexing strategies, or as a consequence of poor schema design patterns.
  • performance issues may be temporary and related to abnormal traffic load.
  • ...9 more annotations...
  • Lock-related slowdowns can be intermittent.
  • If globalLock.currentQueue.total is consistently high, then there is a chance that a large number of requests are waiting for a lock.
  • If globalLock.totalTime is high relative to uptime, the database has existed in a lock state for a significant amount of time.
  • For write-heavy applications, deploy sharding and add one or more shards to a sharded cluster to distribute load among mongod instances.
  • Unless constrained by system-wide limits, the maximum number of incoming connections supported by MongoDB is configured with the maxIncomingConnections setting.
  • When logLevel is set to 0, MongoDB records slow operations to the diagnostic log at a rate determined by slowOpSampleRate.
  • At higher logLevel settings, all operations appear in the diagnostic log regardless of their latency with the following exception
  • Full Time Diagnostic Data Collection (FTDC) mechanism. FTDC data files are compressed, are not human-readable, and inherit the same file access permissions as the MongoDB data files.
  • mongod processes store FTDC data files in a diagnostic.data directory under the instances storage.dbPath.
  • 張 旭 on 20 Apr 21
     
    "MongoDB uses a locking system to ensure data set consistency. If certain operations are long-running or a queue forms, performance will degrade as requests and operations wait for the lock."
張 旭

Controllers | Kubernetes - 0 views

kubernetes.io/...controller

kubernetes system auto

shared by 張 旭 on 14 May 21 - No Cached
  • In robotics and automation, a control loop is a non-terminating loop that regulates the state of a system.
  • controllers are control loops that watch the state of your cluster, then make or request changes where needed
  • Each controller tries to move the current cluster state closer to the desired state.
  • ...12 more annotations...
  • A controller tracks at least one Kubernetes resource type.
  • The controller(s) for that resource are responsible for making the current state come closer to that desired state.
  • in Kubernetes, a controller will send messages to the API server that have useful side effects.
  • Built-in controllers manage state by interacting with the cluster API server.
  • By contrast with Job, some controllers need to make changes to things outside of your cluster.
  • the controller makes some change to bring about your desired state, and then reports current state back to your cluster's API server. Other control loops can observe that reported data and take their own actions.
  • As long as the controllers for your cluster are running and able to make useful changes, it doesn't matter if the overall state is stable or not.
  • Kubernetes uses lots of controllers that each manage a particular aspect of cluster state.
  • a particular control loop (controller) uses one kind of resource as its desired state, and has a different kind of resource that it manages to make that desired state happen.
  • There can be several controllers that create or update the same kind of object.
  • you can have Deployments and Jobs; these both create Pods. The Job controller does not delete the Pods that your Deployment created, because there is information (labels) the controllers can use to tell those Pods apart.
  • Kubernetes comes with a set of built-in controllers that run inside the kube-controller-manager.
  • 張 旭 on 14 May 21
     
    "In robotics and automation, a control loop is a non-terminating loop that regulates the state of a system. "
張 旭

Kubernetes Components | Kubernetes - 0 views

kubernetes.io/...components

kubernetes system

shared by 張 旭 on 17 May 21 - No Cached
  • A Kubernetes cluster consists of a set of worker machines, called nodes, that run containerized applications
  • Every cluster has at least one worker node.
  • The control plane manages the worker nodes and the Pods in the cluster.
  • ...29 more annotations...
  • The control plane's components make global decisions about the cluster
  • Control plane components can be run on any machine in the cluster.
  • for simplicity, set up scripts typically start all control plane components on the same machine, and do not run user containers on this machine
  • The API server is the front end for the Kubernetes control plane.
  • kube-apiserver is designed to scale horizontally—that is, it scales by deploying more instances. You can run several instances of kube-apiserver and balance traffic between those instances.
  • Kubernetes cluster uses etcd as its backing store, make sure you have a back up plan for those data.
  • watches for newly created Pods with no assigned node, and selects a node for them to run on.
  • Factors taken into account for scheduling decisions include: individual and collective resource requirements, hardware/software/policy constraints, affinity and anti-affinity specifications, data locality, inter-workload interference, and deadlines.
  • each controller is a separate process, but to reduce complexity, they are all compiled into a single binary and run in a single process.
  • Node controller
  • Job controller
  • Endpoints controller
  • Service Account & Token controllers
  • The cloud controller manager lets you link your cluster into your cloud provider's API, and separates out the components that interact with that cloud platform from components that only interact with your cluster.
  • If you are running Kubernetes on your own premises, or in a learning environment inside your own PC, the cluster does not have a cloud controller manager.
  • An agent that runs on each node in the cluster. It makes sure that containers are running in a Pod.
  • The kubelet takes a set of PodSpecs that are provided through various mechanisms and ensures that the containers described in those PodSpecs are running and healthy.
  • The kubelet doesn't manage containers which were not created by Kubernetes.
  • kube-proxy is a network proxy that runs on each node in your cluster, implementing part of the Kubernetes Service concept.
  • kube-proxy maintains network rules on nodes. These network rules allow network communication to your Pods from network sessions inside or outside of your cluster.
  • kube-proxy uses the operating system packet filtering layer if there is one and it's available.
  • Kubernetes supports several container runtimes: Docker, containerd, CRI-O, and any implementation of the Kubernetes CRI (Container Runtime Interface).
  • Addons use Kubernetes resources (DaemonSet, Deployment, etc) to implement cluster features
  • namespaced resources for addons belong within the kube-system namespace.
  • all Kubernetes clusters should have cluster DNS,
  • Cluster DNS is a DNS server, in addition to the other DNS server(s) in your environment, which serves DNS records for Kubernetes services.
  • Containers started by Kubernetes automatically include this DNS server in their DNS searches.
  • Container Resource Monitoring records generic time-series metrics about containers in a central database, and provides a UI for browsing that data.
  • A cluster-level logging mechanism is responsible for saving container logs to a central log store with search/browsing interface.
張 旭

鳥哥的 Linux 私房菜 -- 第零章、計算機概論 - 0 views

linux.vbird.org/...0105computers.php

system

shared by 張 旭 on 22 Jun 21 - No Cached
  • 但因為 CPU 的運算速度比其他的設備都要來的快，又為了要滿足 FSB 的頻率，因此廠商就在 CPU 內部再進行加速， 於是就有所謂的外頻與倍頻了。
  • 中央處理器 (Central Processing Unit, CPU)，CPU 為一個具有特定功能的晶片， 裡頭含有微指令集，如果你想要讓主機進行什麼特異的功能，就得要參考這顆 CPU 是否有相關內建的微指令集才可以。
  • CPU 內又可分為兩個主要的單元，分別是： 算數邏輯單元與控制單元。
  • ...63 more annotations...
  • CPU 讀取的資料都是從主記憶體來的！ 主記憶體內的資料則是從輸入單元所傳輸進來！而 CPU 處理完畢的資料也必須要先寫回主記憶體中，最後資料才從主記憶體傳輸到輸出單元。
  • 重點在於 CPU 與主記憶體。 特別要看的是實線部分的傳輸方向，基本上資料都是流經過主記憶體再轉出去的！
  • CPU 實際要處理的資料則完全來自於主記憶體 (不管是程式還是一般文件資料)！這是個很重要的概念喔！ 這也是為什麼當你的記憶體不足時，系統的效能就很糟糕！
  • 常見到的兩種主要 CPU 架構， 分別是：精簡指令集 (RISC) 與複雜指令集 (CISC) 系統。
  • 微指令集較為精簡，每個指令的執行時間都很短，完成的動作也很單純，指令的執行效能較佳； 但是若要做複雜的事情，就要由多個指令來完成。
  • CISC在微指令集的每個小指令可以執行一些較低階的硬體操作，指令數目多而且複雜， 每條指令的長度並不相同。因為指令執行較為複雜所以每條指令花費的時間較長， 但每條個別指令可以處理的工作較為豐富。
  • 多媒體微指令集：MMX, SSE, SSE2, SSE3, SSE4, AMD-3DNow! 虛擬化微指令集：Intel-VT, AMD-SVM 省電功能：Intel-SpeedStep, AMD-PowerNow! 64/32位元相容技術：AMD-AMD64, Intel-EM64T
  • 若光以效能來說，目前的個人電腦效能已經夠快了，甚至已經比工作站等級以上的電腦運算速度還要快！ 但是工作站電腦強調的是穩定不當機，並且運算過程要完全正確，因此工作站以上等級的電腦在設計時的考量與個人電腦並不相同啦
  • 1 Byte = 8 bits
  • 檔案容量使用的是二進位的方式，所以 1 GBytes 的檔案大小實際上為：1024x1024x1024 Bytes 這麼大！ 速度單位則常使用十進位，例如 1GHz 就是 1000x1000x1000 Hz 的意思。
  • CPU的運算速度常使用 MHz 或者是 GHz 之類的單位，這個 Hz 其實就是秒分之一
  • 在網路傳輸方面，由於網路使用的是 bit 為單位，因此網路常使用的單位為 Mbps 是 Mbits per second，亦即是每秒多少 Mbit
  • (1)北橋：負責連結速度較快的CPU、主記憶體與顯示卡界面等元件
  • (2)南橋：負責連接速度較慢的裝置介面， 包括硬碟、USB、網路卡等等
  • CPU內部含有微指令集，不同的微指令集會導致CPU工作效率的優劣
  • 時脈就是CPU每秒鐘可以進行的工作次數。 所以時脈越高表示這顆CPU單位時間內可以作更多的事情。
  • 早期的 CPU 架構主要透過北橋來連結系統最重要的 CPU、主記憶體與顯示卡裝置。因為所有的設備都得掉透過北橋來連結，因此每個設備的工作頻率應該要相同。
  • 前端匯流排 (FSB)
  • 外頻指的是CPU與外部元件進行資料傳輸時的速度
  • 倍頻則是 CPU 內部用來加速工作效能的一個倍數
  • 新的 CPU 設計中， 已經將記憶體控制器整合到 CPU 內部，而連結 CPU 與記憶體、顯示卡的控制器的設計，在Intel部份使用 QPI (Quick Path Interconnect) 與 DMI 技術，而 AMD 部份則使用 Hyper Transport 了，這些技術都可以讓 CPU 直接與主記憶體、顯示卡等設備分別進行溝通，而不需要透過外部的連結晶片了。
  • 如何知道主記憶體能提供的資料量呢？此時還是得要藉由 CPU 內的記憶體控制晶片與主記憶體間的傳輸速度『前端匯流排速度(Front Side Bus, FSB)
  • 主記憶體也是有其工作的時脈，這個時脈限制還是來自於 CPU 內的記憶體控制器所決定的。
  • CPU每次能夠處理的資料量稱為字組大小(word size)， 字組大小依據CPU的設計而有32位元與64位元。我們現在所稱的電腦是32或64位元主要是依據這個 CPU解析的字組大小而來的
  • 早期的32位元CPU中，因為CPU每次能夠解析的資料量有限， 因此由主記憶體傳來的資料量就有所限制了。這也導致32位元的CPU最多只能支援最大到4GBytes的記憶體。
  • 在每一個 CPU 內部將重要的暫存器 (register) 分成兩群， 而讓程序分別使用這兩群暫存器。
  • 可以有兩個程序『同時競爭 CPU 的運算單元』，而非透過作業系統的多工切換！
  • 大多發現 HT 雖然可以提昇效能，不過，有些情況下卻可能導致效能降低喔！因為，實際上明明就僅有一個運算單元
  • 個人電腦的主記憶體主要元件為動態隨機存取記憶體(Dynamic Random Access Memory, DRAM)， 隨機存取記憶體只有在通電時才能記錄與使用，斷電後資料就消失了。因此我們也稱這種RAM為揮發性記憶體。
  • 要啟用雙通道的功能你必須要安插兩支(或四支)主記憶體，這兩支記憶體最好連型號都一模一樣比較好， 這是因為啟動雙通道記憶體功能時，資料是同步寫入/讀出這一對主記憶體中，如此才能夠提升整體的頻寬啊！
  • 第二層快取(L2 cache)整合到CPU內部，因此這個L2記憶體的速度必須要CPU時脈相同。 使用DRAM是無法達到這個時脈速度的，此時就需要靜態隨機存取記憶體(Static Random Access Memory, SRAM)的幫忙了。
  • BIOS(Basic Input Output System)是一套程式，這套程式是寫死到主機板上面的一個記憶體晶片中， 這個記憶體晶片在沒有通電時也能夠將資料記錄下來，那就是唯讀記憶體(Read Only Memory, ROM)。
  • BIOS對於個人電腦來說是非常重要的， 因為他是系統在開機的時候首先會去讀取的一個小程式
  • 由於磁碟盤是圓的，並且透過機器手臂去讀寫資料，磁碟盤要轉動才能夠讓機器手臂讀寫。因此，通常資料寫入當然就是以圓圈轉圈的方式讀寫囉！ 所以，當初設計就是在類似磁碟盤同心圓上面切出一個一個的小區塊，這些小區塊整合成一個圓形，讓機器手臂上的讀寫頭去存取。 這個小區塊就是磁碟的最小物理儲存單位，稱之為磁區 (sector)，那同一個同心圓的磁區組合成的圓就是所謂的磁軌(track)。 由於磁碟裡面可能會有多個磁碟盤，因此在所有磁碟盤上面的同一個磁軌可以組合成所謂的磁柱 (cylinder)。
  • 原本硬碟的磁區都是設計成 512byte 的容量，但因為近期以來硬碟的容量越來越大，為了減少資料量的拆解，所以新的高容量硬碟已經有 4Kbyte 的磁區設計
  • 拿快閃記憶體去製作成高容量的設備，這些設備的連接界面也是透過 SATA 或 SAS，而且外型還做的跟傳統磁碟一樣
  • 固態硬碟最大的好處是，它沒有馬達不需要轉動，而是透過記憶體直接讀寫的特性，因此除了沒資料延遲且快速之外，還很省電
  • 硬碟主要是利用主軸馬達轉動磁碟盤來存取，因此轉速的快慢會影響到效能
  • 使用作業系統的正常關機方式，才能夠有比較好的硬碟保養啊！因為他會讓硬碟的機械手臂歸回原位啊！
  • I/O位址有點類似每個裝置的門牌號碼，每個裝置都有他自己的位址，一般來說，不能有兩個裝置使用同一個I/O位址， 否則系統就會不曉得該如何運作這兩個裝置了。
  • IRQ就可以想成是各個門牌連接到郵件中心(CPU)的專門路徑囉！ 各裝置可以透過IRQ中斷通道來告知CPU該裝置的工作情況，以方便CPU進行工作分配的任務。
  • BIOS為寫入到主機板上某一塊 flash 或 EEPROM 的程式，他可以在開機的時候執行，以載入CMOS當中的參數， 並嘗試呼叫儲存裝置中的開機程式，進一步進入作業系統當中。
  • 電腦都只有記錄0/1而已，甚至記錄的資料都是使用byte/bit等單位來記錄的
  • 常用的英文編碼表為ASCII系統，這個編碼系統中， 每個符號(英文、數字或符號等)都會佔用1bytes的記錄， 因此總共會有28=256種變化
  • 中文字當中的編碼系統早期最常用的就是big5這個編碼表了。 每個中文字會佔用2bytes，理論上最多可以有216=65536，亦即最多可達6萬多個中文字。
  • 國際組織ISO/IEC跳出來制訂了所謂的Unicode編碼系統， 我們常常稱呼的UTF8或萬國碼的編碼
  • CPU其實是具有微指令集的。因此，我們需要CPU幫忙工作時，就得要參考微指令集的內容， 然後撰寫讓CPU讀的懂的指令碼給CPU執行，這樣就能夠讓CPU運作了。
  • 編譯器』來將這些人類能夠寫的程式語言轉譯成為機器能看懂得機器碼
  • 當你需要將運作的資料寫入記憶體中，你就得要自行分配一個記憶體區塊出來讓自己的資料能夠填上去， 所以你還得要瞭解到記憶體的位址是如何定位的，啊！眼淚還是不知不覺的流了下來... 怎麼寫程式這麼麻煩啊！
  • 作業系統(Operating System, OS)其實也是一組程式， 這組程式的重點在於管理電腦的所有活動以及驅動系統中的所有硬體。
  • 作業系統的功能就是讓CPU可以開始判斷邏輯與運算數值、 讓主記憶體可以開始載入/讀出資料與程式碼、讓硬碟可以開始被存取、讓網路卡可以開始傳輸資料、 讓所有周邊可以開始運轉等等。
  • 只有核心有提供的功能，你的電腦系統才能幫你完成！舉例來說，你的核心並不支援TCP/IP的網路協定， 那麼無論你購買了什麼樣的網卡，這個核心都無法提供網路能力的！
  • 核心程式所放置到記憶體當中的區塊是受保護的！ 並且開機後就一直常駐在記憶體當中。
  • 作業系統通常會提供一整組的開發介面給工程師來開發軟體！ 工程師只要遵守該開發介面那就很容易開發軟體了！
  • 系統呼叫介面(System call interface)
  • 程序管理(Process control)
  • 記憶體管理(Memory management)
  • 檔案系統管理(Filesystem management)
  • 通常核心會提供虛擬記憶體的功能，當記憶體不足時可以提供記憶體置換(swap)的功能
  • 裝置的驅動(Device drivers)
  • 『可載入模組』功能，可以將驅動程式編輯成模組，就不需要重新的編譯核心
  • 驅動程式可以說是作業系統裡面相當重要的一環
  • 作業系統通常會提供一個開發介面給硬體開發商， 讓他們可以根據這個介面設計可以驅動他們硬體的『驅動程式』，如此一來，只要使用者安裝驅動程式後， 自然就可以在他們的作業系統上面驅動這塊顯示卡了。
  • 張 旭 on 22 Jun 21
     
    "但因為 CPU 的運算速度比其他的設備都要來的快，又為了要滿足 FSB 的頻率，因此廠商就在 CPU 內部再進行加速， 於是就有所謂的外頻與倍頻了。"
張 旭

Container Runtimes | Kubernetes - 0 views

kubernetes.io/...container-runtimes

kubernetes container docker devops

shared by 張 旭 on 25 Jan 23 - No Cached
  • Kubernetes releases before v1.24 included a direct integration with Docker Engine, using a component named dockershim. That special direct integration is no longer part of Kubernetes
  • You need to install a container runtime into each node in the cluster so that Pods can run there.
  • Kubernetes 1.26 requires that you use a runtime that conforms with the Container Runtime Interface (CRI).
  • ...9 more annotations...
  • On Linux, control groups are used to constrain resources that are allocated to processes.
  • Both kubelet and the underlying container runtime need to interface with control groups to enforce resource management for pods and containers and set resources such as cpu/memory requests and limits.
  • When the cgroupfs driver is used, the kubelet and the container runtime directly interface with the cgroup filesystem to configure cgroups.
  • The cgroupfs driver is not recommended when systemd is the init system
  • When systemd is chosen as the init system for a Linux distribution, the init process generates and consumes a root control group (cgroup) and acts as a cgroup manager.
  • Two cgroup managers result in two views of the available and in-use resources in the system.
  • Changing the cgroup driver of a Node that has joined a cluster is a sensitive operation. If the kubelet has created Pods using the semantics of one cgroup driver, changing the container runtime to another cgroup driver can cause errors when trying to re-create the Pod sandbox for such existing Pods. Restarting the kubelet may not solve such errors.
  • The approach to mitigate this instability is to use systemd as the cgroup driver for the kubelet and the container runtime when systemd is the selected init system.
  • Kubernetes 1.26 defaults to using v1 of the CRI API. If a container runtime does not support the v1 API, the kubelet falls back to using the (deprecated) v1alpha2 API instead.
張 旭

Cluster Networking - Kubernetes - 0 views

kubernetes.io/...networking

kubernetes networking network system

shared by 張 旭 on 08 Jul 19 - No Cached
  • Networking is a central part of Kubernetes, but it can be challenging to understand exactly how it is expected to work
  • Highly-coupled container-to-container communications
  • Pod-to-Pod communications
  • ...57 more annotations...
  • this is the primary focus of this document
    • 張 旭
      張 旭 on 27 Aug 21
       
      Cluster Networking 所關注處理的是： Pod 到 Pod 之間的連線
  • Pod-to-Service communications
  • External-to-Service communications
  • Kubernetes is all about sharing machines between applications.
  • sharing machines requires ensuring that two applications do not try to use the same ports.
  • Dynamic port allocation brings a lot of complications to the system
  • Every Pod gets its own IP address
  • do not need to explicitly create links between Pods
  • almost never need to deal with mapping container ports to host ports.
  • Pods can be treated much like VMs or physical hosts from the perspectives of port allocation, naming, service discovery, load balancing, application configuration, and migration.
  • pods on a node can communicate with all pods on all nodes without NAT
  • agents on a node (e.g. system daemons, kubelet) can communicate with all pods on that node
  • pods in the host network of a node can communicate with all pods on all nodes without NAT
  • If your job previously ran in a VM, your VM had an IP and could talk to other VMs in your project. This is the same basic model.
  • containers within a Pod share their network namespaces - including their IP address
  • containers within a Pod can all reach each other’s ports on localhost
  • containers within a Pod must coordinate port usage
  • “IP-per-pod” model.
  • request ports on the Node itself which forward to your Pod (called host ports), but this is a very niche operation
  • The Pod itself is blind to the existence or non-existence of host ports.
  • AOS is an Intent-Based Networking system that creates and manages complex datacenter environments from a simple integrated platform.
  • Cisco Application Centric Infrastructure offers an integrated overlay and underlay SDN solution that supports containers, virtual machines, and bare metal servers.
  • AOS Reference Design currently supports Layer-3 connected hosts that eliminate legacy Layer-2 switching problems.
  • The AWS VPC CNI offers integrated AWS Virtual Private Cloud (VPC) networking for Kubernetes clusters.
  • users can apply existing AWS VPC networking and security best practices for building Kubernetes clusters.
  • Using this CNI plugin allows Kubernetes pods to have the same IP address inside the pod as they do on the VPC network.
  • The CNI allocates AWS Elastic Networking Interfaces (ENIs) to each Kubernetes node and using the secondary IP range from each ENI for pods on the node.
  • Big Cloud Fabric is a cloud native networking architecture, designed to run Kubernetes in private cloud/on-premises environments.
  • Cilium is L7/HTTP aware and can enforce network policies on L3-L7 using an identity based security model that is decoupled from network addressing.
  • CNI-Genie is a CNI plugin that enables Kubernetes to simultaneously have access to different implementations of the Kubernetes network model in runtime.
  • CNI-Genie also supports assigning multiple IP addresses to a pod, each from a different CNI plugin.
  • cni-ipvlan-vpc-k8s contains a set of CNI and IPAM plugins to provide a simple, host-local, low latency, high throughput, and compliant networking stack for Kubernetes within Amazon Virtual Private Cloud (VPC) environments by making use of Amazon Elastic Network Interfaces (ENI) and binding AWS-managed IPs into Pods using the Linux kernel’s IPvlan driver in L2 mode.
  • to be straightforward to configure and deploy within a VPC
  • Contiv provides configurable networking
  • Contrail, based on Tungsten Fabric, is a truly open, multi-cloud network virtualization and policy management platform.
  • DANM is a networking solution for telco workloads running in a Kubernetes cluster.
  • Flannel is a very simple overlay network that satisfies the Kubernetes requirements.
  • Any traffic bound for that subnet will be routed directly to the VM by the GCE network fabric.
  • sysctl net.ipv4.ip_forward=1
  • Jaguar provides overlay network using vxlan and Jaguar CNIPlugin provides one IP address per pod.
  • Knitter is a network solution which supports multiple networking in Kubernetes.
  • Kube-OVN is an OVN-based kubernetes network fabric for enterprises.
  • Kube-router provides a Linux LVS/IPVS-based service proxy, a Linux kernel forwarding-based pod-to-pod networking solution with no overlays, and iptables/ipset-based network policy enforcer.
  • If you have a “dumb” L2 network, such as a simple switch in a “bare-metal” environment, you should be able to do something similar to the above GCE setup.
  • Multus is a Multi CNI plugin to support the Multi Networking feature in Kubernetes using CRD based network objects in Kubernetes.
  • NSX-T can provide network virtualization for a multi-cloud and multi-hypervisor environment and is focused on emerging application frameworks and architectures that have heterogeneous endpoints and technology stacks.
  • NSX-T Container Plug-in (NCP) provides integration between NSX-T and container orchestrators such as Kubernetes
  • Nuage uses the open source Open vSwitch for the data plane along with a feature rich SDN Controller built on open standards.
  • OpenVSwitch is a somewhat more mature but also complicated way to build an overlay network
  • OVN is an opensource network virtualization solution developed by the Open vSwitch community.
  • Project Calico is an open source container networking provider and network policy engine.
  • Calico provides a highly scalable networking and network policy solution for connecting Kubernetes pods based on the same IP networking principles as the internet
  • Calico can be deployed without encapsulation or overlays to provide high-performance, high-scale data center networking.
  • Calico can also be run in policy enforcement mode in conjunction with other networking solutions such as Flannel, aka canal, or native GCE, AWS or Azure networking.
  • Romana is an open source network and security automation solution that lets you deploy Kubernetes without an overlay network
  • Weave Net runs as a CNI plug-in or stand-alone. In either version, it doesn’t require any configuration or extra code to run, and in both cases, the network provides one IP address per pod - as is standard for Kubernetes.
  • The network model is implemented by the container runtime on each node.
crazylion lee

minoca/os: Minoca operating system - 1 views

github.com/os

os opensource

shared by crazylion lee on 15 Nov 16 - No Cached
  • crazylion lee on 15 Nov 16
     
    "Minoca operating system"
crazylion lee

CHVote : a public system, Swiss and open source - 0 views

republique-et-canton-de-geneve.github.io/...index-en.html

open source voting vote

shared by crazylion lee on 15 Dec 16 - No Cached
  • crazylion lee on 15 Dec 16
     
    "E-voting system - CHVote"
crazylion lee

jamis/castaway: System for building screencasts and video presentations - 0 views

github.com/castaway

screencapture screencast

shared by crazylion lee on 30 Jan 17 - No Cached
  • crazylion lee on 30 Jan 17
     
    "System for building screencasts and video presentations"
crazylion lee

Open Whisper Systems - 0 views

github.com/whispersystems

framework p2p security

shared by crazylion lee on 11 Jul 16 - No Cached
  • crazylion lee on 11 Jul 16
     
    "Open Whisper Systems "
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