Kubernetes的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

Programming Google Cloud: Building Cloud Native Applications with Gcp

為了解決Kubernetes的問題，作者Costa, Rui 這樣論述：

Companies looking to move enterprise applications to the cloud are busy weighing several options, such as the use of containers, machine learning, and serverless computing. There’s a better way. Instead of helping you fit your use case to individual technologies, this practical guide explains how

to use these technologies to fit your use case. Author Rui Costa, a learning consultant with Google, demonstrates this approach by showing you how to run your application on Google Cloud. Each chapter is dedicated to an area of technology that you need to address when planning and deploying your a

pplication. This book starts by presenting a detailed fictional use case, followed by chapters that focus on the building blocks necessary to deploy a secure enterprise application successfully. Build serverless applications with Google Cloud Functions Explore use cases for deploying a real-time mes

saging service Deploy applications to Google Kubernetes Engine (GKE) Build multiregional GKE clusters Integrate continuous integration and continuous delivery with your application Incorporate Google Cloud APIs, including speech-to-text and data loss prevention Enrich data with Google Cloud Dataflow

Secure your application with Google Cloud Identity-Aware Proxy Explore BigQuery and visualization with Looker and BigQuery SDKs

Kubernetes進入發燒排行的影片

使用 Azure 實現預知保養系統架構-以 TFT-LCD 廠為 例

為了解決Kubernetes的問題，作者張翔淨 這樣論述：

以往設備維護的方式是設備壞了才修，以此降低維護成本，又或是計畫性維修，維修人員依照過往經驗，到了機器運行的一定次數或是時間來定期更換，但這樣的方式無法考量到環境及不同元件造成的差異，仍會造成設備損壞，而非預期的停機，讓不管是產能還是維修等費用都大大的損失。工業 4.0 的興起帶起全球邁向智慧製造，製造業結合物聯網、大數據及 AI 等技術，讓現在設備維護的工作可以透過收集機器的電流、溫度及其他機台參數資訊，進一步進行數據分析來做到機台的預知保養，提早進行機台保養、維修，避免非預期的停機，影響產線運行。本論文將以 TFT-LCD 面板零組件製造業作為實驗場域，實作透過 Azure 雲端服務平台來

建置 TFT-LCD 機台預知保養系統，透過皮爾森相關性等分析，找到適合本實驗場域使用的參數，利用 PySpark 提高資料處理的速度，並利用分區方式優化資料表，Operator Cost、I/O Cost 和 CPU Cost 分別提升了 98.77%、98.78% 和 98.74%，且在面對不同機台數據會有差異的情況下，每一個機台建置一個隨機森林模型來進行數據的分析，模型準確率為 0.99，且將模型部屬至 Azure Kubernetes 來進行即時的評分，最後也將數據以及模型分析結果視覺化，讓工廠的維修人員能夠透過數據以及分析結果來調整製程參數、提早了解機台健康狀況，達到預知保養的工作。

Grokking Continuous Delivery

為了解決Kubernetes的問題，作者Wilson, Christie 這樣論述：

Build and use systems that safely automate software delivery from testing through release with this jargon-busting guide to Continuous Delivery pipelines.Grokking Continuous Delivery is a practical guide to implementing and using continuous delivery in your software projects. It’s full of tool-agnos

tic best practices that you can apply to any software project, from libraries to large service applications. You’ll get a complete overview of all the pieces of a CD pipeline and learn how to fit them together for both new and legacy codebases. Grokking Continuous Delivery teaches you the design and

purpose of continuous delivery systems that you can use with any language or stack. You’ll learn directly from your mentor Christie Wilson, Google engineer and co-creator of the Tekton CI/CD framework. Using crystal-clear, well-illustrated examples, Christie lays out the practical nuts and bolts of

continuous delivery for developers and pipeline designers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Christie Wilson is a software engineer at Google, with over a decade of experience dealing with complex deployment environments a

nd high-criticality systems. She is a frequent speaker on CI/CD at conferences including KubeCon, OSCON, QCon, and PyCon. At Google, she built internal productivity tooling for AppEngine, bootstrapped Knative, and created Tekton, a cloud-native CI/CD platform built on Kubernetes.

超規模分佈式雲端數據中心之 NFV 平行流量感知部署演算法

為了解決Kubernetes的問題，作者陳萱庭 這樣論述：

Cloud services are burgeoning, the next distributed computing era and the next generation of hyperscale data centers are subverting the past. With the rise of Cloud Computing, Artificial Intelligence, and the Internet of Things, data centers have ushered in the third wave of upsurge. Since Network

Functions Virtualization (NFV) was put forward by ETSI, NFV development has been highly concerned. Recent methods are becoming obsolete for dealing with the lateral flow in DCN, and attentions to lateral flow to date are also scant. In this research, we devise an algorithm, VIV3A, for hyperscale dis

tributed cloud data centers. The novelty of our work lies not only in considering the new paradigm of lateral flow sensing on real topologies but also in demonstrating the hardness of NFVSED optimization by proof.