股票這個價位要不要買論文書籍站
Serverless的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列股價、配息、目標價等股票新聞資訊
Serverless的問題，我們搜遍了碩博士論文和台灣出版的書籍，推薦Costa, Rui寫的 Programming Google Cloud: Building Cloud Native Applications with Gcp 和的 Azure Data Engineering Cookbook - Second Edition: Get well versed in various data engineering techniques in Azure using this rec都 可以從中找到所需的評價。
另外網站Serverless Consulting, Development, Serverless Guru也說明：Serverless Guru is the leader in serverless consulting, development, cloud computing services and much more. Hire us to unlock the benefits of serverless ...
這兩本書分別來自 和所出版 。
國立政治大學 資訊科學系碩士在職專班 張宏慶所指導 羅時雨的 基於Kubernetes 高可用集群的節點失效容錯研究 以HTTP Web服務為驗證案例 （2021），提出Serverless關鍵因素是什麼，來自於容錯、節點失效、容器化平台、高可用集群、網絡流量工作負載。
而第二篇論文國立成功大學 資訊工程學系 李信杰所指導 董晉宇的 具自動延展能力之跨瀏覽器測試雲平台 （2021），提出因為有 網頁自動化測試、跨瀏覽器測試、具自動延展能力的重點而找出了 Serverless的解答。
最後網站Understanding Serverless Computing for the Beginner則補充：Serverless or serverless computing is a cloud-based execution model in which cloud service providers provision on-demand machine resources and ...
Programming Google Cloud: Building Cloud Native Applications with Gcp
為了解決Serverless 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
Serverless進入發燒排行的影片
基於Kubernetes 高可用集群的節點失效容錯研究 以HTTP Web服務為驗證案例
為了解決Serverless 的問題，作者羅時雨 這樣論述：
近年微服務架構、容器化技術普及、以Docker容器為標準化單位的軟體封裝，其快速佈署、彈性調整、跨平台運作特性，能讓業界更專注於創新和業務需求、可輕鬆管理底層基礎設施。隨著物聯網、大數據機器學習盛行，得跨主機平行處理大量資料，故當服務發生不可預期中斷時，得維持系統資源可用性與穩定性。隨著容器數量增長，Docker公司推出容器的管理平台Docker Swarm管理調度跨主機的容器，依據工作負載去調整其運作規模大小，當容器不可預期停止運作時，Docker Swarm叢集會自動產生新的容器，其確保容器服務高可用性。且在同時Google亦推出Kubernetes，故同時比較以Kubernetes 為
基礎的 Horizontal Pod Autoscaler，其會依據節點記憶體目標使用率，自動調整服務Pod個数，提升整體資源利用率。Kubernetes簡化應用程式的管理與佈署，但佈署後其集群內效能未被有效去評估與比較，本研究會針對集群內節點資源配置、參數設定，以Vertical-Pod-Autoscaler、Descheduler、Ingress Controller、Scheduling Framework做優化調整。並再與Docker Swarm 架構比較。驗證叢集中節點發生故障失效，優化整體叢集內Web服務Traffic Workload平均反應時間、最長反應時間、連線數成功率、成功
次數、失敗次數 數據結果。
Azure Data Engineering Cookbook - Second Edition: Get well versed in various data engineering techniques in Azure using this rec
為了解決Serverless 的問題，作者 這樣論述：
Nearly 80 recipes to help you collect and transform data from multiple sources into a single data source, making it way easier to perform analytics on the dataKey Features: Build data pipelines from scratch and find solutions to common data engineering problemsLearn how to work with Azure Data Fa
ctory, Data Lake, Databricks, and Synapse AnalyticsMonitor and maintain your data engineering pipelines using Log Analytics, Azure Monitor, and Azure PurviewBook Description: The famous quote ’Data is the new oil’ seems more true every day as the key to most organizations’ long-term success lies in
extracting insights from raw data. One of the major challenges organizations face in leveraging value out of data is building performant data engineering pipelines for data visualization, ingestion, storage, and processing. This second edition of the immensely successful book by Ahmad Osama brings t
o you several recent enhancements in Azure data engineering and shares approximately 80 useful recipes covering common scenarios in building data engineering pipelines in Microsoft Azure.You’ll explore recipes from Azure Synapse Analytics workspaces Gen 2 and get to grips with Synapse Spark pools, S
QL Serverless pools, Synapse integration pipelines, and Synapse data flows. You’ll also understand Synapse SQL Pool optimization techniques in this second edition. Besides Synapse enhancements, you’ll discover helpful tips on managing Azure SQL Database and learn about security, high availability, a
nd performance monitoring. Finally, the book takes you through overall data engineering pipeline management, focusing on monitoring using Log Analytics and tracking data lineage using Azure Purview.By the end of this book, you’ll be able to build superior data engineering pipelines along with having
an invaluable go-to guide.What You Will Learn: Process data using Azure Databricks and Azure Synapse AnalyticsPerform data transformation using Azure Synapse data flowsPerform common administrative tasks in Azure SQL DatabaseBuild effective Synapse SQL pools which can be consumed by Power BIMonitor
Synapse SQL and Spark pools using Log AnalyticsTrack data lineage using Microsoft Purview integration with pipelinesWho this book is for: This book is for data engineers, data architects, database administrators, and data professionals who want to get well versed with the Azure data services for bu
ilding data pipelines. Basic understanding of cloud and data engineering concepts will help in getting the most out of this book.
具自動延展能力之跨瀏覽器測試雲平台
為了解決Serverless 的問題，作者董晉宇 這樣論述：
中文摘要 iAbstract iiAcknowledgement iiiTable of Contents ivList of Tables viList of Figures viiList of Listings viiiChapter 1 Introduction 11.1 Motivation 11.2 Overview 21.3 Thesis Organization 3Chapter 2 Background and Related Work 42.1 Selenium Grid 42.2 Docker 52.3 Kubernetes 62.4 KEDA 72.5 Summary
of Related Work 7Chapter 3 The Proposed System Architecture 83.1 Problem 83.2 Building a Stable and Reliable Cross-Browser Testing Platform 93.2.1 Overview of Architecture 93.2.2 Defining Roles for the Worker Nodes 113.2.3 Deploy Selenium Hub on Kubernetes Worker Machines 133.2.4 Deploying Selenium
Nodes on the Other Kubernetes Worker Machines 173.2.5 Kubernetes Cluster Networking 213.3 Optimizing System Resources Using KEDA 25Chapter 4 Experiments 294.1 Hardware Specifications 294.2 Test Case Design 304.3 Finding an Optimal CPU and Memory Limitation for a Pod 314.4 Test Case Execution Time w
ith Single Browser Nodes Deployed 344.5 Test Case Execution Time with Multiple Browser Nodes Deployed 36Chapter 5 Conclusion 395.1 Conclusion 395.2 Future Work 39References 41