쿠버네티스에 어플리케이션을 손쉽게 배포하는 방법은 무엇일까요? 복잡하게 배포된 어플리케이션의 파드들은 어떻게 디버깅하고 로깅해야 할까요? 또한 요즘 자주 이야기 되는 클라우드 네이티브 아키텍처로 설계된 어플리케이션은 어떻게 만들고 배포해야하는 걸까요?삼성전자 무선사업부에서 삼성헬스를 EKS 에 배포한 사례를 살펴보며, 이러한 문제를 어떻게 해결했는지 알아봅니다. 또한 복잡하게만 느껴졌던 쿠버네티스의 어플리케이션 배포와 클라우드 네이티브 아키텍처의 베스트 프렉티스를 EKS 에 어플리케이션을 배포하고, 관리하는 예제를 통하여 간편하게 이해할 수 있게 도와드립니다.
롯데이커머스의 마이크로 서비스 아키텍처 진화와 비용 관점의 운영 노하우-나현길, 롯데이커머스 클라우드플랫폼 팀장::AWS 마이그레이션 A ...Amazon Web Services Korea
2015 년부터 진행한 실험적 퍼블릭클라우드 운영에 대한 최근 결과를 공유하며 그간 경험한 MSA Architecture 환경, Cost optimization, Operation 관련 내용을 공유합니다. 특히 대규모 운영 환경에서 경험한 다양한 관점의 경험과 비용절감에 대해 인사이트를 제공 예정입니다.
1. The document discusses microservices architecture and how Netflix transitioned from a monolithic architecture to microservices. Key aspects discussed include breaking the monolith into many small, independent services that are loosely coupled.
2. Netflix's microservices architecture is composed of hundreds of microservices running on thousands of servers. Each service focuses on doing a small, well-defined piece of work. Services communicate through well-defined APIs and share no code or databases.
3. The document provides examples of how other companies like Samsung and Vingle have also adopted microservices architectures on AWS, breaking monolithic applications into independent, scalable services. This allows for independent deployments, rapid innovation, and improved resilience.
컨테이너를 활용하여 마이크로서비스를 구성할 때는 효과적으로 컨테이너 및 서비스를 관리할 수 있는 방법이 필요합니다. 본 세션에서는 유연하게 컨테이너 환경을 관리/모니터링 할 수 있는 Amazon EC2 Container Service 및 EC2 Container Registry를 소개합니다. 아울러 Amazon ECS/ECR 환경에서 효과적인 자원 및 로그 관리, 마이크로서비스 관리에 대해서 자세히 살펴봅니다.
Amazon EKS를 통한 빠르고 편리한 컨테이너 플랫폼 활용 – 이일구 AWS 솔루션즈 아키텍트:: AWS Cloud Week - Ind...Amazon Web Services Korea
컨테이너를 활용하고자 하는 고객은 많이 있지만, 일정 규모 이상의 서비스를 하려면 오케스트레이션 플랫폼이 필수적 입니다. 직접 물리/가상 서버를 이용하여 컨테이너 플랫폼을 설치하는 방법도 있지만 이 경우 설치, 모니터링, 용량관리, 트래픽 처리 등 다양한 문제들을 마주하게 됩니다. AWS의 완전 관리형 쿠버네티스 서비스인 EKS를 통해 클러스터에 운영 시 고려해야하는 다양한 문제를 보다 쉽게 해결할 수 있습니다. 또한 다양한 에코 시스템과 연동하여 탄력적이고 비용 효율적인 모델을 서비스할 수 있도록 소개해 드립니다.
금융 서비스 패러다임의 전환 가속화 시대, 신한금융투자의 Cloud First 전략 - 신중훈 AWS 솔루션즈 아키텍트 / 최성봉 클라우...Amazon Web Services Korea
신한금융투자는 급변하는 금융 환경에 민첩하게 대응하기 위해 디지털 트랜스포메이션 마스터플랜을 수립하고, 2021년 상반기 본격적인 서비스 시작을 앞두고 있습니다. 비즈니스와 서비스의 중심을 클라우드 기반으로 전환하는 Cloud First 전략을 추진 중입니다. Cloud First 전략의 일환으로 데이터 & 고객 중심의 Seamless 서비스를 위해 클라우드 기반의 데이터 분석 플랫폼, 인공지능 컨택센터 구축에 착수하였으며, 이번 발표에서는 서비스 구축 과정에서 당사가 고민했던, Why Cloud, What and How to do에 대해 공유하고자 합니다.
데브시스터즈의 Cookie Run: OvenBreak 에 적용된 Kubernetes 기반 다중 개발 서버 환경 구축 시스템에 대한 발표입니다.
Container orchestration 기반 개발 환경 구축 시스템의 필요성과, 왜 Kubernetes를 선택했는지, Kubernetes의 개념과 유용한 기능들을 다룹니다. 아울러 구축한 시스템에 대한 데모와, 작업했던 항목들에 대해 리뷰합니다.
*NDC17 발표에서는 데모 동영상을 사용했으나, 슬라이드 캡쳐로 대신합니다.
클라우드 네이티브로의 전환이 확산되면서 애플리케이션을 상호 독립적인 최소 구성 요소로 쪼개는 마이크로서비스(microservices) 아키텍쳐가 각광받고 있는데요.
MSA는 애플리케이션의 확장이 쉽고 새로운 기능의 출시 기간을 단축시킬 수 있다는 장점이 있지만,
반면에 애플리케이션이 커지고 동일한 서비스의 여러 인스턴스가 동시에 실행되면 MSA간 통신이 복잡해 진다는 단점이 있습니다.
서비스 메쉬(Service Mesh)는 이러한 MSA의 트래픽 문제를 보완하기 위해 탄생한 기술로,
서비스 간의 네트워크 트래픽 관리에 초점을 맞춘 네트워킹 모델입니다.
서로 다른 애플리케이션이 얼마나 원활하게 상호작용하는지를 기록함으로써 커뮤니케이션을 최적화하고 애플리케이션 확장에 따른 다운 타임을 방지할 수 있습니다.
서비스 메쉬의 탄생 배경과 기능, 그리고 현재 오픈소스로 배포되어 있는 서비스 메쉬 솔루션에 대해 소개합니다.
Step1. Cloud Native Trail Map
Step2. Service Proxy, Discover, & Mesh
Step3. Service Mesh 솔루션
Step4. Service Mesh 구현화면 - Istio / linkerd
Step5. Multi-cluster (linkerd)
Hands-On Introduction to Kubernetes at LISA17Ryan Jarvinen
This document provides an agenda and instructions for a hands-on introduction to Kubernetes tutorial. The tutorial will cover Kubernetes basics like pods, services, deployments and replica sets. It includes steps for setting up a local Kubernetes environment using Minikube and demonstrates features like rolling updates, rollbacks and self-healing. Attendees will learn how to develop container-based applications locally with Kubernetes and deploy changes to preview them before promoting to production.
The document discusses Amazon EKS (Elastic Kubernetes Service), which allows users to run Kubernetes on AWS. It provides an overview of EKS and Kubernetes, the EKS control plane and worker nodes, networking options, storage, scaling, and CI/CD (continuous integration and continuous delivery) workflows. Key points include that EKS manages the control plane for users and integrates well with other AWS services, while allowing users to choose their own worker nodes and retain a native Kubernetes experience.
[AWS Container Service] Getting Started with Cloud Map, App Mesh and FirecrackerAmazon Web Services Korea
This document provides an overview and summary of Amazon Web Services (AWS) announcements from a conference in Seoul, South Korea. It includes summaries of new and updated AWS services across various categories such as compute, database, analytics, developer tools, and containers. Key announcements include the general availability of AWS App Mesh for managing communications between microservices applications and the public beta of AWS Cloud Map for service discovery.
AWS offers a comprehensive portfolio of compute services allowing customers to develop, deploy, run, and scale applications and workloads in the world’s most powerful, secure and innovative compute cloud. As customer implements AWS @Scale, it is essential to understand how to leverage the Big Three compute patterns – Virtual Server Hosting (Amazon EC2), Micro services (ECS/EKS) and Serverless computing (AWS Lambda) in ways that accelerate your Cloud adoption. This session will cover how Amazon EC2, Elastic Container Service(ECS), managed Amazon Kubernetes (EKS) and AWS Lambda are important additions to enterprises adopt AWS @scale. Join our discussion as we focus on patterns for usage and how to secure these respective workloads as you adopt these compute platforms in the enterprise environment.
This document provides an overview of microservices architecture and how BuzzFeed uses it with Amazon ECS and Docker containers. It discusses the benefits of microservices and characteristics. It then details how BuzzFeed developed their WatchBot platform on Amazon ECS, including that they now have over 400 services deployed across 7 clusters in 2 regions, with over 180 users and 39,000 deploys. The document also discusses lessons learned in developing the platform and current challenges.
Amazon Webservices Introduction And Core Modules Manish Kumar
AWS provides cloud computing services that allow companies to run their workloads on AWS infrastructure instead of building their own data centers. Major companies use AWS for agility, lower costs, global scale, and innovation. AWS offers a variety of services including compute, storage, databases, analytics, mobile, developer tools, management tools, and enterprise applications. Customers use these services to run websites and applications, process and store data, and more. AWS continues to lower prices and expand its services and features to help more customers adopt the cloud.
Modernizing applications with Amazon EKS - MAD304 - Santa Clara AWS Summit.pdfAmazon Web Services
In this session, learn how to easily containerize and migrate existing applications to Amazon Elastic Container Service for Kubernetes (Amazon EKS) without needing to refactor your code or tooling. Amazon EKS makes it easy to deploy, manage, and scale containerized applications using Kubernetes on AWS.
Donnie Prakoso, Technology Evangelist, ASEAN, AWS.
Container technology provides unparalleled improvements in efficiency and agility of packaging and deploying applications. Containers offer VM-like isolation and process-like efficiency and hence are becoming the de-facto method for deploying micro-services. However, using containers for running services at scale has required that operations team handle complex, dynamically changing infrastructure requirements, or run the risk or under- or over-provisioning infrastructure. Sounds like going back to the days before Cloud? In this session, learn how AWS services for containers take the pain out of managing infrastructure, and best practices for developing new services rapidly while running them at scale.
Attendees will learn how to leverage the identity and authorisation, network security and secrets management features of the wider AWS platform for their containers, including Amazon Elastic Container Service (Amazon ECS) and Amazon Elastic Container Service for Kubernetes (Amazon EKS). We also discuss best practices for the security of your container images such as scanning them for known vulnerabilities.
AWS Core Services Overview, Immersion Day Huntsville 2019Amazon Web Services
The document provides an overview of AWS core services including compute, storage, database, analytics, machine learning, IoT, and mobile services. It discusses AWS' breadth and depth of services across infrastructure, application services, management tools, and developer tools. It also highlights AWS' leadership in cloud computing with the largest customer base and most comprehensive set of services and features.
- AWS (Amazon Web Services) provides on-demand cloud computing platforms and APIs to individuals, companies, and governments, including computing power, database storage, content delivery and other functionality.
- It has over 1 million active customer accounts across 190 countries and offers a variety of services including compute, storage, databases, analytics, mobile, developer tools and management tools.
- AWS aims to enable businesses and developers to use web services to build scalable and sophisticated applications, with an emphasis on security, high availability, scalability and flexibility.
Docker and AWS have been working together to improve the Docker experience you already know and love. Deploying from Docker straight to AWS with your existing workflow has never been easier. Developers can use Docker Compose and Docker Desktop to deploy applications on Amazon ECS on AWS Fargate. This new functionality streamlines the process of deploying and managing containers in AWS from a local development environment running Docker. Join us for a hands-on walk through of how you can get started today.
Comparing Compute Options for Microservices - AWS Summti Sydney 2018Amazon Web Services
This document discusses options for deploying microservices on AWS, including using containers with ECS and EKS, as well as serverless architectures with Lambda. It covers topics like container orchestration, continuous delivery pipelines, and monitoring with X-Ray.
Fast-Track Your Application Modernisation Journey with Containers - AWS Summi...Amazon Web Services
The document discusses containers and orchestration platforms like Amazon ECS and Amazon EKS. It introduces the Mythical Misfits application that will be used in the hands-on lab. The lab will involve setting up environments for the monolithic and microservices versions of the application using containers and either ECS or EKS. Participants will build Docker images, deploy to ECS or EKS clusters, split the application into microservices, enable monitoring and logging, and automate deployments.
This document provides an overview of microservices architecture and Amazon ECS. It begins with definitions of microservices and comparisons to monolithic architectures. Key characteristics of microservices are described. Amazon ECS is introduced as a fully managed container orchestration service that integrates with other AWS services. The document discusses deploying containers on ECS and task placement options. Examples are provided of architectures using ECS and other AWS services like Lambda, Aurora and DynamoDB. Case studies of Samsung and Instacart's use of microservices on ECS are summarized. Details of the internal workings of ECS around scheduling and placement are covered. The Twelve-Factor App methodology is discussed in relation to ECS. Finally, the document introduces Blo
Securing serverless and container services - SDD306 - AWS re:Inforce 2019 Amazon Web Services
Most customers are uncertain of how to secure their serverless services because these services deviate from traditional perimeter security. Additionally, many security stakeholders do not have as much insight into serverless architectures as developer communities. In this session, we provide best practices, patterns, and demos on securing serverless services using a combination of secure coding practices with partner code libraries, DevOps principles, code/container version control using code, and a deep understanding of serverless services such as AWS Lambda, AWS Fargate, and Amazon EKS. We aim to provide some baselining mechanisms and patterns to build full serverless and secure service architectures.
This document provides an overview and agenda for a workshop on deploying a deep learning framework on Amazon ECS and Spot Instances. The workshop will:
- Introduce MXNet, an open source deep learning framework, and how it can be used to define, train, and deploy neural networks.
- Discuss containers and how they can increase infrastructure utilization and make it easy to deploy diverse applications on shared hardware.
- Provide an overview of Amazon ECS for managing Docker containers, Amazon ECR for storing container images, and Spot Instances for running containers on unused EC2 capacity.
- Include hands-on labs to set up the environment, build an MXNet Docker image,
WKS401 Deploy a Deep Learning Framework on Amazon ECS and EC2 Spot InstancesAmazon Web Services
Deep learning is an implementation of machine learning that uses neural networks to solve difficult and complex problems, such as computer vision, natural language processing, and recommendations. Due to the availability of deep learning libraries and frameworks, developers have the ability to enhance the capabilities of their applications and projects.
In this workshop, you learn how to build and deploy a powerful deep learning framework called MXNet on containers. The portability and resource management benefit of containers means developers can focus less on infrastructure and more on building. The labs start by demonstrating the automation capabilities of AWS CloudFormation to stand up core infrastructure; as an added bonus, you use Spot Fleet to leverage the cost benefits of using Spot Instances, especially for developer environments. Then, you walk through creating an MXNet container in Docker and deploying it with Amazon ECS. Finally, you walk through an image classification demo of MXNet to validate that everything is working as expected.
Pre-reqs: Laptop and AWS account
Connect and Interconnect – The Mesh of Event-Driven Compute and Marvelous Vir...Amazon Web Services
This document summarizes a presentation about innovating using AWS services. It discusses how AWS services allow lowering the cost of failure through experimentation using containers, serverless computing, mobile development, IoT, voice control with Alexa, and virtual worlds. Specific services mentioned include EC2 Container Service, AWS Lambda, API Gateway, Cognito, DynamoDB, Mobile Hub, Device Farm, IoT, Lumberyard, and Twitch integration. The presentation encourages attendees to build scalable and modular solutions using these AWS services.
AWS Cloud Experience CA: ¿Porqué Correr WorkLoads Microsoft & Oracle en AWS?Amazon Web Services LATAM
The document discusses the benefits of running Microsoft workloads on AWS, including AWS's extensive experience supporting Windows workloads for 10 years, large portfolio of services optimized for Microsoft applications, and global infrastructure providing high availability. It highlights key AWS services that help customers run SQL Server, Active Directory, file storage, and more in hybrid environments. The document also covers security, management tools, and flexible licensing options available on AWS for Microsoft workloads.
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클라우드에서 Database를 백업하고 복구하는 방법에 대해 설명드립니다. AWS Backup을 사용하여 전체백업/복구 부터 PITR(Point in Time Recovery)백업, 그리고 멀티 어카운트, 멀티 리전등 다양한 데이터 보호 방법을 소개합니다(데모 포함). 또한 self-managed DB 의 데이터 저장소로 Amazon FSx for NetApp ONTAP 스토리지 서비스를 사용할 경우 얼마나 신속하게 데이터를 복구/복제 할수 있는지 살펴 봅니다.
기업은 이벤트나 신제품 출시 등으로 예기치 못한 트래픽 급증 시 데이터베이스 과부하, 서비스 지연 및 중단 등의 문제를 겪곤 합니다. Aurora 오토스케일링은 프로비저닝 시간으로 인해 실시간 대응이 어렵고, 트래픽 대응을 위한 과잉 프로비저닝이 발생합니다. 이러한 문제를 해결하기 위해 프로비저닝된 Amazon Aurora 클러스터와 Aurora Serverless v2(ASV2) 인스턴스를 결합하는 Amazon Aurora 혼합 구성 클러스터 아키텍처와 고해상도 지표를 기반으로 하는 커스텀 오토스케일링 솔루션을 소개합니다.
Amazon Aurora 클러스터를 초당 수백만 건의 쓰기 트랜잭션으로 확장하고 페타바이트 규모의 데이터를 관리할 수 있으며, 사용자 지정 애플리케이션 로직을 생성하거나 여러 데이터베이스를 관리할 필요 없이 Aurora에서 관계형 데이터베이스 워크로드를 단일 Aurora 라이터 인스턴스의 한도 이상으로 확장할 수 있는 Amazon Aurora Limitless Database를 소개합니다.
Amazon Aurora MySQL 호환 버전 2(MySQL 5.7 호환성 지원)는 2024년 10월 31일에 표준 지원이 종료될 예정입니다. 이로 인해 Aurora MySQL의 메이저 버전 업그레이드를 검토하고 계시다면, Amazon Blue/Green Deployments는 운영 환경에 영향을 주지 않고 메이저 버전 업그레이드를 할 수 있는 최적의 솔루션입니다. 본 세션에서는 Blue/Green Deployments를 통한 Aurora MySQL의 메이저 버전 업그레이드를 실습합니다.
Amazon DocumentDB(MongoDB와 호환됨)는 빠르고 안정적이며 완전 관리형 데이터베이스 서비스입니다. Amazon DocumentDB를 사용하면 클라우드에서 MongoDB 호환 데이터베이스를 쉽게 설치, 운영 및 규모를 조정할 수 있습니다. Amazon DocumentDB를 사용하면 MongoDB에서 사용하는 것과 동일한 애플리케이션 코드를 실행하고 동일한 드라이버와 도구를 사용하는 것을 실습합니다.
사례로 알아보는 Database Migration Service : 데이터베이스 및 데이터 이관, 통합, 분리, 분석의 도구 - 발표자: ...Amazon Web Services Korea
Database Migration Service(DMS)는 RDBMS 이외에도 다양한 데이터베이스 이관을 지원합니다. 실제 고객사 사례를 통해 DMS가 데이터베이스 이관, 통합, 분리를 수행하는 데 어떻게 활용되는지 알아보고, 동시에 데이터 분석을 위한 데이터 수집(Data Ingest)에도 어떤 역할을 하는지 살펴보겠습니다.
Amazon Elasticache - Fully managed, Redis & Memcached Compatible Service (Lev...Amazon Web Services Korea
Amazon ElastiCache는 Redis 및 MemCached와 호환되는 완전관리형 서비스로서 현대적 애플리케이션의 성능을 최적의 비용으로 실시간으로 개선해 줍니다. ElastiCache의 Best Practice를 통해 최적의 성능과 서비스 최적화 방법에 대해 알아봅니다.
Internal Architecture of Amazon Aurora (Level 400) - 발표자: 정달영, APAC RDS Speci...Amazon Web Services Korea
ccAmazon Aurora 데이터베이스는 클라우드용으로 구축된 관계형 데이터베이스입니다. Aurora는 상용 데이터베이스의 성능과 가용성, 그리고 오픈소스 데이터베이스의 단순성과 비용 효율성을 모두 제공합니다. 이 세션은 Aurora의 고급 사용자들을 위한 세션으로써 Aurora의 내부 구조와 성능 최적화에 대해 알아봅니다.
[Keynote] 슬기로운 AWS 데이터베이스 선택하기 - 발표자: 강민석, Korea Database SA Manager, WWSO, A...Amazon Web Services Korea
오랫동안 관계형 데이터베이스가 가장 많이 사용되었으며 거의 모든 애플리케이션에서 널리 사용되었습니다. 따라서 애플리케이션 아키텍처에서 데이터베이스를 선택하기가 더 쉬웠지만, 구축할 수 있는 애플리케이션의 유형이 제한적이었습니다. 관계형 데이터베이스는 스위스 군용 칼과 같아서 많은 일을 할 수 있지만 특정 업무에는 완벽하게 적합하지는 않습니다. 클라우드 컴퓨팅의 등장으로 경제적인 방식으로 더욱 탄력적이고 확장 가능한 애플리케이션을 구축할 수 있게 되면서 기술적으로 가능한 일이 달라졌습니다. 이러한 변화는 전용 데이터베이스의 부상으로 이어졌습니다. 개발자는 더 이상 기본 관계형 데이터베이스를 사용할 필요가 없습니다. 개발자는 애플리케이션의 요구 사항을 신중하게 고려하고 이러한 요구 사항에 맞는 데이터베이스를 선택할 수 있습니다.
Demystify Streaming on AWS - 발표자: 이종혁, Sr Analytics Specialist, WWSO, AWS :::...Amazon Web Services Korea
실시간 분석은 AWS 고객의 사용 사례가 점점 늘어나고 있습니다. 이 세션에 참여하여 스트리밍 데이터 기술이 어떻게 데이터를 즉시 분석하고, 시스템 간에 데이터를 실시간으로 이동하고, 실행 가능한 통찰력을 더 빠르게 얻을 수 있는지 알아보십시오. 일반적인 스트리밍 데이터 사용 사례, 비즈니스에서 실시간 분석을 쉽게 활성화하는 단계, AWS가 Amazon Kinesis와 같은 AWS 스트리밍 데이터 서비스를 사용하도록 지원하는 방법을 다룹니다.
Amazon EMR - Enhancements on Cost/Performance, Serverless - 발표자: 김기영, Sr Anal...Amazon Web Services Korea
Amazon EMR은 Apache Spark, Hive, Presto, Trino, HBase 및 Flink와 같은 오픈 소스 프레임워크를 사용하여 분석 애플리케이션을 쉽게 실행할 수 있는 관리형 서비스를 제공합니다. Spark 및 Presto용 Amazon EMR 런타임에는 오픈 소스 Apache Spark 및 Presto에 비해 두 배 이상의 성능 향상을 제공하는 최적화 기능이 포함되어 있습니다. Amazon EMR Serverless는 Amazon EMR의 새로운 배포 옵션이지만 데이터 엔지니어와 분석가는 클라우드에서 페타바이트 규모의 데이터 분석을 쉽고 비용 효율적으로 실행할 수 있습니다. 이 세션에 참여하여 개념, 설계 패턴, 라이브 데모를 사용하여 Amazon EMR/EMR 서버리스를 살펴보고 Spark 및 Hive 워크로드, Amazon EMR 스튜디오 및 Amazon SageMaker Studio와의 Amazon EMR 통합을 실행하는 것이 얼마나 쉬운지 알아보십시오.
Amazon OpenSearch - Use Cases, Security/Observability, Serverless and Enhance...Amazon Web Services Korea
로그 및 지표 데이터를 쉽게 가져오고, OpenSearch 검색 API를 사용하고, OpenSearch 대시보드를 사용하여 시각화를 구축하는 등 Amazon OpenSearch의 새로운 기능과 기능에 대해 자세히 알아보십시오. 애플리케이션 문제를 디버깅할 수 있는 OpenSearch의 Observability 기능에 대해 알아보세요. Amazon OpenSearch Service를 통해 인프라 관리에 대해 걱정하지 않고 검색 또는 모니터링 문제에 집중할 수 있는 방법을 알아보십시오.
Enabling Agility with Data Governance - 발표자: 김성연, Analytics Specialist, WWSO,...Amazon Web Services Korea
데이터 거버넌스는 전체 프로세스에서 데이터를 관리하여 데이터의 정확성과 완전성을 보장하고 필요한 사람들이 데이터에 액세스할 수 있도록 하는 프로세스입니다. 이 세션에 참여하여 AWS가 어떻게 분석 서비스 전반에서 데이터 준비 및 통합부터 데이터 액세스, 데이터 품질 및 메타데이터 관리에 이르기까지 포괄적인 데이터 거버넌스를 제공하는지 알아보십시오. AWS에서의 스트리밍에 대해 자세히 알아보십시오.
How RPA Help in the Transportation and Logistics Industry.pptxSynapseIndia
Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.
In this follow-up session on knowledge and prompt engineering, we will explore structured prompting, chain of thought prompting, iterative prompting, prompt optimization, emotional language prompts, and the inclusion of user signals and industry-specific data to enhance LLM performance.
Join EIS Founder & CEO Seth Earley and special guest Nick Usborne, Copywriter, Trainer, and Speaker, as they delve into these methodologies to improve AI-driven knowledge processes for employees and customers alike.
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
Scaling Connections in PostgreSQL Postgres Bangalore(PGBLR) Meetup-2 - MydbopsMydbops
This presentation, delivered at the Postgres Bangalore (PGBLR) Meetup-2 on June 29th, 2024, dives deep into connection pooling for PostgreSQL databases. Aakash M, a PostgreSQL Tech Lead at Mydbops, explores the challenges of managing numerous connections and explains how connection pooling optimizes performance and resource utilization.
Key Takeaways:
* Understand why connection pooling is essential for high-traffic applications
* Explore various connection poolers available for PostgreSQL, including pgbouncer
* Learn the configuration options and functionalities of pgbouncer
* Discover best practices for monitoring and troubleshooting connection pooling setups
* Gain insights into real-world use cases and considerations for production environments
This presentation is ideal for:
* Database administrators (DBAs)
* Developers working with PostgreSQL
* DevOps engineers
* Anyone interested in optimizing PostgreSQL performance
Contact info@mydbops.com for PostgreSQL Managed, Consulting and Remote DBA Services
Details of description part II: Describing images in practice - Tech Forum 2024BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and transcript: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
How Netflix Builds High Performance Applications at Global ScaleScyllaDB
We all want to build applications that are blazingly fast. We also want to scale them to users all over the world. Can the two happen together? Can users in the slowest of environments also get a fast experience? Learn how we do this at Netflix: how we understand every user's needs and preferences and build high performance applications that work for every user, every time.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/07/intels-approach-to-operationalizing-ai-in-the-manufacturing-sector-a-presentation-from-intel/
Tara Thimmanaik, AI Systems and Solutions Architect at Intel, presents the “Intel’s Approach to Operationalizing AI in the Manufacturing Sector,” tutorial at the May 2024 Embedded Vision Summit.
AI at the edge is powering a revolution in industrial IoT, from real-time processing and analytics that drive greater efficiency and learning to predictive maintenance. Intel is focused on developing tools and assets to help domain experts operationalize AI-based solutions in their fields of expertise.
In this talk, Thimmanaik explains how Intel’s software platforms simplify labor-intensive data upload, labeling, training, model optimization and retraining tasks. She shows how domain experts can quickly build vision models for a wide range of processes—detecting defective parts on a production line, reducing downtime on the factory floor, automating inventory management and other digitization and automation projects. And she introduces Intel-provided edge computing assets that empower faster localized insights and decisions, improving labor productivity through easy-to-use AI tools that democratize AI.
Video traffic on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research and of industrial networked multimedia services certainly was the HTTP Adaptive Streaming (HAS) technique. This resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) which, together with HTTP Live Streaming (HLS), is widely used for multimedia delivery in today’s networks. Existing challenges in multimedia systems research deal with the trade-off between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, latency), and (iii) quality of experience (QoE). Optimizing towards one aspect usually negatively impacts at least one of the other two aspects if not both. This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry. In this talk, we will present selected novel approaches and research results of the first year of the ATHENA CD Lab’s operation. We will highlight HAS-related research on (i) multimedia content provisioning (machine learning for video encoding); (ii) multimedia content delivery (support of edge processing and virtualized network functions for video networking); (iii) multimedia content consumption and end-to-end aspects (player-triggered segment retransmissions to improve video playout quality); and (iv) novel QoE investigations (adaptive point cloud streaming). We will also put the work into the context of international multimedia systems research.
The Rise of Supernetwork Data Intensive ComputingLarry Smarr
Invited Remote Lecture to SC21
The International Conference for High Performance Computing, Networking, Storage, and Analysis
St. Louis, Missouri
November 18, 2021
Hire a private investigator to get cell phone recordsHackersList
Learn what private investigators can legally do to obtain cell phone records and track phones, plus ethical considerations and alternatives for addressing privacy concerns.
Coordinate Systems in FME 101 - Webinar SlidesSafe Software
If you’ve ever had to analyze a map or GPS data, chances are you’ve encountered and even worked with coordinate systems. As historical data continually updates through GPS, understanding coordinate systems is increasingly crucial. However, not everyone knows why they exist or how to effectively use them for data-driven insights.
During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to maintain and transform your data’s coordinate systems in an easy-to-digest way, accurately representing the geographical space that it exists within. During this webinar, you will have the chance to:
- Enhance Your Understanding: Gain a clear overview of what coordinate systems are and their value
- Learn Practical Applications: Why we need datams and projections, plus units between coordinate systems
- Maximize with FME: Understand how FME handles coordinate systems, including a brief summary of the 3 main reprojectors
- Custom Coordinate Systems: Learn how to work with FME and coordinate systems beyond what is natively supported
- Look Ahead: Gain insights into where FME is headed with coordinate systems in the future
Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!
How to Avoid Learning the Linux-Kernel Memory ModelScyllaDB
The Linux-kernel memory model (LKMM) is a powerful tool for developing highly concurrent Linux-kernel code, but it also has a steep learning curve. Wouldn't it be great to get most of LKMM's benefits without the learning curve?
This talk will describe how to do exactly that by using the standard Linux-kernel APIs (locking, reference counting, RCU) along with a simple rules of thumb, thus gaining most of LKMM's power with less learning. And the full LKMM is always there when you need it!
How to Avoid Learning the Linux-Kernel Memory Model
[AWS Dev Day] 앱 현대화 | DevOps 개발자가 되기 위한 쿠버네티스 핵심 활용 예제 알아보기 - 정영준 AWS 솔루션즈 아키텍트, 이상호 삼성전자 Health서비스팀 선임
2. DevOps 개발자가 되기 위한 쿠버
네티스 핵심 활용 예제 알아보기
정영준
솔루션즈 아키텍트
AWS
이상호
선임
삼성전자 Health서비스팀
3. Agenda
• Container Service Overview
• EKS Tenets and Core Concept
• Samsung Health Use Case
• EKS Best Practices & Sample Deploy Architecture
5. We’re making AWS the best place to run contain
ers and Kubernetes
6. AWS container services landscape
Management
Deployment, Scheduling,
Scaling & Management of
containerized applications
Hosting
Where the containers run
Amazon Elastic
Container Service
Amazon Elastic
Kubernetes Service
Amazon EC2 AWS Fargate
Image Registry
Container Image Repository
Amazon Elastic
Container Registry
7. Balancing flexibility and simplicity:
Workload-by-workload
Flexibility focused
Low level of opinion
Low level of abstraction
Focus on infrastructure
and configuration
Installing, configuring, and
managing my compute environment
is critical to achieving my goals
Value simplicity
High level of opinion
High level of abstraction
Focus only on app
and primitive
Having a standardized and
on-demand compute environment
is critical to achieving my goals
8. We give you the power to choose
Amazon ECS Amazon EKS
Amazon
EC2
AWS
Fargate
Amazon
EC2
AWS
Fargate
1. Choose your
orchestration tool
2. Choose your
launch type
We’re
working
on it #32
9. EKS tenets
1. Amazon EKS is a platform to run production-grade workloads. Security and reliability are
our first priority. After that we focus on doing the heavy lifting for you in the control
plane, including life cycle-related things like version upgrades.
2. Amazon EKS provides a native and upstream Kubernetes experience. Amazon EKS
provides vanilla, un-forked Kubernetes. In keeping with our first tenant, we ensure the
Kubernetes versions we run have security-related patches, even for older, supported
versions as quickly as possible. But there’s no special sauce and no lock in.
3. If you want to use additional AWS services, integrations are as seamless as possible.
4. The Amazon EKS team in AWS actively contributes to the upstream Kubernetes project
and the wider CNCF activities, both on the technical level as well as community, from
communicating good practices to participation in SIGs and working groups.
10. How are customer using Amazon EKS?
Microservices
PaaS
Platform as a service Enterprise App
Migration
Machine Learning
13. VPC
Kubernetes control plane
Highly available and single
tenant infrastructure
All “native AWS” components
Fronted by a Network Load
Balancer
NLB
Amazon
EKS
Availability Zone 1 Availability Zone 2 Availability Zone 3
Etcd
API Servers
15. eksctl - a CLI for Amazon EKS
• Single command cluster creation
eksctl create cluster --nodes=4
• Open source and on GitHub
• Built by Weave and AWS
• Official Amazon EKS CLI
16. Standard EC2 compute instance types
P and G type accelerated instances
i3 bare metal
Spot Instances
Bring your own instances
Instance flexibility
17. Bring your own OS
Amazon EKS AMI build scripts
https://github.com/awslabs/amazon-eks-ami
Amazon
Amazon
Amazon
18. Windows containers
Run Windows containers and Windows Server nodes with Amazon EKS
Supports heterogeneous (mixed) clusters.
Kubernetes version 1.11+
Available in all Amazon EKS Regions
Developer preview:
https://github.com/aws/containers-roadmap
21. Support Kubernetes version
Latest Kubernetes: 1.14
Amazon EKS will support up to three versions of Kubernetes at once
Deprecation in line with the community stopping support for older
versions
Version 1.11 deprecation on Nov 4th, 2019
22. Amazon EKS platform version
Platform version revisions represent API server configuration
changes or Kubernetes patches
Platform versions increment within a Kubernetes version only
23. Amazon EKS update life cycle
May 21, 2019
Blog: https://aws.amazon.com/blogs/compute/updates-to-amazon-eks-version-lifecycle/
24. Amazon EKS support sophisticated and scalable infrastructure
[mycluster].eks.amazonaws.com
Availability
Zone 1
Availability
Zone 2
Availability
Zone 3
Kubectl
VPC
Instance
Auto Scaling group for m4.large Spot Instances
Auto Scaling group for t2.medium Spot Instances
Auto Scaling group for On-Demand Instances
Cluster Autoscaler Daemonset
Spot Interruption handler
Daemonset
https://eksworkshop.com/spot/
managespot/deployhandler
33. Reverse Proxy with ES
in case of vpc access
NGINXNGINX
KIBANAKIBANA
COGNITOCOGNITO
② if no authentication then 302 redirect_to_cognito
?redirect_uri={vpc_kibana_domain}
① kibana access via nginx
③ sign up then redirect to kibana
with redirect_uri
proxy_redirect https://{cognito_host} https://$host;
proxy_redirect https://{kibana_host} https://$host;
user
40. Challenges in Using & Deploying Containers
As cloud native technologies change the way companies are designing an
d building applications, challenges are inevitable. The top challenges that
respondents face are:
• Cultural Changes with Development Team (41%)
• Complexity (40% up from 35%)
• Lack of Training (40%)
• Security (38% down from 43%)
• Monitoring (34% down from 38%)
• Storage (30% down from 41%)
• Networking (30% down from 38%)
https://www.cncf.io/blog/2018/08/29/cncf-survey-use-of-cloud-native-technologies-in-production-has-grown-over-200-percent/
42. Container security onion model: Defense in depth
• full blown distro (Ubuntu, AL) vs. minimal
environment (container-optimized
distribution)
• multi-tenancy requirements
• gotchas: Linux packages/CVEs,
leaks, GDPR (in Europe)
• runtime/standards (OCI)
• immutability of images
• all containers share a kernel (mitigation: Firecracker)
• gotchas: unnecessary privileged users, no scans, trust
• code analysis
• source available?
• gotchas: big surface,
many languages
{}
}
• sanitizing user input
• static code analysis
• gotchas: log-leaking }
• sensitive config (passwords,
API keys, etc.)
• gotchas: commits-to-source,
non-separated access (dev has
cleartext password)
{
• business core data
• Personal Identifiable
Information (PII)
• gotchas: leaks, GDPR
(in Europe)
{
host
container
dependencies
code
config
user data
45. API-server endpoint access control
Worker VPC (your account)
Kubectl
Master VPC (AWS account)
etcd
AZ 1
API Server
etcd
API Server
prod-cluster-123.eks.amazonaws.com
EKS-owned ENI
Kubelet
AZ 1
Worker
node
EKS-owned ENI
Kubelet
AZ 2
Worker
node
Public == true
AZ 2
Kube-proxy Kube-proxy
46. API-server endpoint access control
Worker VPC (your account)
Kubectl
Master VPC (AWS account)
etcd
AZ 1
AZ 2
API Server
etcd
API Server
prod-cluster-123.eks.amazonaws.com
EKS-owned ENIs
Public == true
Private == true
prod-cluster-123.eks.amazonaws.com
Private hosted zone
Kubelet
AZ 1
Worker
node
Kube-proxy
Kubelet
AZ 2
Worker
node
Kube-proxy
47. API-server endpoint access control
Worker VPC (your account)
Kubectl
Master VPC (AWS account)
etcd
AZ 1 AZ 2
API Server
etcd
API Server
EKS-owned ENIs
Public == false
Private == true
prod-cluster-123.eks.amazonaws.com
Private hosted zone
Kubelet
AZ 1
Worker
node
Kube-proxy
Kubelet
AZ 2
Worker
node
Kube-proxy
48. AWS Identity and Access Management (IAM) Aut
hentication
Kubectl
3) Authorizes AWS identity with RBAC
K8s API
1) Passes AWS identity
2) Verifies AWS identity
4) K8s action
allowed/denied
49. Great integration responsibility – IAM roles
Frontend pod
Node
Backend pod
Amazon S3
UI customization
bucket
Billing reports
bucket
Role
IAM
Log DaemonSet
Kinesis
Kinesis Data
Streams
ElastiCache
ElastiCache for
Redis
50. Great integration responsibility – IAM roles
Frontend pod
Node
Backend pod
Amazon S3
UI customization
bucket
Billing reports
bucket
IAM Kinesis
Kinesis Data
Streams
Log DaemonSet
ElastiCache
ElastiCache for
RedisCredential
Credential
Credential
52. Service type LoadBalancer
Service
(LoadBalancer)
ELB Users
Pod selector
(type = nodejs)
Frontend pod 1
Node
NodePort
Frontend pod 2
Node
NodePort
Frontend pod 3
Node
NodePort
Backend pod 1
Node
NodePort
Generic pod selector
53. Great responsibility – Service type LoadBalancer
Service
(LoadBalancer)
ELB Users
Pod selector
(app = frontend)
Frontend pod 1
Node
NodePort
Frontend pod 2
Node
NodePort
Frontend pod 3
Node
NodePort
Backend pod 1
Node
NodePortService
(LoadBalancer)
Pod selector
(app = backend)
ELB Users
55. Amazon CloudWatch Container Insights
Gives you complete visibility into your cloud resources and applications
so you can monitor, troubleshoot, and remediate issues
Collect logs &
metrics
Monitor,
troubleshoot &
set alarms
Act AnalyzeAmazon
CloudWatch
56. Collects, aggregates, and summarizes
Reliable, secure metrics and logs collection
Automated dashboards and analysis
Observability experience across metrics, logs, traces
Ad hoc analytics
CloudWatch Container Insights
A fully managed observability service for monitoring, troubleshooting
, and alarming on your containerized applications and microservices
58. Container Insights available now
1. Fully managed, AWS native observability service providing autom
ated summary and analysis of compute capacity
2. Reliable and secure collection of application logs with built-in an
alytics capabilities
3. Prebuilt visualization to summarize cluster and node errors
4. Application & microservice tracing - Troubleshoot and debug ap
plication & microservice
60. Container storage interface (CSI)
A flexible standard for orchestration
and storage provider connections
We support the CSI standard through following drivers:
Amazon Elastic Block Store: Amazon EBS CSI Driver
Amazon Elastic File System: Amazon EFS CSI Driver
Amazon FSx for Lustre: Amazon FSx CSI Driver
61. Storage volume lifecycle
Provisioning Binding Using Reclaiming
• Static
• Dynamic*
• Control loop watches
for PVC requests and
satisfies if PV is
available.
• For Dynamic, PVC will
provision PV
• PVC to PV binding is
one-to-one mapping
• Cluster mounts
volume based on
PVC
• Retain (default)
• Recycle
• Delete
62. What if I need specific volume type?
StorageClass
gp2 io1 sc1 encrypted
io1
st1
1) Admin pre-provisions
StorageClass based
on workload needs
2) End user requests for
specific volume types
(e.g., encrypted io1
volume)
3) Control loop watches
PVC request and
allocates volume if PV
exists
MySQL Pods
4) User creates stateful
workload
65. Load balancing
All three Elastic Load Balancing products are supported
NLB and CLB supported by Kubernetes Service
type=LoadBalancer
Internal and External Load Balancer support
https://aws.amazon.com/blogs/opensource/network-load-balancer-nginx-ingress-controller-eks/
66. • Exposes the service externally using a cloud
provider’s load balancer
• NodePort and ClusterIP services (to which LB
will route) automatically created
• Each service exposed with a LoadBalancer (ELB
or NLB) will get its own IP address
• Exposes L4 (TCP) or L7 (HTTP) services
Kubernetes ServiceType: LoadBalancer
67. Load balancing
Want to use an Internal Load Balancer? Use annotation:
service.beta.kubernetes.io/aws-load-balancer-internal: 0.0.0.0/0
Want to use an NLB? Use annotation:
service.beta.kubernetes.io/aws-load-balancer-type: nlb
69. Service load balancer: Network Load Balancer (NLB)
• NLB supports forwarding the client’s IP through to the node
.spec.externalTrafficPolicy = Local client ip passed to pod
• Nodes with no matching pods will be removed by specified NLB’s health check
.spec.healthCheckNodePort
• Use DaemonSet or pod anti-affinity to verify even traffic split
70. • exposes HTTP/HTTPS routes
to services within the cluster
• Many implementations: ALB,
NGINX, F5, HAProxy etc.
• Default service type: ClusterIP
Kubernetes Ingress object
71. ALB Ingress controller
AWS Resources
Kubernetes Cluster
Node Node
Kubernetes
API Server ALB Ingress
Controller
Node
HTTP ListenerHTTPS Listener
Rule: /cheesesRule: /charcuterie
TargetGroup:
Green (IP Mode)
TargetGroup:
Blue (Instance
Mode)
NodePort NodePort
https://docs.aws.amazon.com/en_pv/eks/latest/userguide/alb-ingress.html
74. Implementing logging with EFK
fluentd is an open source
data collector providing a
unified logging layer
elasticsearch is a
distributed, RESTful search
and analytics engine
kibana lets you visualize
your Elasticsearch data
76. Logging with FluentBit
• New AWS FluentBit container
plugin
• Optimize costs. Route logs fr
om Amazon EKS and Amazon
ECS clusters directly to Amaz
on S3 and query with Amazo
n Athena
• Open source
• More resource-efficient than
Fluentd. Tests show Fluentd u
ses 4x more CPU and 6x more
memory
https://aws.amazon.com/blogs/opensource/centra
lized-container-logging-fluent-bit/
77. Logging performance compare
Log Lines Per second Data Out Fluentd CPU Fluent Bit CPU Fluentd Memory Fluent Bit Memory
100 25 KB/s 0.013 vCPU 0.003 vCPU 146 MB 27 MB
1000 250 KB/s 0.103 vCPU 0.03 vCPU 303 MB 44 MB
10000 2.5 MB/s 1.03 vCPU 0.19 vCPU 376 MB 65 MB
Log Lines Per second Data Out Fluentd CPU Fluent Bit CPU Fluentd Memory Fluent Bit Memory
100 25 KB/s 0.006 vCPU 0.003 vCPU 84 MB 27 MB
1000 250 KB/s 0.073 vCPU 0.033 vCPU 102 MB 37 MB
10000 2.5 MB/s 0.86 vCPU 0.13 vCPU 438 MB 55 MB
79. Why AWS App Mesh?
http/tcp
Service
team A
Service
team B
Common need: Manage interservice traffic
• How to observe (logs, metrics, traces)
• How to load balance E/W traffic
• How to shift traffic between deployments
• How to decouple service teams
• How to minimize impact to app code
80. App Mesh uses Envoy proxy
OSS community project
Wide community support, numerous integrations
Stable and production-proven
Graduated Project in Cloud Native Computing Foundation
Started at Lyft in 2016
81. Why App Mesh?
HTTP / TCP
Service
team A
Service
team B
Control plane
Translates logical intent to proxy config
Distributes proxy config
Proxy
Sits between all services
Manages and observes traffic
Control plane
82. App Mesh: App-level communication across AWS
Amazon ECS
AWS Fargate
Amazon EKS
Amazon EC2
AWS App Mesh
Kubernetes on EC2
83. App Mesh: Application observability
Logging
HTTP access logging
Amazon CloudWatch Logs
Available as container logs on
Amazon ECS, Amazon EKS, AWS Fargate
Metrics
CloudWatch metrics
StatsD (with tags)
Prometheus
Tracing
AWS X-Ray
Other Envoy tracing drivers
89. Adding business data (Node.js)
//Example showing how to add business data to traces
app.use(function(req, res, next){
if (req.session !== undefined) {
let segment = AWSXRay.getSegment()
// User sessionID as userID
segment.addAnnotation(‘userID', req.sessionID);
}
next();
})
//Example showing how to add business data to traces
app.use(function(req, res, next){
if (req.session !== undefined) {
let segment = AWSXRay.getSegment()
// User sessionID as userID
segment.addAnnotation(‘userID', req.sessionID);
}
next();
})
95. To conclude…
Clearly understand managed systems’ ownership boundaries and your sys
tems to build the own K8s Platform.
“With great power
comes great responsibility.”