Presentation shows how current Cloud looks like and how it will evolve with serverless technologies such as AWS Lambda, API Gateway and CloudFront.
Do we need servers?
Do we need regions?
No.
Droplr Serverless Revolution - How we killed 50 servers in a yearAntoni Orfin
Droplr is transitioning their infrastructure from traditional servers to serverless architectures to reduce complexity, improve maintainability, and increase performance. They have moved many of their services to AWS Lambda, including scheduled jobs, event-driven processes, and HTTP microservices. This has allowed them to simplify their stack, improve scalability, and reduce costs. While serverless architectures require some adjustments, Droplr has seen benefits from their transition and plans to expand their use of serverless and further optimize their infrastructure.
A Year of Droplr Cloud Architecture Evolution with AWS and ServerlessAntoni Orfin
This document summarizes Droplr's evolution to a serverless cloud architecture on AWS over the past year. Some key points:
- Droplr moved from multiple programming languages and microservices to primarily using Node.js and monolith architecture.
- Monitoring was optimized using services like Pingdom, DataDog, Logz.io and Sentry.
- AWS Lambda was adopted for its cost effectiveness, scalability and reliability. It allowed Droplr to reduce costs by 60% and deploy updates in minutes.
- The current architecture uses Lambda with VPC, CloudFront, API Gateway, SNS and CloudWatch Events. Serverless improved performance and reduced response times.
Presentation shows how at Droplr we consider the DevOps role.
It covers topics related with Amazon Web Services, Infrastructure as Code concept (with tools like Terraform and Ansible) and going into Continuous Deployment as the key of making our company the most competitive on the market.
Presentation from the 8th Wrocław's DevOps Meeting which took place on 28.03.2017.
beSharp a serverless approach to big data on awsClaudio Pontili
Claudio Pontili, a senior cloud solution architect at beSharp, presented on using serverless architectures for big data on AWS. He discussed using Lambda for ETL processes and Glue for managed ETL jobs. He also covered CI/CD for deploying Lambda and Glue code, data warehousing on Aurora Serverless v1, and a fully serverless big data architecture. Some key learnings included using serverless for high availability and scalability with no effort, pausing Aurora Serverless v1 clusters when not in use, and using infrastructure as code to deploy architectures.
Serverless architectures are promising and will play an important role in the coming years but the ecosystem around serverless is still pretty young. We have been operating Lambda based applications for about a year and faced several challenges. In this presentation we share these challenges and propose some solutions to work around them.
This document discusses deploying web services using AWS Lambda. It begins with an agenda that covers Lambda essentials, creating Lambda code, limitations of Lambda, a demo, event-driven architecture, and Q&A. The document then discusses what Lambda is, Lambda essentials like memory allocation and supported languages, a "Hello World" example, how to deploy a Lambda function from the command line, event sources for Lambda, Lambda limitations, security, a demo of a file sharing app using Lambda, event-driven architecture, pricing, deployment frameworks, and concludes with thanking the audience and asking for questions.
The Reinvent 2016 conference hosted by Amazon Web Services included keynotes, over 400 sessions across 4 locations over 5 days. New services and updates were announced across compute, analytics, database, developer tools, artificial intelligence, monitoring, migration, mobile, containers, and lambda. Significant announcements included new instance types, elastic GPUs, IPv6 support for EC2, Athena for querying S3 data with SQL, Glue for data integration and transformations, and expanded capabilities for many existing services like Lambda, CloudFront, and Snowmobile for large data transfers.
Ford's AWS Service Update - March 2020 (Richmond AWS User Group)Ford Prior
1. The document summarizes new and updated AWS services from February 15th to March 5th, 2020 that were presented to an AWS user group.
2. Key updates include EKS availability in new regions, improved EBS-optimized EC2 instances, secrets management integration with ECS, and new IAM condition keys.
3. The summary also briefly outlines updates to several other AWS services including EBS, Lambda, Connect, Transcribe, Greengrass, IoT Core, Control Tower, Bottlerocket, Rekognition, Aurora, and more.
Serverless Microservices w/ AWS Lambda and node.jsFrank Valcarcel
This document discusses serverless microservices using AWS Lambda and Node.js. It begins by explaining that AWS Lambda is a serverless computing platform that runs code without provisioning or managing servers. It then discusses how serverless architectures can be used to build microservices and outlines some of the pros and cons of AWS Lambda, including event-driven automatic scaling, on-demand pricing model, and resource limits. It concludes by providing examples of serverless use cases and architectures using AWS Lambda and other AWS services.
What We Learned From Building a Modern Messaging and Streaming System for CloudStreamNative
Sijie Guo discusses lessons learned from building Apache Pulsar, a modern messaging and streaming system. Key points include:
- Pulsar was designed for the cloud-native era, separating compute and storage for scalability unlike earlier systems designed for on-premise use.
- It supports unified messaging and streaming with a single API and multi-protocol support like Kafka, AMQP, and MQTT.
- Features like multi-tenancy, geo-replication, and infinite stream storage help support use cases like multi-cloud, hybrid cloud, and managing entire data lifecycles.
- The talk reflects on how Pulsar's architecture was influenced by trends in cloud computing,
AWS to Bare Metal: Motivation, Pitfalls, and ResultsMongoDB
Like many startups, Wish grew up on AWS. As our cluster grew and the price of SSDs fell, we started exploring bare metal. Fast-forward 2 years and we have hundreds of MongoDB instances on bare metal fully integrated with our AWS infrastructure. It wasn't all smooth sailing, but the performance & cost improvements were worth it! Hear the story of how we did it and gain a framework for thinking about how to make the leap from cloud-centric architecture to a hybrid model.
This document discusses Google Kubernetes Engine (GKE). It introduces containers and Kubernetes, then summarizes GKE as a container platform that fully manages master nodes. GKE provides automated operations like cluster autoscaling and node auto-repair. It allows creating multiple node pools with different configurations. GKE also enables high availability clusters across zones and monitoring with Stackdriver. Demos show using GKE to run game servers and implementing continuous integration and delivery pipelines.
What does programming without servers look like? What are the possibilities? And how does it work? Wojciech Gawroński (Pattern Match) told us about it during the third meeting of Serverless User Group Poland, which took place on 27/09/2018 in Warsaw.
Wojtek's social media:
LinkedIN https://www.linkedin.com/in/afronski/
www https://pattern-match.com/
Serverless UG Poland
Facebook https://bit.ly/2zHuJeo
Pytheas is a web-based resource and UI framework for dashboards, web consoles, and exploring structured and unstructured data. It is based on open source frameworks like Guice, Jersey, FreeMarker, jQuery, and uses a modular design. Conformity Monkey helps keep cloud instances and clusters following best practices by using a mark and notify approach with customizable rules and rule sets. Zuul is Netflix's edge tier service that acts on HTTP requests using dynamic filters written in Groovy. Genie provides an abstraction of physical Hadoop clusters and a simple API to run jobs on them. Lipstick provides a visualization of Pig workflows. ICE is a tool for analyzing AWS usage data by tagging billing files and providing a
EC2 and S3 are core AWS services. EC2 provides virtual servers and S3 provides cloud storage. EC2 instances run on different hardware types and can be configured with operating systems and software. S3 stores files and objects accessed via unique buckets. EBS provides persistent block storage volumes for EC2 instances, while S3 provides scalable cloud storage. VPC allows creation of virtual private networks within AWS.
The document provides an overview of serverless computing and deploying Python applications using AWS Lambda. It discusses how serverless computing removes the need to manage servers and allows scaling without capacity planning. The rest of the document demonstrates how to deploy a Python application on AWS Lambda using the Zappa framework. It shows how Zappa handles packaging code and dependencies, deployment, and management of Lambda functions and API Gateway configuration. Some potential issues with serverless like cold starts and limitations on function duration are also covered.
With Docker it became easy to start applications locally without installing any dependencies. Even running a local cluster is not a big thing anymore. AWS on the other side offers with ECS a managed container service that states to schedule containers based on resource needs, isolation policies and availability requirements. But what happens between? Is it really that easy? In this talk you’ll see which existing services can already be used to deploy your containers automatically and what still needs to be done to get them running on AWS.
Mit Docker ist es einfach geworden, Applikationen lokal zu starten, ohne zusätzliche Abhängigkeiten installieren zu müssen. Einen Cluster auf seinem eigenen Rechner laufen zu lassen ist kein großes Ding mehr. Mit ECS bietet AWS einen Container-Management-Service für die Cloud an, der verspricht, Container entsprechend ihrem Ressourcenbedarf und Verfügbarkeitserfordernissen automatisch im Cluster zu platzieren.
Aber was passiert dazwischen? Und ist es wirklich so einfach?
In diesem Talk werden wir betrachten, welche existierenden Services von AWS verwendet werden können, um Container automatisch zu deployen, und was zusätzlich alles benötigt wird, um sie im Betrieb laufen zu lassen.
Kubernetes and AWS Lambda can play nicely togetherEdward Wilde
Vendor lock-in can be a worry for many engineers . A new innovative approach, will for the first time, allow open-source serverless to run on AWS Lambda or Kubernetes using the same deployment artefact, packaged using the tools we love: containers.
OpenFaaS is an open-source function as a service (FaaS) platform on the [CNCF serverless landscape](https://landscape.cncf.io/format=serverless).
With OpenFaaS you can package anything as a serverless function and deploy to Kubernetes using containers. Due to UNIX-like primitives in the core architecture, it was possible to extend the system to run functions on both Kubernetes and AWS Lambda depending on user preference. The core components of OpenFaaS still run on Kubernetes but the functions are deployed and invoked on AWS Lambda
This document summarizes a chatting server built on AWS. It describes using DynamoDB with a Python wrapper for data storage. It then discusses testing latency between UCloud and AWS AP-Northeast-1 regions for packet delivery. The live chatting functionality is shown to use EC2 instances, ElastiCache Redis, and load balancing. Logging of messages is implemented with SQS and DynamoDB. Push notifications on new messages uses SNS. An API allows controlling the chat server via commands sent to SQS.
This document discusses how Between, a mobile app for couples, migrated their photo architecture to reduce storage costs by 70%. The old architecture generated and stored multiple thumbnail sizes for each photo, using 6.6 billion thumbnails and 738 TB of storage. The new architecture resizes thumbnails on demand using the fast Skia library, saving the original high resolution photos as smaller WebP files. Migration of 1.1 billion existing photos to the new system took 4 days using Spot instances. This reduced storage usage by 75% to 184 TB and number of S3 objects by 82%, cutting total photo costs by 68%.
Alex Casalboni and Austen Collins discuss the evolution of Serverless. Learn about the exciting new trend that's redefining the cloud computing industry in this in-depth webinar designed to teach you the basics of serverless computing and design.
In this session Arash will show you how to use Open Cloud service delivery models such as Open IaaS and Open PaaS to deploy OpenCms as a service for your organization or your customers. You will learn how Open Source cloud operating systems and platforms such as OpenStack and Cloud Foundry can help jumping and scaling between OpenCms content clouds. Arash will also compare other PaaS solutions like AppScale, CloudBees, OpenShift and Jelastic and show if and how OpenCms can work with them. He will introduce you to the Cloud Federation concept, which helps to avoid vendor lock-in with private, public and hybrid cloud environments. Last but not least, he will explain how to achieve a high level of data security in Open Clouds, so that even system administrators won’t be able to access your OpenCms data. This session is targeted at all types of OpenCms users, such as business users, service providers and developers.
OpenSource API Server based on Node.js API framework built on supported Node.js platform with Tooling and DevOps. Use cases are Omni-channel API Server, Mobile Backend as a Service (mBaaS) or Next Generation Enterprise Service Bus. Key functionality include built in enterprise connectors, ORM, Offline Sync, Mobile and JS SDKs, Isomorphic JavaScript and Graphical API creation tool.
AWS re:Invent 2016 : announcement, technical demos and feedbacksEmmanuel Quentin
Slides of our intervention with Mathieu Mailhos about re:Invent 2016 :
- Annoucements
- Technical demonstration of Athena, monitoring via Lambda and step function
- Feedbacks
Scripts available here : https://gist.github.com/manuquentin/adee523b60a4723e9e4819ea69713ab6
Cloud for Developers: Azure vs. Google App Engine vs. Amazon vs. AppHarborSvetlin Nakov
Software Development for the Public Cloud Platforms: Windows Azure vs. Google App Engine vs. Amazon Web Services (AWS) vs AppHarbor.
In this talk the speaker will compare the most widely used public PaaS clouds (Azure, GAE and AWS) from the software developer’s perspective.
A parallel between Azure, GAE, AWS and few other clouds (like AppHarbor, Heroku, Cloudfoundry and AppForce) will be made based on several criteria: architecture, pricing, storage services (non-relational databases, relational databases in the cloud and blob/file storage), business-tier services (like queues, notifications, email, CDN, etc.), supported languages, platforms and frameworks and front-end technologies.
A live demo will be made to compare the way we build and deploy a multi-tiered application in Azure, Amazon and GAE and how to implement its back-end (using a cloud database), business tier (based on REST services) and front-end (based on HTML5).
The speaker Svetlin Nakov (http://www.nakov.com) is well-known software development expert and trainer, a head of the Telerik Software Academy and a main organizer of the Cloud Development course (http://clouddevcourse.telerik.com).
Serverless Microservices w/ AWS Lambda and node.jsFrank Valcarcel
This document discusses serverless microservices using AWS Lambda and Node.js. It begins by explaining that AWS Lambda is a serverless computing platform that runs code without provisioning or managing servers. It then discusses how serverless architectures can be used to build microservices and outlines some of the pros and cons of AWS Lambda, including event-driven automatic scaling, on-demand pricing model, and resource limits. It concludes by providing examples of serverless use cases and architectures using AWS Lambda and other AWS services.
What We Learned From Building a Modern Messaging and Streaming System for CloudStreamNative
Sijie Guo discusses lessons learned from building Apache Pulsar, a modern messaging and streaming system. Key points include:
- Pulsar was designed for the cloud-native era, separating compute and storage for scalability unlike earlier systems designed for on-premise use.
- It supports unified messaging and streaming with a single API and multi-protocol support like Kafka, AMQP, and MQTT.
- Features like multi-tenancy, geo-replication, and infinite stream storage help support use cases like multi-cloud, hybrid cloud, and managing entire data lifecycles.
- The talk reflects on how Pulsar's architecture was influenced by trends in cloud computing,
AWS to Bare Metal: Motivation, Pitfalls, and ResultsMongoDB
Like many startups, Wish grew up on AWS. As our cluster grew and the price of SSDs fell, we started exploring bare metal. Fast-forward 2 years and we have hundreds of MongoDB instances on bare metal fully integrated with our AWS infrastructure. It wasn't all smooth sailing, but the performance & cost improvements were worth it! Hear the story of how we did it and gain a framework for thinking about how to make the leap from cloud-centric architecture to a hybrid model.
This document discusses Google Kubernetes Engine (GKE). It introduces containers and Kubernetes, then summarizes GKE as a container platform that fully manages master nodes. GKE provides automated operations like cluster autoscaling and node auto-repair. It allows creating multiple node pools with different configurations. GKE also enables high availability clusters across zones and monitoring with Stackdriver. Demos show using GKE to run game servers and implementing continuous integration and delivery pipelines.
What does programming without servers look like? What are the possibilities? And how does it work? Wojciech Gawroński (Pattern Match) told us about it during the third meeting of Serverless User Group Poland, which took place on 27/09/2018 in Warsaw.
Wojtek's social media:
LinkedIN https://www.linkedin.com/in/afronski/
www https://pattern-match.com/
Serverless UG Poland
Facebook https://bit.ly/2zHuJeo
Pytheas is a web-based resource and UI framework for dashboards, web consoles, and exploring structured and unstructured data. It is based on open source frameworks like Guice, Jersey, FreeMarker, jQuery, and uses a modular design. Conformity Monkey helps keep cloud instances and clusters following best practices by using a mark and notify approach with customizable rules and rule sets. Zuul is Netflix's edge tier service that acts on HTTP requests using dynamic filters written in Groovy. Genie provides an abstraction of physical Hadoop clusters and a simple API to run jobs on them. Lipstick provides a visualization of Pig workflows. ICE is a tool for analyzing AWS usage data by tagging billing files and providing a
EC2 and S3 are core AWS services. EC2 provides virtual servers and S3 provides cloud storage. EC2 instances run on different hardware types and can be configured with operating systems and software. S3 stores files and objects accessed via unique buckets. EBS provides persistent block storage volumes for EC2 instances, while S3 provides scalable cloud storage. VPC allows creation of virtual private networks within AWS.
The document provides an overview of serverless computing and deploying Python applications using AWS Lambda. It discusses how serverless computing removes the need to manage servers and allows scaling without capacity planning. The rest of the document demonstrates how to deploy a Python application on AWS Lambda using the Zappa framework. It shows how Zappa handles packaging code and dependencies, deployment, and management of Lambda functions and API Gateway configuration. Some potential issues with serverless like cold starts and limitations on function duration are also covered.
With Docker it became easy to start applications locally without installing any dependencies. Even running a local cluster is not a big thing anymore. AWS on the other side offers with ECS a managed container service that states to schedule containers based on resource needs, isolation policies and availability requirements. But what happens between? Is it really that easy? In this talk you’ll see which existing services can already be used to deploy your containers automatically and what still needs to be done to get them running on AWS.
Mit Docker ist es einfach geworden, Applikationen lokal zu starten, ohne zusätzliche Abhängigkeiten installieren zu müssen. Einen Cluster auf seinem eigenen Rechner laufen zu lassen ist kein großes Ding mehr. Mit ECS bietet AWS einen Container-Management-Service für die Cloud an, der verspricht, Container entsprechend ihrem Ressourcenbedarf und Verfügbarkeitserfordernissen automatisch im Cluster zu platzieren.
Aber was passiert dazwischen? Und ist es wirklich so einfach?
In diesem Talk werden wir betrachten, welche existierenden Services von AWS verwendet werden können, um Container automatisch zu deployen, und was zusätzlich alles benötigt wird, um sie im Betrieb laufen zu lassen.
Kubernetes and AWS Lambda can play nicely togetherEdward Wilde
Vendor lock-in can be a worry for many engineers . A new innovative approach, will for the first time, allow open-source serverless to run on AWS Lambda or Kubernetes using the same deployment artefact, packaged using the tools we love: containers.
OpenFaaS is an open-source function as a service (FaaS) platform on the [CNCF serverless landscape](https://landscape.cncf.io/format=serverless).
With OpenFaaS you can package anything as a serverless function and deploy to Kubernetes using containers. Due to UNIX-like primitives in the core architecture, it was possible to extend the system to run functions on both Kubernetes and AWS Lambda depending on user preference. The core components of OpenFaaS still run on Kubernetes but the functions are deployed and invoked on AWS Lambda
This document summarizes a chatting server built on AWS. It describes using DynamoDB with a Python wrapper for data storage. It then discusses testing latency between UCloud and AWS AP-Northeast-1 regions for packet delivery. The live chatting functionality is shown to use EC2 instances, ElastiCache Redis, and load balancing. Logging of messages is implemented with SQS and DynamoDB. Push notifications on new messages uses SNS. An API allows controlling the chat server via commands sent to SQS.
This document discusses how Between, a mobile app for couples, migrated their photo architecture to reduce storage costs by 70%. The old architecture generated and stored multiple thumbnail sizes for each photo, using 6.6 billion thumbnails and 738 TB of storage. The new architecture resizes thumbnails on demand using the fast Skia library, saving the original high resolution photos as smaller WebP files. Migration of 1.1 billion existing photos to the new system took 4 days using Spot instances. This reduced storage usage by 75% to 184 TB and number of S3 objects by 82%, cutting total photo costs by 68%.
Alex Casalboni and Austen Collins discuss the evolution of Serverless. Learn about the exciting new trend that's redefining the cloud computing industry in this in-depth webinar designed to teach you the basics of serverless computing and design.
In this session Arash will show you how to use Open Cloud service delivery models such as Open IaaS and Open PaaS to deploy OpenCms as a service for your organization or your customers. You will learn how Open Source cloud operating systems and platforms such as OpenStack and Cloud Foundry can help jumping and scaling between OpenCms content clouds. Arash will also compare other PaaS solutions like AppScale, CloudBees, OpenShift and Jelastic and show if and how OpenCms can work with them. He will introduce you to the Cloud Federation concept, which helps to avoid vendor lock-in with private, public and hybrid cloud environments. Last but not least, he will explain how to achieve a high level of data security in Open Clouds, so that even system administrators won’t be able to access your OpenCms data. This session is targeted at all types of OpenCms users, such as business users, service providers and developers.
OpenSource API Server based on Node.js API framework built on supported Node.js platform with Tooling and DevOps. Use cases are Omni-channel API Server, Mobile Backend as a Service (mBaaS) or Next Generation Enterprise Service Bus. Key functionality include built in enterprise connectors, ORM, Offline Sync, Mobile and JS SDKs, Isomorphic JavaScript and Graphical API creation tool.
AWS re:Invent 2016 : announcement, technical demos and feedbacksEmmanuel Quentin
Slides of our intervention with Mathieu Mailhos about re:Invent 2016 :
- Annoucements
- Technical demonstration of Athena, monitoring via Lambda and step function
- Feedbacks
Scripts available here : https://gist.github.com/manuquentin/adee523b60a4723e9e4819ea69713ab6
Cloud for Developers: Azure vs. Google App Engine vs. Amazon vs. AppHarborSvetlin Nakov
Software Development for the Public Cloud Platforms: Windows Azure vs. Google App Engine vs. Amazon Web Services (AWS) vs AppHarbor.
In this talk the speaker will compare the most widely used public PaaS clouds (Azure, GAE and AWS) from the software developer’s perspective.
A parallel between Azure, GAE, AWS and few other clouds (like AppHarbor, Heroku, Cloudfoundry and AppForce) will be made based on several criteria: architecture, pricing, storage services (non-relational databases, relational databases in the cloud and blob/file storage), business-tier services (like queues, notifications, email, CDN, etc.), supported languages, platforms and frameworks and front-end technologies.
A live demo will be made to compare the way we build and deploy a multi-tiered application in Azure, Amazon and GAE and how to implement its back-end (using a cloud database), business tier (based on REST services) and front-end (based on HTML5).
The speaker Svetlin Nakov (http://www.nakov.com) is well-known software development expert and trainer, a head of the Telerik Software Academy and a main organizer of the Cloud Development course (http://clouddevcourse.telerik.com).
AWS re:Invent 2016: NEW LAUNCH! AWS announced several new services and capabilities including elastic GPUs for EC2, new EC2 instance types like T2.xlarge and T2.2xlarge, next generation R4 memory optimized instances, programmable F1 instances, Amazon Lightsail for simple virtual private servers, interactive queries on data in S3 with Amazon Athena, image and facial recognition with Amazon Rekognition, text-to-speech with Amazon Polly, natural language processing with Amazon Lex, PostgreSQL compatibility for Amazon Aurora, local compute and messaging for connected devices with AWS Greengrass, petabyte-scale data transport with storage and compute using AWS Snowball Edge and AWS Snow
This document compares and contrasts the cloud platforms AWS, Azure, and GCP. It provides information on each platform's pillars of cloud services, regions and availability zones, instance types, databases, serverless computing options, networking, analytics and machine learning services, development tools, security features, and pricing models. Speakers then provide more details on their experience with each platform, highlighting key products, differences between the platforms, and positives and negatives of each from their perspective.
During the session we'll talk about IoT Solution based on Azure & AWS which is under active development phase at the moment. We will review product architecture and compare implementations on both of the cloud platforms as well as briefly take a look to the possible evolvements of the architecture to cover future needs. Also I'll share the main problems we've faced in during development process as well as cover solutions to them.
Introduction of AWS Cloud Computing and its future for Biometric DepartmentKevin Lee
When statistical programmers or statisticians starts in open-source programming, we usually begin with installing Python and/or R on our local computer and writing codes in a local IDE such as Jupyter notebook or RStudio, but as biometric team grow, and advanced analytics become more prevalent, collaborative solutions and environments are needed. Traditional solutions have been SAS® servers, but nowadays, there is a growing need and interest for Cloud Computing. The paper is written for those who want to know about the Cloud Computing environment (e.g., AWS) and its possible implementation for the Biometric Department.
The paper will start with the main components of Cloud computing – databases, servers, applications, data analytics, reports, visualization, dashboards etc., and its benefits - Elasticity, Control, Flexibility, Integration, Reliability, Security, Inexpensive and Easy to Start. Most popular Cloud computing platforms are AWS, Google Cloud and Microsoft Azure, and this paper will introduce AWS Cloud Computing Environment.
The paper will also introduce the core technologies of AWS Cloud Computing – computing (EC2), Storage ( EBS, EFS, S3), Database ( Redshift, RDS, DynamoDB ), Security (IAM) and Networking (VPC ), and how they could be integrated to support modern-day data analytics.
Finally, the paper will introduce the department-driven Cloud computing transition project that the whole SAS programming department has moved from SAS Window Server into AWS Cloud Computing. It will also discuss the challenges, and the lessons learn and its future in the Biometric department
Adrian Cockcroft discusses the challenges of building reliable cloud services in an imperfect environment. He describes Netflix's approach of using microservices, continuous delivery, and automation to create stability. Cockcroft also introduces NetflixOSS, an open source platform that provides libraries and tools to help other companies adopt this "cloud native" architecture. The talk outlines opportunities to improve portability and foster an ecosystem around NetflixOSS.
This document provides an overview of a workshop on cloud native, capacity, performance and cost optimization tools and techniques. It begins with introducing the difference between a presentation and workshop. It then discusses introducing attendees, presenting on various cloud native topics like migration paths and operations tools, and benchmarking Cassandra performance at scale across AWS regions. The goal is to explore cloud native techniques while discussing specific problems attendees face.
Aws-What You Need to Know_Simon ElishaHelen Rogers
This document provides an overview of AWS services and capabilities over time. It discusses:
- The rapid growth in the number of AWS services from 2010 to 2017, indicating AWS's focus on innovation.
- The wide range of services available across computing, storage, databases, analytics, developer tools, management and security categories to support all types of workloads.
- New capabilities in 2017 including P2 GPU instance types for machine learning, Amazon Rekognition visual recognition service, and serverless computing using AWS Lambda.
The document discusses the three phases of major galactic civilizations: survival, inquiry, and sophistication. It then summarizes Mark Slodge's presentation on using C# for cloud to mobile applications. The presentation covers using C# for the cloud backend, clients on various platforms, and communication between the cloud and clients. It provides examples of applications built with C# and Azure and discusses technologies like Mobile Services.
We, as KKStream / KKTV / KKBOX, just kicked off the 1st sharing session inside our organization, introducing the event, the new services and potentially some of our insights and opinions. Let's keep fingers crossed for the following deeper sessions.
Journey Towards Scaling Your Application to Million UsersAdrian Hornsby
1) The document discusses scaling a web application from basic static hosting to serving millions of users on AWS, including strategies like serverless architectures, auto-scaling, caching, messaging queues, and database sharding.
2) It provides an overview of AWS services that can be used at different stages of scaling, from basic S3 hosting to load balancing, caching with ElastiCache, auto-scaling groups, and serverless architectures using Lambda and API Gateway.
3) The document outlines an example progression of an application from version 0.1 with a single EC2 instance to version 0.7 with decoupled and event-driven architectures, discussing strategies for scaling databases, adding asynchronous processing, and implementing micro
게임을 위한 Cloud Native on AWS
IT 기술이 변화하며 클라우드를 보다 적극적으로 사용하는 게임사가 늘어나는 추세입니다. 게임 고객분들이 다양한 시각에서 AWS Cloud Service를 보다 효과적으로 잘 사용할 수 있는 방법을 소개합니다. 또한, 고객분들께서 개발에 집중하고 효율적으로 운영할 수 있도록 AWS가 어떠한 도움을 드리는지에 대해 말씀드리고자 합니다.
This document provides an overview of AWS (Amazon Web Services) and cloud computing. It discusses key concepts like utility computing, pay as you go pricing, global availability, and elasticity. It also describes AWS's global infrastructure including 11 regions, 30 availability zones, and 53 edge locations. Finally, it lists and briefly explains various AWS computing, database, storage, analytics and other services that can be utilized on demand via the cloud.
Presentation from SysOps/DevOps Wrocław MeetUp #8 (28.03.2019). I was sharing my experience of introducing DevOps in projects of various size. We started with agile ways of working with architecture (evolutionary approaches, ADR), talked about introduction of continuous security practices (DevSecOps) and ended with the ways of final business release of our applications.
Presentation shows how to use Apache Benchmark and JMeter to run load-tests. It also shows how to collect metrics from Google Analytics that are needed to configure your tests.
Testowanie poziomu bezpieczeństwa aplikacji internetowychAntoni Orfin
Niniejsza praca ma za zadanie przedstawić zagrożenia związane z bezpieczeństwem aplikacji internetowych.
Omawia najpowszechniejsze rodzaje zagrożeń, przykłady podatności i sposoby ochrony. Pozwoli na zapoznanie się z ogólnymi zasadami, którymi powinny kierować się osoby odpowiedzialne za wytwarzanie systemów webowych. Jest również bazą która pozwoli skuteczniej przeprowadzać audyty bezpieczeństwa.
Projektowanie wysokowydajnych i skalowalnych serwisów WWW - Warstwa danychAntoni Orfin
Część druga prezentacji pochodzącej z warsztatów skupiających się na zagadnieniach projektowania i wytwarzania wysokowydajnych i skalowalnych serwisów webowych.
Prezentacja opisuje problemy związane z warstwą danych:
- Replikacja (master-master, master-slave)
- Partycjonowanie (sharding)
- Wydajne przechowywanie danych (agregacja, denormalizacja)
Projektowanie wysokowydajnych i skalowalnych serwisów WWW - Warstwa aplikacjiAntoni Orfin
Część pierwsza prezentacji pochodzącej z warsztatów skupiających się na zagadnieniach projektowania i wytwarzania wysokowydajnych i skalowalnych serwisów webowych.
Prezentacja opisuje problemy związane z warstwą aplikacji:
- Rodzaje skalowania
- Architektury nastawione na zapewnienie wysokiej wydajności i skalowalności
- Zagadnienia Load-Balancingu
- Metody cache'owanie - n-Tier Cache, Varnish, Redis
- Service Oriented Architecture
This document discusses Elasticsearch and its uses for search. It describes how Elasticsearch can be used for intelligent search engines, autocomplete, geo-search, and search by colors. It then covers the basics of how Elasticsearch works, including its architecture with nodes, shards, and replicas. The document also outlines how to map and index documents, perform searches using queries and filters, and generate analytics through aggregations.
Concept of Problem Solving, Introduction to Algorithms, Characteristics of Algorithms, Introduction to Data Structure, Data Structure Classification (Linear and Non-linear, Static and Dynamic, Persistent and Ephemeral data structures), Time complexity and Space complexity, Asymptotic Notation - The Big-O, Omega and Theta notation, Algorithmic upper bounds, lower bounds, Best, Worst and Average case analysis of an Algorithm, Abstract Data Types (ADT)
International Journal of Distributed and Parallel systems (IJDPS)samueljackson3773
The growth of Internet and other web technologies requires the development of new
algorithms and architectures for parallel and distributed computing. International journal of
Distributed and parallel systems is a bimonthly open access peer-reviewed journal aims to
publish high quality scientific papers arising from original research and development from
the international community in the areas of parallel and distributed systems. IJDPS serves
as a platform for engineers and researchers to present new ideas and system technology,
with an interactive and friendly, but strongly professional atmosphere.
π0.5: a Vision-Language-Action Model with Open-World GeneralizationNABLAS株式会社
今回の資料「Transfusion / π0 / π0.5」は、画像・言語・アクションを統合するロボット基盤モデルについて紹介しています。
拡散×自己回帰を融合したTransformerをベースに、π0.5ではオープンワールドでの推論・計画も可能に。
This presentation introduces robot foundation models that integrate vision, language, and action.
Built on a Transformer combining diffusion and autoregression, π0.5 enables reasoning and planning in open-world settings.
☁️ GDG Cloud Munich: Build With AI Workshop - Introduction to Vertex AI! ☁️
Join us for an exciting #BuildWithAi workshop on the 28th of April, 2025 at the Google Office in Munich!
Dive into the world of AI with our "Introduction to Vertex AI" session, presented by Google Cloud expert Randy Gupta.
Value Stream Mapping Worskshops for Intelligent Continuous SecurityMarc Hornbeek
This presentation provides detailed guidance and tools for conducting Current State and Future State Value Stream Mapping workshops for Intelligent Continuous Security.
Fluid mechanics is the branch of physics concerned with the mechanics of fluids (liquids, gases, and plasmas) and the forces on them. Originally applied to water (hydromechanics), it found applications in a wide range of disciplines, including mechanical, aerospace, civil, chemical, and biomedical engineering, as well as geophysics, oceanography, meteorology, astrophysics, and biology.
It can be divided into fluid statics, the study of various fluids at rest, and fluid dynamics.
Fluid statics, also known as hydrostatics, is the study of fluids at rest, specifically when there's no relative motion between fluid particles. It focuses on the conditions under which fluids are in stable equilibrium and doesn't involve fluid motion.
Fluid kinematics is the branch of fluid mechanics that focuses on describing and analyzing the motion of fluids, such as liquids and gases, without considering the forces that cause the motion. It deals with the geometrical and temporal aspects of fluid flow, including velocity and acceleration. Fluid dynamics, on the other hand, considers the forces acting on the fluid.
Fluid dynamics is the study of the effect of forces on fluid motion. It is a branch of continuum mechanics, a subject which models matter without using the information that it is made out of atoms; that is, it models matter from a macroscopic viewpoint rather than from microscopic.
Fluid mechanics, especially fluid dynamics, is an active field of research, typically mathematically complex. Many problems are partly or wholly unsolved and are best addressed by numerical methods, typically using computers. A modern discipline, called computational fluid dynamics (CFD), is devoted to this approach. Particle image velocimetry, an experimental method for visualizing and analyzing fluid flow, also takes advantage of the highly visual nature of fluid flow.
Fundamentally, every fluid mechanical system is assumed to obey the basic laws :
Conservation of mass
Conservation of energy
Conservation of momentum
The continuum assumption
For example, the assumption that mass is conserved means that for any fixed control volume (for example, a spherical volume)—enclosed by a control surface—the rate of change of the mass contained in that volume is equal to the rate at which mass is passing through the surface from outside to inside, minus the rate at which mass is passing from inside to outside. This can be expressed as an equation in integral form over the control volume.
The continuum assumption is an idealization of continuum mechanics under which fluids can be treated as continuous, even though, on a microscopic scale, they are composed of molecules. Under the continuum assumption, macroscopic (observed/measurable) properties such as density, pressure, temperature, and bulk velocity are taken to be well-defined at "infinitesimal" volume elements—small in comparison to the characteristic length scale of the system, but large in comparison to molecular length scale
"Boiler Feed Pump (BFP): Working, Applications, Advantages, and Limitations E...Infopitaara
A Boiler Feed Pump (BFP) is a critical component in thermal power plants. It supplies high-pressure water (feedwater) to the boiler, ensuring continuous steam generation.
⚙️ How a Boiler Feed Pump Works
Water Collection:
Feedwater is collected from the deaerator or feedwater tank.
Pressurization:
The pump increases water pressure using multiple impellers/stages in centrifugal types.
Discharge to Boiler:
Pressurized water is then supplied to the boiler drum or economizer section, depending on design.
🌀 Types of Boiler Feed Pumps
Centrifugal Pumps (most common):
Multistage for higher pressure.
Used in large thermal power stations.
Positive Displacement Pumps (less common):
For smaller or specific applications.
Precise flow control but less efficient for large volumes.
🛠️ Key Operations and Controls
Recirculation Line: Protects the pump from overheating at low flow.
Throttle Valve: Regulates flow based on boiler demand.
Control System: Often automated via DCS/PLC for variable load conditions.
Sealing & Cooling Systems: Prevent leakage and maintain pump health.
⚠️ Common BFP Issues
Cavitation due to low NPSH (Net Positive Suction Head).
Seal or bearing failure.
Overheating from improper flow or recirculation.
Lidar for Autonomous Driving, LiDAR Mapping for Driverless Cars.pptxRishavKumar530754
LiDAR-Based System for Autonomous Cars
Autonomous Driving with LiDAR Tech
LiDAR Integration in Self-Driving Cars
Self-Driving Vehicles Using LiDAR
LiDAR Mapping for Driverless Cars
The role of the lexical analyzer
Specification of tokens
Finite state machines
From a regular expressions to an NFA
Convert NFA to DFA
Transforming grammars and regular expressions
Transforming automata to grammars
Language for specifying lexical analyzers
ADVXAI IN MALWARE ANALYSIS FRAMEWORK: BALANCING EXPLAINABILITY WITH SECURITYijscai
With the increased use of Artificial Intelligence (AI) in malware analysis there is also an increased need to
understand the decisions models make when identifying malicious artifacts. Explainable AI (XAI) becomes
the answer to interpreting the decision-making process that AI malware analysis models use to determine
malicious benign samples to gain trust that in a production environment, the system is able to catch
malware. With any cyber innovation brings a new set of challenges and literature soon came out about XAI
as a new attack vector. Adversarial XAI (AdvXAI) is a relatively new concept but with AI applications in
many sectors, it is crucial to quickly respond to the attack surface that it creates. This paper seeks to
conceptualize a theoretical framework focused on addressing AdvXAI in malware analysis in an effort to
balance explainability with security. Following this framework, designing a machine with an AI malware
detection and analysis model will ensure that it can effectively analyze malware, explain how it came to its
decision, and be built securely to avoid adversarial attacks and manipulations. The framework focuses on
choosing malware datasets to train the model, choosing the AI model, choosing an XAI technique,
implementing AdvXAI defensive measures, and continually evaluating the model. This framework will
significantly contribute to automated malware detection and XAI efforts allowing for secure systems that
are resilient to adversarial attacks.
6. 18 Regions
52 Availability Zones
>100 available services
Compute, Storage, Database,
Networking/CDN, AR/VR, IoT,
Analytics, Security…
Amazon Web
Services
7. 15 Regions
44 Availability Zones
>50 available services
Compute, Storage, Database,
Networking/CDN, Big Data, Machine
Learning, APIs (Google Translate,
Image Recognition etc.)...
Google Cloud
Platform
8. 36 Regions
46 Availability Zones (!!!)
>100 available services
Compute, Networking, Storage,
Containers, Databases, Analytics, AI +
Machine Learning, IoT, Security
Microsoft Azure
9. 1 Region (Warsaw/Atman)
5 Availability Zones
<10 available services
Compute, Volume Storage, Object
Storage, Databases, Networking,
Monitoring
Oktawave
20. Serverless Revolution
AWS Lambda - run code without thinking about underlying
infrastructure
1. Crazy cheap
2. Multiple programming languages support
3. Highly-Scalable
4. Reliable
5. Fast
21. Serverless Revolution
AWS Lambda - run code without thinking about underlying
infrastructure
1. Crazy cheap
Free Tier: 1M requests per month & 400,000/GB-sec
Above: $0.0000002/request + $0.00001667/GB-sec
22. Serverless Revolution
AWS Lambda - run code without thinking about underlying
infrastructure
2. Multiple programming languages support: Node.js
(JavaScript), Python, Java (Java 8), C# (.NET Core), Go
(from 15.01.2018)
$ npm install serverless -g
$ serverless create --template hello-world
$ serverless deploy
23. Serverless Revolution
AWS Lambda - run code without thinking about underlying
infrastructure
3. Highly-Scalable out of the box
Default limit: 1,000 concurrent executions
Our Benchmark: 800 req/s per Node.js function
24. Serverless Revolution
AWS Lambda - run code without thinking about underlying
infrastructure
4. Reliable
100% uptime
in our Pingdom from start (7 months)
25. Serverless Revolution
AWS Lambda - run code without thinking about underlying
infrastructure
5. Fast
<1 ms
for Node.js Express “Hello World”
34. Thanks
Time for discussion :-)
- Serverless as a game-changer?
- Your ideas of using Serverless?
- Btw. Senior JavaScript/React Developer wanted! ;-)
Ref.: https://serverless.com/blog/how-droplr-scales-to-millions-serverless-framework/