AWS re:Invent 2016

Accelerated computing is on the rise because of massively parallel, compute-intensive workloads such as deep learning, 3D content rendering, financial computing, and engineering simulations. In this session, we provide an overview of our accelerated computing instances, including how to choose instances based on your application needs, best practices and tips to optimize performance, and specific examples of accelerated computing in real-world applications.

Every day, the computing power of high-performance computing (HPC) clusters helps scientists make breakthroughs, such as proving the existence of gravitational waves and screening new compounds for new drugs. Yet building HPC clusters is out of reach for most organizations, due to the upfront hardware costs and ongoing operational expenses. Now the speed of innovation is only bound by your imagination, not your budget. Researchers can run one cluster for 10,000 hours or 10,000 clusters for one hour anytime, from anywhere, and both cost the same in the cloud. And with the availability of Public Data Sets in Amazon S3, petabyte scale data is instantly accessible in the cloud. Attend and learn how to build HPC clusters on the fly, leverage Amazon’s Spot market pricing to minimize the cost of HPC jobs, and scale HPC jobs on a small budget, using all the same tools you use today, and a few new ones too.

AWS Step Functions is a new, fully managed service that makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Step Functions is a reliable way to coordinate components and step through the functions of your application. A graphical console helps you arrange and visualize the components of your application as a series of steps. Step Functions automatically triggers and tracks each step and retries when there are errors so that your application executes in order―and as expected―every time. This session shows how to use Step Functions to create, run, and debug multi-service applications in a matter of minutes. We also share how customers are using Step Functions to reliably build and scale multi-step applications such as order processing, report generation, and data transformation―and to innovate faster.

AWS provides unprecedented computational power for graphics-intensive applications in areas such as design, engineering simulations, and 3D content rendering. Together, Amazon EC2 Elastic GPUs and Amazon AppStream 2.0 provide the capabilities necessary for end users to access and run these applications. In this session, you learn more about Elastic GPUs and Amazon AppStream 2.0, and how you can run graphics-intensive applications on AWS. You also hear from ANSYS, a leader in engineering simulation software, and why they are moving the ANSYS Enterprise Cloud to Elastic GPUs and Amazon AppStream 2.0 to deliver a better experience for customers.

Amazon AppStream 2.0 is an application streaming service that provides users with instant access to their applications from anywhere, on any connected device. Delivering desktop apps from the cloud allows enterprises to manage user access easily and keep apps current; ISVs can reach more users across more devices. In this session, we cover best practices for streaming desktop applications from the AWS Cloud with Amazon AppStream 2.0, and show you how you can address advanced topics such as user authentication, security, and managing an application catalog.

AWS and SAP have worked together closely to certify the AWS platform so that companies of all sizes can fully realize all the benefits of the SAP HANA in-memory database platform on the AWS cloud. By placing SAP systems in the cloud, organizations are achieving greater agility, flexibility, and cost efficiency while saving resources to focus on their core businesses. We will discuss recent SAP and AWS innovations including the Amazon EC2 X1 instance type that offers up to 2TB of RAM, and dive into features of the AWS platform that bring significant flexibility to SAP HANA deployments.

AWS Batch is a fully-managed service that enables developers, scientists, and engineers to easily and efficiently run batch computing workloads of any scale on AWS. AWS Batch automatically provisions compute resources and optimizes the workload distribution based on the quantity and scale of the workloads. With AWS Batch, there is no need to install or manage batch computing software, allowing you to focus on analyzing results and solving problems. AWS Batch plans, schedules, and executes your batch computing workloads across the full range of AWS compute services and features, such as Amazon EC2, Spot Instances, and AWS Lambda. AWS Batch reduces operational complexities, saving time and reducing costs. In this session, Principal Product Managers Jamie Kinney and Dougal Ballantyne describe the core concepts behind AWS Batch and details of how the service functions. The presentation concludes with relevant use cases and sample code.

Managing the infrastructure behind your ECS cluster is an important and crucial task. In this session we will dive deep into best practices to setup and maintain your ECS cluster with tools such as CloudFormation and Auto Scaling. We will discuss how to setup monitoring and logging infrastructure for your cluster and containers. We will also detail how to tighten the security of your container instances with tools such as SELinux. We will do a live deployment of a complete microservices-based ecommerce website, using Weave Net and Weave Scope for container networking, automated service discovery, monitoring and real-time app visualization. We’ll use CloudFormation, Auto Scaling Groups and other services to show how easy it is to architect and deploy a real application on Amazon ECS with fewer moving parts. We’ll also visualize our application using a real-time map of the containers.

Keeping consistent environments across your development, test, and production systems can be a complex task. Docker containers offer a way to develop and test your application in the same environment in which it runs in production. You can use tools such as the ECS CLI and Docker Compose for local testing of applications; Jenkins and AWS CodePipeline for building and workflow orchestration; Amazon EC2 Container Registry to store your container images; and Amazon EC2 Container Service to manage and scale containers. In this session, you will learn how to build containers into your development workflow and orchestrate container deployments using Amazon ECS. You will hear how Okta runs 30,000 tests per developer commit and releases 10,000 new lines of code each week to production with a CI system based on 100% AWS services. We'll also discuss how Okta uses ECS for parallelized testing in CI and for production microservices in a multi-region, always on cloud service.

Managing and scaling hundreds of containers is a challenging task. A container management solution takes care of these challenges for you, allowing you to focus on developing your application. In this session, we cover the role and tasks of a container management solution and we analyze how four common container management solutions - Amazon EC2 Container Service, Docker for AWS, Kubernetes, and Apache Mesos - stack against each other. We also see how you can easily get started with each of these solutions on AWS.