SceneJS

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Lindsay Kay

JSON-Based Scene Graph Engine for WebGL

Technology

Define 3D scenes in JSON http://scenejs.wikispaces.com/JSON+Scene+Graph+API SceneJS.org

Update scenes with JSON http://scenejs.wikispaces.com/JSON+Scene+Graph+API SceneJS.org Move the viewpoint: Add a sphere:

Listen to scene nodes http://scenejs.wikispaces.com/SceneJS.bind SceneJS.org Listen for pick:

Send messages to scene nodes http://scenejs.wikispaces.com/ClipBox+Plugin SceneJS.org Move the viewpoint:

Extend the message system http://scenejs.wikispaces.com/ClipBox+Plugin SceneJS.org Insert a clipbox:

Instance library symbols ,[object Object]

Through the Web SceneJS.org I 1 I 2 WWW http://my-server.com Services NodeLoader

Boundaries for visibility culling ,[object Object]

Useful with instancing SceneJS.org http://scenejs.wikispaces.com/SceneJS.BoundingBox

Boundaries for LOD ,[object Object],SceneJS.org Scene Graph 1 2 3

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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.

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Nearly any Internet-connected screen is capable of streaming Netflix content. Sitting on top of a cloud-native microservice architecture, the entire ecosystem generates over 1 trillion events every day to feed critical Netflix systems to monitor service health, to detect fraudulent behaviors, and to improve customer experience. Keystone is the critical piece of Netflix backend infrastructure to ensure massive amount of events are processed in near real time, reliably, at scale, and in face of failures in a cloud-native microservices environment. Turns out, such an embarrassingly parallel stream processing system is not embarrassingly easy to develop and operate, especially given the challenges of unpredictable failures in a cloud-native environment, self-serve multi-tenancy support, and assumptions of maintaining extremely high development/operation agility. This talk will shed light on how we built an elastic, resilient, reactive, and self-healing distributed system in the cloud. Zhenzhong will present * High-level cloud-native microservice based Keystone architecture. * A deep dive on how we built the system based on ideas such as declarative reconciliation, container based immutable deployment, logical workload isolation, and chaos exercise. * Insights into our operation best practices, such as capacity provisioning, delivery semantics, deployment tradeoffs, backpressure management, etc.

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From NDC Oslo, 2017. Linux containers run on Linux. Windows containers run on Windows. You can't mix them on a single host, but you can build a cluster of hosts into a single Docker Swarm, using a mixture of Windows and Linux servers. That swarm can run both Windows and Linux containers, and within the swarm all the containers can talk to each other. This session will show you how to make that happen, but more importantly you'll see why it's a such an important capability - one that will change the way you design, build and deliver software. With a hybrid Docker Swarm you can build a distributed solution where you pick the right technology stack for each component, and leverage high-quality open-source applications to minimize the amount of custom software you need to write and maintain.

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Prometheus was recently accepted into the Cloud Native Computing Foundation, making it the second project after Kubernetes to be given their blessing and acknowledging that Prometheus and Kubernetes make an awesome combination. In this talk we'll cover common patterns for running Prometheus on Kubernetes, how to monitor services on Kubernetes, and some cool tips and hacks to ensure you get the most out of your Prometheus + Kubernetes deployment.

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If you are planning on building a connect integration for any of the Atlassians cloud offerings, growth, performance, and stability should be your highest priorities. In addition, you have to think of keeping the cost down, delivering the product on time, and keeping both users and developers happy. In this session, Nathan Burrell will talk about the architecture of Bitbucket Pipelines (Beta) which is a feature of Bitbucket Cloud that is integrated via connect, runs on AWS and heavily utilises docker. He will walk you through examples that show how one can implement a solid integration while staying aligned and meeting all previously mentioned priorities. You will learn about best practices, software architecture insights, and the technologies that are readily available to assist you in your endeavours. Products covered: Bitbucket

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SceneJS

1. Define 3D scenes in JSON http://scenejs.wikispaces.com/JSON+Scene+Graph+API SceneJS.org

2. Update scenes with JSON http://scenejs.wikispaces.com/JSON+Scene+Graph+API SceneJS.org Move the viewpoint: Add a sphere:

3. Listen to scene nodes http://scenejs.wikispaces.com/SceneJS.bind SceneJS.org Listen for pick:

4. Send messages to scene nodes http://scenejs.wikispaces.com/ClipBox+Plugin SceneJS.org Move the viewpoint:

5. Extend the message system http://scenejs.wikispaces.com/ClipBox+Plugin SceneJS.org Insert a clipbox:

7. Through the Web SceneJS.org I 1 I 2 WWW http://my-server.com Services NodeLoader

9. View and locality intersection tests

10. Useful with instancing SceneJS.org http://scenejs.wikispaces.com/SceneJS.BoundingBox

11.

12. Fast compiled scene graph Optimized Display List http://scenejs.wikispaces.com/Rendering+Algorithm SceneJS.org Fast redraw Recompile Update

13.

14. Hashed for re-use at similar states SceneJS.org Scene Graph

15.

16. JSON Message System

17. Pluggable Service Layer

18. Fast Scene Graph Engine SceneJS.org

SceneJS

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