Scalable Cloud Render Farm
Challenge
Our client was looking to reduce the time and cost associated with the rendering of 3D and 2D graphics. Their existing 12-node on-premise render farm was proving to be a bottleneck in the rendering process as it was costly to scale, maintain and support.

Solution
Utilising our experience in cloud computing, it was decided to augment the on-premise capabilities by deploying a hybrid-cloud workflow, enabling our client to burst into the cloud when deadlines are tight.
The main project requirement was to enable faster rendering of 15K images for display on the Time Square billboards. To expedite render time, Support Partners created a custom Azure service to split the image into grids, allowing each grid to be parallel-processed. Deadline Render was used to manage the on-premise and cloud resources with Azure Batch being used to spin-up a pool of 6000 Virtual Machine’s for cloud rendering. Azure Render Farm Manage was used to monitor usage and costs, with all data passed through into Microsoft Power BI for project–based drill-down of costs.
Cloud processing enabled our client to dramatically reduce processing time from 72 hours to 23 minutes and make significant cost savings, as required by the brief.
Technology used

Azure Batch
Azure Batch Service is a cloud based job scheduling and compute management platform that enables running large-scale parallel and high performance computing applications efficiently in the cloud.

Deadline
Deadline is a hassle-free administration and compute management toolkit for Windows, Linux, and macOS based render farms. It supports over 80 different content creation applications out of the box.

Avere
Avere vFXT for Azure provides a cloud-based cache layer for data-intensive high-performance computing (HPC) tasks. By caching files close to your compute nodes, Avere vFXT speeds read times and lets you work more smoothly even at peak load.

Maya
Autodesk Maya, commonly shortened to just Maya, is a 3D computer graphics application that runs on Windows, macOS and Linux
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