Optimizing Storage Efficiency for Meta Video Processing


Dec 13, 12:00PM PDT(08:00PM GMT).
  • Free 111 Attendees
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This event is hosted by SF Big Analytics Group. https://www.meetup.com/SF-Big-Analytics

Like the rest of the video world, Facebook Video has significantly grown year to year. While we celebrate the growth rate, we are also concerned about the resources consumption to support the growth, which became worse during COVID.
Seeing the gap between increased storage demand and supply, video infra has worked with Facebook capacity team to invent new methods to bend the curve. We establish source + MVE (minimum variable encoding) storage policy for every FB video. Then through a video lifecycle manage system, we ensure the storage consumption of a video is proportional to its popularity. For example, purge unused encodings, and reduce source from two permanent copies to one copy if the video becomes “cold”. With such technology, 70% of FB Video projected storage growth for 2021 has been successfully suppressed.
We are exploring to further improve the performance and accuracy of the storage management system with cost-benefit model.

Jamie Chen (Meta)

tech lead with Meta (Facebook) Video Infrastructure. Her work focuses on building scalable and efficient video transcoding and management systems to support billions of video uploads to Facebook, Instagram etc. Before Meta, Jamie was with Dolby Laboratory conducting research on HDR video algorithms and compression technology towards Dolby Vision. She received her Ph.D. in Electronic & Computer Engineering from University of Florida, and has co-authored 15 patents on video compression, video signal processing and related areas
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