LUNA, a short film produced by Universal/ETC cloud project, had the goal to establish the past, examine the current reality and determine benefits of cloud centralization. This talk will explore the out come from multiple points of view, including Studio perspective, Directors point of view then outline failures and successes of the project.

1. UNIVERSAL CLOUD PROJECT
“Luna”
An Analysis by Guillaume Aubuchon, CTO, DigitalFilm Tree

2. ABOUT US
Cloud Future | Production History
In film, or any creative endeavor for that matter, there is a sense that the future must be reverent of the past. The forthcoming, while
offering efficiency, must bow to the humble wisdom of that and those which came before. With this in mind, we entered the era of cloud.
Shaping the best of past, present, and future workflow.

3. A Brief History of Cloud3
OpenStack
Discovery
Implementing OpenStack for software.
2012
The year we first discovered OpenStack. We began to develop and integrate OpenStack functionality in
our cloud based software effort.
OpenStack
Implementation
Applying OpenStack to production.
2013
OpenStack was fully developed and began to be implemented as part of production storage
infrastructure.
OpenStack
Beyond
Exploring the benefits of OpenStack.
2014
OpenStack maturing into a search an analytics platform. MongoDB integration with full text search.
Docker Containerization added to run compute applications within compute infrastructure.

4. The Pillars of Cloud4
Instant
Security
Tracking
Location
Access Instant
We can grant or revoke access to any file instantly.
Access Encrypted
Every bit of video and metadata is encrypted. At rest
and on the move.
Access Everywhere
Cloud storage are providing our clients efficient ways
to shoot where they want while collaborating with
whom they choose.
Access Logged
We know and can report, who, what, and where of
every bit of video in the entire storage infrastructure.

5. Metadata Centralization and Assimilation5
01
Audio Assets
Audio assets, originals and
derivative.
04
Technical Metadata
Metadata contributed by systems during the
production process or metadata derived through
automated processes run on the assets.
02
Subjective Metadata
Metadata contributed by
individuals during the
production process.
03
Video Assets
Video assets, originals and derivative.
Subjective
Metadata
Video
Technical
Metadata
Audio

6. The Primary Participants6
SyncOnSet
Scenechronize Pix
Critique
OpenStack Data Aggregation
The Production Process Redefined by the API.
Our goal was to register with multiple Api and CLI tools to aggregate as much metadata as possible to a singular storage and
database.

7. 7
3rd Party API
Software vendor
provides API.
Database
The open cloud.
Open API
Open API for full
text search and
analytics.
3rd Party Software Vendor
The software vendor pushed through an
API updates to relevant metadata and
data.
An Open Database
The open database stores and tags the
metadata as it is queried.
An Open API
An open API is provided for search of the
data.
Query, Store, Query

8. 5
Analysis4Access
3
Metadata
The Basic Cloud Workflow8
2
Dailies
1
Shoot
Media Acquisition/ Ingest
Camera and audio masters ingested into
the cloud.
Metadata Association
Automated association of metadata and
elements. Camera originals and proxies
tied together through UDID.
Data and Efficiency Analysis
Analysis of all data accumulated.
Checksum Verification and Proxy Generation
Verifying against checksum data
uploaded through Pix. Proxy
generation and upload to Pix.
Access Granted to Material
Material accessed by other
entities and individuals through
the cloud.

9. 9 The Universal Identifier in Practice
Pomfort Silverstack Onset Checksum Establishes Hash
Proxy and Editorial Dailies Carry Hashed Value
AAF and EDL Material Linking to Source Carry Hashed ID
VFX and Audio Dailies Carry Hashed Value
Hashed Values Are Used to Archive Material in Use

10. Search Engine Analytics10
01 SEARCH ENGINE
OPTIMIZATION
Full text search, learning with every
search.
02 PRODUCTION
METADATA
Limited spectrum of information, redundant
information.
03 ANALYSIS
RESULTS
Looking at the entirety of t6he
footage, we can query and
graph a multitude of analytics.
The centralization of
information.

11. Production Efficiency Measured11
DAY 01
DAY 02
DAY 03
0.00% 25.00% 50.00% 75.00% 100.00%
02 MATERIAL IN CUT
01 UNUSED MATERIAL
03 USABLE FOOTAGE

12. OUR REALITY
Cloud Future | Hurdles
As with any project that seeks to define a future, there were many pitfalls.

13. The Issues Ahead13
A
B C
DBANDWIDTH
Although we were able to run with
limited bandwidth, there is a limit.
PERSONNEL
Personnel training and retraining. The
changing of personnel.
CONNECTIVITY READINESS
Vendors, although certainly not required
to interact with specific data, were not
prepared to give any data.
POWER TO PROCESS
As with bandwidth, the equation of time,
money, and speed interact in the way.
We face many hurdles in cloud becoming a reality across all production.

14. 14
System Duplication
Redundancy of Data
Beneficial Duplication
78%
12%
12%
12%
Without applications querying and
interacting we are forced to duplicate.
The vast majority of data was duplicated
by individuals.
Metadata Duplication

15. Thank You
“Luna”
An Analysis by Guillaume Aubuchon, CTO, DigitalFilm Tree