Modernizing Media Supply Chains with AWS Serverless

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Modernizing Media Supply Chains
Mark Stephens
M&E Global Segment Leader
Amazon Web Services
A P I 3 0 1
Hilary Roschke
Director Strategy and
Process
Discovery Inc.
Jaime Valenzuela
Director Software Development
20th Century Fox

Learning From Industry Leaders

What’s pushing customer to modernize?

What you have to do?

How are things being built?

Build: With native & fully managed services
Networking and Delivery
AR/VR Machine LearningVideo Processing
VFX/Rendering
StorageDatabases Compute

Build: Hybrid and/or cloud workflows
Scalable
Compute and
Storage
Integrated
Networking
Common Controls
for Security &
Access
Global
Workflows
Same/New
Technology
Partners
Your
Datacenter
Amazon Web
Services
Internal/External
Collaboration

Build: With largest M&E partner community

Content Creation &
Post Production
Machine Learning and Analytics
Digital Asset Management & Supply Chain
Distribution
(OTT, Broadcast, Publish)
Build: Agile | Build for the future

What is being built?

14
Fox Digital Media Archive & Fox Media Services:
Leveraging Innovation in AWS to Capitalize on an
Evolving Media Landscape Problem Statement
• Re-think existing Disaster Recovery (DR)
• Develop serverless, microservices & cloud native workflows
• Replace expensive and support heavy on-prem databases.
• Expand workflows without additional software
development
Use of AWS & Partners
• Amazon S3 and Glacier, AWS Lambda, Amazon DynamoDB,
• AWS Elemental, Step Functions, API Gateway
• Vidispine Asset Management
Business Benefits
• Scale quickly to meet expanding business requirements
• Satisfies goal for having geo-separated assets for DR
• Empower developers with the ability to create media
pipelines to increase reliability and productivity in a
modern media company.
20th Century Fox Digital Media Archive

with AWS Serverless
Hilary Roschke
Director – Strategy & Process (Global Technology & Operations)
Discovery, Inc.
A P I 3 0 1

Distribution:
• ~ 8,000 hours of original
programming/year
• Across all screens in more
than 220 countries and
territories
• In 50 languages
Incoming media processing:
• ~ 700 active vendors
• More than 10,000 files per
month uploaded

Discovery’s move to the cloud

Discovery’s move to the cloud
Intelligent (Rules-
Based) Manufacturing
Media
Sniff
Auto
QC
Advanced
Assessment Edit
Compress
Convert
Transcode
Create
Proxies
Language
Stacking
Language
Customization
Transform
Metadata
Playout /
Publishing
ML
AI
PSE

Overview / expectations
• Problem
• growing volume/complexity
• fixed/aging infrastructure
• Solution
• flexible, scalable cloud
infrastructure
• Architectural overview
• Discovery
• SDVI
• Examples with more detail
• Recurring architectural pattern
• Closing thoughts

The problem
- Unpredictable volume
- High peaks and low lows
- Increase in large, urgent
“special projects”
- Multiple bottlenecks
- Fixed licenses/tools
- Serial processing
- Manual intervention
- Aging infrastructure

The Solution
• Adaptive content processing
• Flexible, scalable infrastructure
• Reduced bottlenecks
• Perform multiple actions in
parallel
• Exception-driven intervention
• Ageless infrastructure
• Eliminate maintenance/refresh
cycles
• Kill/respawn cloud instances in
seconds

Discovery’s
incoming media
processing
architecture
Producer /
Distributor
File Match to
Deliverable
File Validation Result?
Rejected
Rejected
Landing
Point
File
Validation
Delivery
Location
AmericasEurope Asia
Approved
Europe
Delivery
Asia Delivery
AWS Cross Region
Replication
AWS Cross Region
Replication
Americas
Cloud QC
Approved
Cloud QC Result?
Approved
Rejected
Producer s Portal or
Bespoke Interface*
On Prem MAM and TE Workflows (as usual)
Status & TE Data
Status & TE Data Status & TE Data
Status & Technical Data
Status & TE Data
Discovery Deal/
Inventory
System
Europe Asia
Amazon
S3 Bucket
OnRamp
Microservice
Decision
Point
SAAS
(Software
as a Service)
On Premises
Storage
Discovery
System
Key:
Metadata Only Media & Metadata

Elastic Providers
Amazon VPC Amazon Route
53
Elastic IP
address
Cross account permissions, managed using IAM
Customer
Account
AWS in the Rally platform
SDVI
Rally

Elastic Providers
53
Elastic IP
address
Customer
Account
SDVI
Rally
AMI Auto ScalingAmazon SQS Amazon SNSAmazon EC2 AWS Lambda
Amazon S3 Amazon EBS
Amazon RDSAmazon Kinesis

Elastic Providers
53
Elastic IP
address
Amazon S3IAM KMS Amazon GlacierAWS Direct Connect AWS CloudTrailParameter
Store
Customer
Account
SDVI
Rally
AMI Auto ScalingAmazon SQS Amazon SNSAmazon EC2 AWS Lambda
Amazon S3 Amazon EBS
Amazon RDSAmazon Kinesis

• The combination of serverless architecture, step
functions and lambdas drives efficiency and scalability
for Discovery
• Discovery processes in Amazon S3 through an SDVI
extraction layer
• Step Function => Heavy Lifter => Step Function
Order from chaos

Launch => Process => Evaluate
Order from chaos

Discovery’s incoming media processing supply chain

Discovery’s incoming media processing supply chain
Write out & add
Sanitized Code
Launch
Evaluate
Heavy
Lifter
Post Task
Evaluate

Supply chain for media analysis
Dynamic QC Laucher
SDVI Evaluate – Dynamic
QC Split
Read WF
MD
File Class
Read WF MD
Video
Format
Analysis Tool 1
Multiple Presets
Runs tool 1 and generates
QC metadata & report
Analysis Tool 2
Multiple Presets
NextStep
NextStep
QC Chain Join
SDVI Evaluate – Dynamic QC Join
Workflow Join for QC Tool Jobs
Post QC Evaluate
SDVI Evaluate – Post QC
Evaluate
Failures Send Workflow to
OR02 and cancel all active
workflows on movie
NextStep
Python-coded
Split Workflow Python-coded
Join Workflow
Analysis Tool 1 Launcher
Launcher
Loads presets to tool 1
Launcher
Loads presets to tool 2 Fail
Redirect
Pass
Redirect

Dynamic QC Laucher
QC Split
Read WF
MD
File Class
Read WF MD
Video
Format
Analysis Tool 1
Multiple Presets
Analysis Tool 2
Multiple Presets
NextStep
NextStep
QC Chain Join
SDVI Evaluate – Dynamic QC Join
Workflow Join for QC Tool Jobs
Post QC Evaluate
SDVI Evaluate – Post QC
Evaluate
Failures Send Workflow to
OR02 and cancel all active
workflows on movie
NextStep
Python-coded
Split Workflow Python-coded
Join Workflow
Launcher
Loads presets to tool 1
Launcher
Loads presets to tool 2 Fail
Redirect
Pass
Redirect
Supply chain for media analysis

Dynamic QC Launcher
code example

Post QC Evaluate code example

Example recap
• Issues solved
• Process
• Fewer, more powerful, more
intelligent workflows
• Multiple, scalable provider options
• Increased throughput
• Tech
• Workflows respond elegantly to
failure conditions, reducing support
• Workflows adapt to file conditions
without any human intervention
• Tips and tricks
• If a bad file condition happens more
than twice, code a response

46© 2018 SDVI Corporation –
Confidential
Amazon
Rekognition
AWS Elemental
MediaConvert
Adobe
PremierSDVI Rally
Create low res proxy Labelling / Content Moderation Timeline Markers
SNS
GetObject
PutObject
CreateJob GetLabelDetection
GetContentModeration
SQS
Amazon S3
AI generated timeline markers for Adobe Premier

Confidential
Amazon
Rekognition
AWS Elemental
MediaConvert
Adobe
PremierSDVI Rally
SNS
GetObject
PutObject
SQS
Amazon S3

Example RecapIN PROGRESS
• Issues solved
• Process
• Providing single user interface for
operators to curate, update, and
approve metadata
• Direct integration of asset review
with cloud-based supply chain
• Tech
• Proxy workflows minimize egress
• Normalized metadata format
makes the information from ML
and QC more machine-readable
and able to drive workflow

Closing thoughts
• Serverless functions bring a myriad of benefits
but impacts can be incredibly far reaching
• OK to Start slow
• Use hybrid architecture if appropriate
• Be prepared to live in two worlds during
transition
• Potential friction/growing pains
• Licensing shifts can be difficult
• True scalability needs to be available at all
points in your supply chain
• Monitoring and availability are paramount
• Security and permissions need to be
managed carefully

Thank you!
Hilary Roschke
Hilary_Roschke@Discovery.com
@hroschke

with AWS Serverless
Jaime Valenzuela
Director software development
20th Century Fox – Digital Media Archive
A P I 3 0 1

Jessica Ver
Deva Sattanathan
Paul DiLoreto
Sundar Babu
Marianandan Arockiasamy
Aditya Maturi
Nick Chen
Tim Saarinen
Ryan Johnson
Allan Smith
Denis Olennikov
The crew
Al Rundle
Thanh Nguyen
Sergey Sarkisyan
Paul Appicelli
Stephen Han
Dipti Prajapati
Jason Shao
Denis Olennikov
Andrew Thomson
Brian Kueck
Rama Golla
Alex Maleski

Agenda
Digital Media Archive’s goals
The evolution of our business, challenges and opportunities
Why AWS Serverless
Media Workflows
Microservices
Digital Media Archive
Migrating, ingesting and restoring assets with Stencil, Tube, and Stepr

Digital Media Archive’s goals
• 20th Century Fox’s Digital Media Archive’s main goals are:
• Safely and securely store the losslessly compressed
Studio assets
• Geo-separation of these assets for Disaster Recovery
while creating a virtual second facility in the cloud.
• Keep pace with the increased data requirements for
formats such as 4K HDR
• Provide an easy interface for users to find assets
• Ability to retrieve assets in AWS and On-premises
• Be extensible to adapt and support emerging formats
such as IMF

The evolution of our business
• At 83 years, 20th Century Fox is one of the oldest
movie and television studios
• Our Digital Media Archive was founded using on-
premises LTO storage and “Enterprise” workflow
solutions
• Large capital investment, continuous maintenance
and support, power and cooling, and highly
specialized software development and engineering
staff was required
• This rigid infrastructure resulted in high costs, lost
innovation and technical debt
• Needed to re-think our archive and disaster recovery
strategy to be cloud native and replace aging
infrastructure

The situation and challenges
• Limited physical space in our on-premises datacenter
• Reoccurring cooling problems
• Same functionality was coded across multiple applications in
different programming languages
• Scaling applications was impossible or difficult
• Existing SOA / BPM stack was expensive and difficult to maintain
• Application deployment was involved and required extensive
documentation

The opportunity
• Rethink our archive strategy to support the evolution of our business in
the cloud so that in the near future we can easily transition our
“Disaster Recovery” site in the cloud to our primary site and visa versa.
• Develop serverless and cloud native workflows and microservices to
replace our rigid on-prem workflow software
• Utilize Terraform to deploy and maintain consistent infrastructure in all
environments
• Replace expensive and support heavy on-premises databases
• Build applications that empower users and administrators to map APIs
and dynamically generate webforms
• Create and expand workflows without the need of software
development for each feature request
AWS Step
Functions
AWS
Lambda
Amazon
S3
Amazon
Glacier
Amazon
DynamoDB
Amazon API
Gateway
Amazon
SQS
Amazon
SNS
Amazon ECR Amazon ECS Amazon
EC2
Amazon ES

Why AWS Serverless
• Using Lambda Functions and API Gateway provided:
1. Scalability out of the box
2. Simple deployment with Terraform
3. Easy connection to serverless DynamoDB collections
• Step Functions allowed us to customize and orchestrate
microservice calls
Amazon Elastic Container Service (Amazon ECS) and Amazon
Elastic Container Registry (Amazon ECR) provided reliable
container management and deployment
• Elemental MediaConvert allowed us to create proxy videos for
our entire archive without worry about scalability
• Amazon S3 and Glacier integration out of the box
AWS Step
Functions
AWS
Lambda
Amazon
S3
Amazon
Glacier
Amazon
DynamoDB
Amazon API
Gateway
Amazon
SQS
Amazon
SNS
EC2
Amazon ES

Media workflows
• A media workflow is a sequence of steps carried out to catalog, transform,
package, and/or deliver media
• Pipelines are workflows with normalized inputs and outputs. They are
optimized throughput and ease of use
• Jobs are instances of pipelines. Hundreds of jobs run in our facility 24/7
• Tube, our in-house solution to create, modify, and manage pipelines
• Pipelines use Stepr to wrap AWS Step Functions that invoke reusable and
scalable microservices to aggregate and transform data necessary to
execute job

Microservices
• Decoupling common functionality from applications into NodeJS
hyper-focused microservices
• Two microservice categories:
1. Provide technical or title metadata like aspect ratios, frame
rates, language codes, audio configurations etc. They retrieve
the data from No SQL databases. Used to cascade data to
another service or display it in a form for user selection
2. Perform common functionality like string manipulation, file
naming, timecode calculations, frame rate conversions etc.
• Goal is to empower operators to be focused on our most valuable
resource, the content. This is achieved by querying these services
to compute, derive or fetch data in order to reduce operator data
entry.

Migrating, ingesting and restoring assets
Framerates Languages Codecs
Text and numeric values to display for
user selection or used in computations
Durations Timecode Eamil
Common computations in one place. Guarantees
that same formula and specifications are used for all
apps
Step Functions
Stencil ECR / EC2
Tube ECR / EC2
DMA Restore Pipeline DMA Ingest Pipeline

Tube, Stencil and StepR applications
Status
Retry Step

Migrating, ingesting and restoring assets
• Our own Tube / Stepr application was developed to define and manage
pipelines
1. Ingest and archiving workflow is defined in the ingest pipeline in
Tube
2. Restoring functionality is defined in the retrieval pipeline
3. Pipelines wrap and create Step Functions in AWS
• Our own Stencil application provides the programmatically rendered
webforms when operator intervention is required
1. Admin users define webforms by mapping input to required API
variables to UI elements like textboxes, dropdowns, checkboxes etc.
2. Mapping is done by point and click to API JSON structures
3. A stencil can be invoked and rendered programmatically as
required in a pipeline

Stencil

Stencil: Mapping JSON to UI elements

Stencil components
Validator
{ runValidations }
Parameters: takes an object. {value,
inputType, validation, settings}. Used
internally in Stencil to return an
error message or an empty string if the
value passed is valid.
{ validationList }
Useful for exposing currently developed
validation functions. Use key/value as
the function's name & a label to
display it.
Group
Shortcut component to creating a
Material UI Expandable Group. Has some
extra logic so that Autocomplete
components Menu will display if it
overflows.
Expected props:
startExpanded (boolean)
expandable (boolean)
title (string)

Stencil object schema
"sourceMap": [
{
"groupName": ”Example:
i.e.",
"groupCollapse": false,
"groupStyle": "",
"hidden": false,
"rows": [
{
{
"_id": string,
"identifier": string (key),
"label": string,
"application": string (key),
"applicationSettings": {
// These are replaced dynamically
},
"isCustomJson": false

Stencil object sample
"validation": {},
"inputTags": [],
"settings": {},
"disabled": boolean
}
}
]
},
]
}
"inputs": [
{
"id": string,
"label": string,
"labelColor": string,
"inputStyle": "”,
"mappingPath": array,
"inputType": string (key),
"inputOptions": object or array,
"source": "userInput",
"value": "",

Stencil: Webform rendered

Tube: Creating and managing pipelines

Tube / Stepr: Creating pipelines with Step Functions
start skip_1
step_1
pass_1
skip_2
step_2
pass_2
skip_3 end

Tube / Stepr pipelines
/* available params for step_1 … step_n
task : <string> required - should be same name as controllers key for specific function passed into Stepr
Constructor.
params? : <any> - OPTIONAL params to include while running specific task.
skip : <boolean> required - if true, skips the steps and adds step_#:"skipped" as result to result object.
mapper? : <Array[<objects>,...]> OPTIONAL - maps metadata to keys from any previous steps to use in current
step as params. Maps path to Param key.
flags? : <Array[<strings>,...]> OPTIONAL - array of strings
*/

Keeping track of jobs in Tube
{ "_id": "5be49a8e86ec21029c7dd004",
"exeId": "u3vjyikjo91e6ew",
"status": "SUCCESS",
"comments": "",
"stepsTotal": ”4",
"stateMachine": ”4_stepr",
"retry": "false",
"createdBy": "Sergey",
"workflowId": "100230",
"input": {

Keeping track of jobs in Tube
“step_0": { “step_1:. . . }, //Instructions
"step_1": { //result path from step 1 the state machine definition
"status": "OK",
"templateVersion": "ASM-V_2-1_2016-09-27",
"templateType": "ASM VIDEO FILE",
},
"step_2": [{ "status": "OK",
"Title": "ItsAlwaysSunnyInPhiladelphia",
"Version_ID_Number": ”V123",
"Extension": "mxf"
}],

DMA: Migrating assets

DMA: Ingesting assets
Status
Retry Step

Amazon
Glacier
Vidispine Ingest
Amazon S3
Amazon S3

S3 Copy

DMA: Restoring assets

Conclusion
• Serverless built for scalability
• Easy to deploy lambda based microservices
• DynamoDB collections are easy to create and connect to lambda
• Step Functions
• Development effort needed to track and triage jobs
• Non-trivial when operating a hybrid cloud – on-premises
• Recommend to create generic state machines
• Need to develop an end-user friendly system to define tasks to
be executed and attach to steps
• Fargate
• Still exploring how to orchestrate deployment to fargate
• Cost might be higher going this route than EC2
AWS
Lambda
Amazon
DynamoDB
AWS
Step Functions
Amazon API
Gateway
EC2
Amazon ES

Modernizing Media Supply Chains with AWS Serverless

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Semelhante a Modernizing Media Supply Chains with AWS Serverless

Semelhante a Modernizing Media Supply Chains with AWS Serverless (20)

Mais de Amazon Web Services

Mais de Amazon Web Services (20)

Modernizing Media Supply Chains with AWS Serverless