la m and e symposium nov 2017 all decks.pdf

November 7th 2017

AWS Media & EntertainmentLA Media Symposium - Loews Hollywood

Today’s Agenda: AM

Time General Track

10:00 AM Registration

10:30 AM Welcome - AWS Update

Bhavik Vyas, AWS Global Partner Segment Lead for M&E

10:45 AM Hulu Live TV on AWS

Rafael Soltanovich, VP of Software Development for Hulu

11:00 AM Broadcast Media Management in the Cloud

Chris Blandy, EVP Technology Solutions for Fox

11:30 AM Securing Content torage and Workflows to Meet Studio Requirements

Eli Mezie, Lead Architect Executive at ISE

Dan Meacham, CISO for Legendary Pictures

12:00 PM Networking Lunch

Today’s Agenda: PMTime Track #1: Production & Content Creation Track #2: Broadcast & OTT Track

1:00 PM

Track Session #1

Rendering in Cloud with Deadline 10

Chris Bond, AWS Thinkbox CEO

Jason Fotter, Co-Founder and CTO, FuseFX

Track Session #1

AI Distribution - C-SPAN

Dan Mbanga, AWS BD Artificial Intelligence

1:45 PM

Track Session #2

Panel Discussion: Remote Creatives - Storytelling In a Distributed Age

Igor Boshoer, Founder/CEO for Linumio;

David Benson, CTO for Bebop

David Andrade, Cofounder for Theory Studios

Andy Stack, COO for Arcturus Studio

Track Session #2

Real-time Media Analysis with AI in Media

Kon Wilms, AWS Specialist Media Solutions Architect

2:30 PM Break (15mins)

2:45 PM

Track Session #3

Enhanced Content Workflows using Amazon AI

Track Session #3

Delivery Side Supply Chain and OTT

Ken Kanagaraj, Principal Solutions Architect,

AWS Elemental

3:15 PM

Track Session #4

attn: Scaling to 500 million monthly views

Jake McGraw CTO Attn

Peter Agelasto, Founder Digital ReLab;

Luke Adams, CTO Digital ReLab;

Track Session #4

Low Latency Streaming via AWS and Wowza

Mike Talvensaari, Wowza VP of Product;

Chris Gruiz, WW Business Dev & Ops, AWS Marketplace

4:00 PM Closing Comments

4:15 PM Networking Reception

L e a r n m o r e a b o u t t h e i n n o v a t i v e M & E s o l u t i o n s b u i l t o n A W S b y v i s i t i n g

o u r p a r t n e r s p o n s o r s i n t h e r e c e p t i o n a r e a d u r i n g t h e i n t e r m i s s i o n .

T H A N K Y O U

Content Acquisition

Production/Post

Playout & Distribution

DAM/MAM/ Archive

Digital Supply Chain

Publishing

Analytics

AWS Enabled Media Workloads

Ingest/Move Store Transform Process

LIVE OR FILE

WAN OR LAN

BLOCK OR OBJECT POST PRODUCTION

CONTENT FINISHING

LIVE OF FILE

PACKAGE & DELIVER

Basic Media Pipeline

Supply Chain Is the Media Dynamo

Playout &

DistributionLive TV

B2B/Cinematic

DeliveryDPP, IMF, NABA

Publishing

OTTLive-To-VOD

AVOD, SVOD, TVOD

Catchup TV

Cloud PVR

Normalization

Digital Supply Chain

Ingestion

Manual QC

Auto-QC

Metadata Extraction

Transcoding

Delivery

Packaging

Clips/Edits

CMS/DAM

Production/Post

ProductionEditorial

Dailies

Social Media

Marketing/Promo

DAM & ArchiveActive Archive

Golden Copy

Preservation

Content AcquisitionLive

Media Challenges and Cloud Benefits

Content Growth

Directory

Region

Amazon VPC

VPN gateway

VPN connection

route table

Compute Subnet

Render Farm Compute

Domain

ControllerAD

Connect

Access Control

Principals of Secure Design in Content

Product ion workf lows

Principal : Least Pr iv i lege

Principal : Pr iv i lege Separat ion

Robust Ident ity Controls

Subnet

AWS KMS

Region

Amazon VPC

route table

S3 Bucket

permissions

Define role

Userpolicy

permissions

Encryptionkeys

AWS CloudFormation

Access Data

Monitor

Attach policy

AWS Encrypt ion & Key Management

AWS KMS

PRINCIPAL: DEFENSE IN DEPTH

VPC Segmentat ion

• Controls for an application or

publicly accessible service

• Controls that are currently used

can be applied to most AWS

implementations

VPC Peer ing

• Extends trust and controls

to Counterparties

• Restricts access to only

known infrastructure

• Used for workflows that

involve trusted counterparties

& shared infrastructure

VPC Sub-Account

• Trust is completely centralized

• Tantamount to a physical network

• Most restrictive trust model

Principal : Trust Reluctance

Virtual private cloud

Amazon VPC

flow logsAmazon CloudWatch

bucket

AWS CloudTrail

Monitor API

VPC Logs

Asset Subnet

AWS KMS

route table

S3 Bucket

Encryptionkeys

Logging data

Key Logging

Logging datainstance with

CloudWatch

Amazon

Elasticsearch

Service

Event drive analytics

analytics'

Amazon RedshiftData warehousequeries

AWS LambdaCentralized Logging Proxy

Proxy ServerAdministrator

Log Management

API Calls

Summing it a l l up

SECURE DESIGN IS INTERCHANGEABLE

What’s next

• Collaborate with vendors and with productions to identify workflows to port to

the cloud

• Use published guidance

• Leverage AWS services and documentation

• Blaze a new trail

emezei@securityevaluators.com

THANK YOU!!

@ISESecurity

dmeacham@legendary.com@ DanTechieTweets

Afternoon AgendaTime Track #1: Production & Content Creation Track #2: Broadcast & OTT Track

1:00 PM

Track Session #1

Rendering in Cloud with Deadline 10

Chris Bond, AWS Thinkbox CEO

Jason Fotter, Co-Founder and CTO, FuseFX

Track Session #1

AI Distribution - C-SPAN

Dan Mbanga, AWS BD Artificial Intelligence

1:45 PM

Track Session #2

Panel Discussion: Remote Creatives - Storytelling In a Distributed Age

Igor Boshoer, Founder/CEO for Linumio;

David Benson, CTO for Bebop

David Andrade, Cofounder for Theory Studios

Andy Stack, COO for Arcturus Studio

Track Session #2

Real-time Media Analysis with AI in Media

2:30 PM Break (15mins)

2:45 PM

Track Session #3

Enhanced Content Workflows using Amazon AI

Track Session #3

Delivery Side Supply Chain and OTT

Ken Kanagaraj, Principal Solutions Architect,

AWS Elemental

3:15 PM

Track Session #4

attn: Scaling to 500 million monthly views

Jake McGraw CTO Attn

Peter Agelasto, Founder Digital ReLab;

Luke Adams, CTO Digital ReLab;

Track Session #4

Low Latency Streaming via AWS and Wowza

Mike Talvensaari, Wowza VP of Product;

Chris Gruiz, WW Business Dev & Ops, AWS Marketplace

4:00 PM Closing Comments

4:15 PM Networking Reception

L e a r n m o r e a b o u t t h e i n n o v a t i v e M & E s o l u t i o n s b u i l t o n A W S b y v i s i t i n g

o u r p a r t n e r s p o n s o r s i n t h e r e c e p t i o n a r e a d u r i n g t h e i n t e r m i s s i o n .

L U N C H : R E S T A R T @ 1 P M

Low-Latency Streamingvia AWS and Wowza

Founded in 2005, Wowza offers a complete portfolio to power today’s video streaming ecosystem from encoding to delivery. Wowza provides both software and managed streaming services for producers, developers and engineers to build unique streaming experiences for any device or audience size. Wowza Streaming Engine is Wowza’sflagship award-winning media server software. Wowza Streaming Cloud is an end-to-end live streaming service built on AWS. Wowzaalso offers Wowza GoCoder a mobile SDK and free app for live streaming and Wowza Player, a modern HTML5 video player that is tightly integrated with other Wowza products.

What is Latency?

• Latency is a time interval between the stimulation and response, or, from a more general point of view, a time delay between the cause and the effect of some physical change in the system being observed.

• In terms of streaming, latency is the delay between the initial capture of the video and the viewer.

Other Related Terms

• Time to first frame - Time delay from when a person clicks the play button and when video appears

• Broadcast Delay – The practice of intentionally delaying the broadcast of live material to prevent profanity, bloopers, or violence (wikipedia)

• Drift – when latency increases during a broadcast

• Quality - Higher quality = higher resolution = more data to send

• Scale - How many inputs/participants, how far, how many viewers

• Bandwidth - How much traffic can the infrastructure ideally handle

• Throughput - How much traffic is the infrastructure really delivering

• Bitrate - How many bits of data are being processed over time

Why is there latency?

Q: Why is latency a problem?

A: Latency is not usually a problem – but people think it is.

• For many (or possibly most) live streams, latency does not matter

• HTTP streaming intentionally introduces latency for improved reliability.

• For some live streams, latency is critical.

Latency Demo

What Factors Impact Latency

Main factors affecting latency are:1. Quality2. Scale

Low Latency

Large Scale

High Quality

As either (or both) increase latency increases

What Factors Impact LatencyLow

Latency

Large Scale

High Quality

Quality• Resolution• Two-way• Multi-thread• Frame rate• Smooth playback• Chunk/Block Size• Buffering

Scale• Distance• Participants• Viewers• Streams• Complexity• Variability• Diverse endpoints

How Quality Injects LatencyHigher quality = higher resolution = more data to send• Resolution increases the amount of data in any frame, block, chunk or time

slice. If we hold infrastructure constant as resolution goes up the window and latency to deliver a segment increases.

• Trade-offs: Some mitigation techniques

Increasing ThroughputIncreasing throughput with more bandwidth(network) and bitrate(compute) = More infrastructure, more overhead, more cost

Reducing BufferReducing the buffer requirement will reduce latency but it also makes network fluctuations more visible to the viewer – e.g. a 1ms buffer ideally means content can be viewed as little as 1ms after capture but a 0.1-second network interruption will result in 100 buffer segments being un-recoverable in a live stream.

Reducing FramerateReducing the (frame rate) number of frames per second reduces the smoothness of the viewing experience – e.g. 15 fps is generally a good viewing experience for low movement broadcasts like presentation screen capture/sharing or many church services, it will produce a choppy experience for viewing sports

How Scale Injects LatencyScale increases distances, complexity and variability• As distance, the number of streams, and viewers increase, network

imperfections, variability, and degradation increase and can amplify latency.

• Trade-offs: Some mitigation techniques

Increasing ThroughputIncreasing throughput with more bandwidth(network) and bitrate(compute) = More infrastructure, more complexity, more overhead, more cost

Increasing BufferIncreasing buffer size adds ability for both encoders, transcoders and players to deal with variability smoothly but increasing buffer inherently increases latency. By definition, buffer size is the minimum amount of data for which a process can be initiated. If more data must be accumulated, then processing will take longer.

TCP vs UDPBecause it is a lighter weight protocol without error checking, monitoring, order of messaging and headers roughly 1/3 the size of TCP, UDP is inherently faster but there is no guarantee data is received. SRT can help here

Where streaming latency has been

• Windows Media• MMS, RTSP, HTTP (using UDP or

• Encoder, Server, and Player buffer size management

• “Low delay” audio codecs

• Fast Start, Advanced Fast Start, Fast Recovery, Fast Reconnect, FEC

• 2-3 seconds (on a good network)

• Real Time Streaming Protocol (RTSP)

• Developed and by RealNetworks

• Relies on RTP and RTCP

• Frames can be sent one at a time in real time

• Can leverage UDP

• Potential latency could be as low as ~125 ms with minimal buffering (2-3 frames behind)

Where streaming latency has been

• Real Time Messaging Protocol (RTMP)

• Developed and open sourced by Adobe

• 1-3 seconds and sometimes below 1 second

• HTTP• Apple HLS

• 30+ seconds on iOS (using 10 second chunks)

• MPEG-DASH• 10-20 seconds (variable)

Where latency is going

• WebRTC• Designed for real-time audio,

video and data delivery over less-reliable connections

• Leverages TCP or UDP

• Multiple protocols related to RTSP/RTP

• 1 second or less (as low as 200 ms)

• WebSocket• Designed to provide a

standardized, two-way, reliable communications channel between a browser and a server

• Works with TCP

• Can be used with other streaming protocols including RTMP, WebRTC, Haivision SRT, Wowza WOWZ and Aspera FASP

• As low as 200 ms

• HTTP• Reducing to 4 seconds (using 1 second chunks) for DASH and below 8

seconds (using 2 second chunks) for HLS (includes GOP adjustments as well)

• CMAF + HTTP/1.1 (using HTTP Chunked Transfer Coding) enables video transfer, decode, and display before the end of the chunk encoding (“chunks of chunks”)

• Optimizing DASH MPD attributes (availabilityStartTime and minBufferTime)

• Video encoding enhancements that do not impact decoding (H.264 GDR)

• Quick UDP Internet Connections (QUIC)• Uses UDP (with TCP fallback)

• Focuses on security and reliability

• Tries to be like TCP while reducing connect and round trip times, packet loss, congestion control, and more using intelligent retransmissions and storage and delivery of information

• Built with HTTP/2 in mind

• And….

Network Characteristic that KILL real time video

Packet loss – packets being discarded by routers

Jitter – packets arriving at different times than expected

Delay – the time from sender to receiver

Bandwidth – the fluctuating capacity between points

Overcoming Adverse Network Conditions

SRT Fundamentals

Public InternetSource Destination

SRT Workflow

AWS AWS

SRT Fundamentals

tConstant frame rate

Encoder Variable Bitrate

Unreliable “Dirty" NetworkPacket Loss, Network Jitter,

Variable Delay, Bandwidth Fluctuation

tErratic bitrate and frame rate

Decoder’s Worst Nightmare!

SRT Fundamentals

tReconstructed video frame rate

Much Happier Decoder

SenderBuffer

ReceiverBuffer

SRTEncapsulation & Network Time Stamps

Feedback

SRT Fundamentals

tReconstructed video frame rate

Much Happier Decoder Video

Metadata

Packet acknowledgement – error recovery

Timing – low latency streaming

Network connection characteristics

Bi-directional

Firewall Friendly

SRT Fundamentals

SRT Demo

SRT in Wowza Streaming Engine

Greater reliability of throughput and packet loss

Improved bandwidth utilization with network bandwidth fluctuations & long-haul video transport

RESULTS - Germany to Australia, using a consumer DSL connection: • 7.5x increase of throughput with SRT, compared to RTMP connections over TCP.• RTMP: 800/10,000 kbits throughput• SRT: and 6000/10,000 kbits throughput

Independent security / encryption

Minimized buffer 4x RTT (re-transmission time)

Logic for packet loss recovery and jitter only requires 4 x the network latency to the server

Wide adoption: free open source, known & trusted

SRT in Wowza Streaming Engine Wowza Streaming EngineSRT Fundamentals

Benefits of SRT | Results

» Needs / Challenges

» Provide ESPN-like sports broadcasting to any screen

» High reliability and scalability to support load fluctuations

» Affordable for Division II & III universities and high schools

» Reliable first-mile network connections from remote venues

» Solution

» Wowza + AWS + SRT

» SRT integration significantly increases first-mile throughput

» Wowza transcoding enables ABR, simplifies on-site capture

» EC2 gives on-demand scalability to match broadcast load

» CloudFront supplies cost-effective global delivery

» Benefits

» Increases reliability of connections, reducing support calls

» Enables new opportunities for end users at remote venues

» Reduces costs from alternative solutions by 50%

Business Case

Enabling Customer Success for BlueFrame Technology

Common BlueFrame Technology Customer Scenario

Wireless connection without SRT HLS

SDI, HDMI, or NDI

CloudFront

Wireless connection using SRT

Live broadcast from a college athletics venue with unreliable Internet connectivity

SRT Alliance MembersMembers

Try SRT in Wowza Streaming Engine

• Sign up for a free trial

• www.wowza.com/products/streaming-engine

• Images available in AWS Marketplace

Questions?

Thank you!

AWS Media & Entertainment Cloud SymposiumKen Kanagaraj, Principal Solutions Architect, AWS Elemental

MEDIA SUPPLY CHAIN ON AWS

March 14, 2018

AWS acquired Elemental Technologies October, 2015

Enterprise Government

Broadcasters

Content Programmers

Pay TV Operators

MARKETSCONTENT

ON DEMAND

Storage

Amazon S3

DISPLAYS

DEVICES

AWS ELEMENTAL – PERFECTING THE MEDIA EXPERIENCE

THE ECOSYSTEM: POWERING MEDIA WORKFLOWS

Content

Production

Content Ingest

& Storage

Processing

Management

Content

Distribution &

Consumption

Consumer

Insight &

Analytics

Partner Solutions

Shared Services

Infrastructure Security Network Operations

• Modelling

• Rendering

• Video Editing

• Post Production

• Broadcast Signal

Acquisition

• Digital Dailies /

Approvals

• High Speed Ingest

• Library Storage &

More in

Infrastructure CPU

Engines MXNet TensorFlow Caffe Theano Pytorch CNTK

ServicesAmazon Polly

Platforms

Speech

Mobile

Amazon

Spark &

EMRKinesis Batch ECS

More in

Amazon Lex

Amazon AI: Machine Learning In The Hands Of Every Developer

Amazon Rekognition

Vision

Automating Footage Tagging with Amazon

Rekognition

Built in 3 weeks

Index against 99,000 people

Saving ~9,000 hours a year in labor

More in

Infrastructure CPU

Engines MXNet TensorFlow Caffe Theano Pytorch CNTK

ServicesAmazon Polly

Platforms

Speech

Mobile

Amazon

Spark &

EMRKinesis Batch ECS

More in

Amazon Lex

Amazon AI: Machine Learning In The Hands Of Every Developer

Amazon Rekognition

Vision

Solving Some Of The Hardest Problems In Computer Science

Learning Language Perception Problem

Solving

Reasoning

AWS Deep Learning AMI: One-Click GPU Deep Learning

Up To 40,000

CUDA Cores

Python 3 Notebooks

& Examples

(and others)(Volta at launch)

Scale for

Training

TensorFlow,

Apache MXNet

With Thanks To The Apache MXNet Open Source Contributors!

Bing Xu

Chiyuan Zhang

Qihoo 360

Yizhi Liu

Microsoft

Tianjun Xiao

Indiana

Tianjun Xiao

“Sounds great, Dan!

How can we get started?”

Direct Connect

Collect & Organize Data

KinesisStreams, Firehose,

Analytics

Upload

AWS Snowball Edge

100T Data Transport Device

With On-board Compute

Snowball Selfies: Customers Love AWS Snowball

Direct Connect

Kinesis

Snowball

Snowmobile

Database Migration

Streams, Firehose,

Analytics

Upload

Direct Connect

Kinesis

Snowball

Snowmobile

Database Migration

DynamoDB

Elastic Search

Cloud Search

Glacier

RDS MySQL, PostgreSQL, MariaDB

Oracle, SQL Server

Aurora MySQL

PostgreSQL

Streams, Firehose,

Analytics

Upload

Direct Connect

Kinesis

Snowball

Snowmobile

Database Migration

DynamoDB

Elastic Search

Cloud Search

Glacier

Oracle, SQL Server

Aurora MySQL

PostgreSQL

Streams, Firehose,

Analytics

Redshift & Spectrum

Athena

QuickSight

Upload

Amazon AI

Lambda

Direct Connect

Kinesis

Snowball

Snowmobile

Database Migration

DynamoDB

Elastic Search

Cloud Search

Glacier

Oracle, SQL Server

Aurora MySQL

PostgreSQL

Streams, Firehose,

Analytics

Athena

QuickSight

Upload

Amazon AI

Lambda

Mech Turk

Redshift & Spectrum

Security & Encryption Integrated Across The Platform

Fine grained

access controls

Broad KMS

integrationServer-side

encryption

with CMK

Audit key

usage by

user & role

Import

keysPolicy

validation

& simulation

Use Case: Recommendations

The Concept

Capture Audience Signal Data

Create history of user and item preferences

Estimate similar users and items

Record these in Search Engine

Query Search Engine with User History

Enjoy recommendations!

Storage

Interface

Serving

Layerusers

Recommend

Engine

Media platforms

Mobile

Apache MXNet

SearchPlayBuyRate

Recommendations

Step 1: Logs History Matrix

User1 Thing1

User2 Thing2

User3 Thing3

User2 Thing4

User5 Thing1

User1 Thing2

User1 Thing3

Logs History Matrix

Step 2: Estimate Similar Things

History Matrix

Item-Item Matrix

Step 3: Reduce to Interesting Pairs

Item-Item Matrix

Indicators

(“Items Similar To This….”)

Step 3: Reduce to Interesting Pairs

Indicators

(“Items Similar To This….”)

Items Similar To This

Step 4: Store Indicators in a Search Engine (BATCH)

Superman Highlander, Dune

Star Wars Raiders, Minority

Report

Highlander Superman

Mulan Home Alone, Mermaid

Star Trek …

… …

4587 223, 5234

748 5345, 235

12 8234

245 9543, 7673

3456 4587

… …

Indicators

Step 5: Query Search Engine w/ User History

748 Star Wars

12 Highlander

245 Mulan

4587 Superman

3456 Star Trek

“12”

Amazon AI Lab

Partner with ML engineers

from Amazon to:

Prototype ML with your data

Learn through hands-on

workshops & training

Build custom models

in production

amazon-ai-lab@amazon.com

Thank you!

dmmbanga@amazon.com

THANK YOU!

@dmbanga

la m and e symposium nov 2017 all decks.pdf

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