Post on 21-Aug-2020
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Isilon SCALE-OUT NAS bertrand.ounanian@emc.com
Stockage Central et Securise De Donnees Video
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Vidéo et Monde Sportif
Stockage et Production Vidéo
Vidéo et Traitements Analytiques
Applications à la Santé
Introduction
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Archive Bande
Serveur d’archive
Archive NearLine NAS
Création de contenu vidéo
Serveurs Ingest/Playout
Live
3
Stockage dédié SAN/DAS
MAM
Stations d’édition
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Spécificités du Stockage Vidéo
Perfomance – Séquentialité des traitements
– Traitements non-linéaires
Evolutivité
Fiabilité – résilience
Ouverture
Simplicité !
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Near-line & Archive
Archive Server
Live
Simplification des workflows
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Ingest/Playout Servers
Stations d’édition
MAM
Production
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HiRes Media Source
• Mezzanine Quality
• Licensed Movies
• Live Programming
• Media Storage
Prepare Media
• Transcode
• Segment
• Clone
• Media Storage
Content Management
• MAM
• DAM
Conditional Access Control
• Encrypt
• DRM
Originate Media
• File Streaming
• Origin Storage
• Cache Servers
• WebDAV
• CDN Brokers
Deliver Media
• Cache Servers
• Web Servers
• Edge Storage
• Application Brokers
Present/ Consume Media
• STB
• PC/Mac
• Tablet
• Smartphone
• BYOD
Content Delivery – Media Flow Overview
Broadcast Transmitter
Internet Transmitter
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Content Delivery – CDN Overview
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Example model – IPTV Streaming IPTV Streaming 0.8 million subs
CDN Sizing worksheet 10% concurrency
Average Bitrate
2.0 Mbps
1 Origin 5 Locations 23 POPs
116 streams 98% Cache Hit 1,163 streams 85% Cache Hit
0.2 Gb/s 2.3 Gb/s
3,370 streams
253 6.7 Gb/s
0.84 GB/s
253
VOD + CatchUp TV Streams VOD + CatchUp TV Streams LIVE + VOD + CatchUp TV Streams
5 X400 3 X400 3 S200
393 TiB useable 154 TiB useable 21 TiB useable
Originate Media
• File Streaming
• Origin Storage
• Cache Servers
• WebDAV
• CDN Brokers
Deliver Media
• Cache Servers
• Web Servers
• Edge Storage
• Application Brokers
Present/ Consume Media
• STB
• PC/Mac
• Tablet
• Smartphone
• BYOD
Origin Server Distribution Servers Edge Cache Servers
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Live & Entertainment
Technique Analysis
Team Performance
Coaching
Security
Sport and Video uses
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Video Surveillance Analytics Examples
Analytic Description Example Use Case
Motion Detection When pixels change in a scene, the system will denote this. Only record on motion scenarios are prevalent such that recording for a camera is only when there is motion plus 60 seconds before and after motion starts/stops.
Object Left Behind Detection of when a person drops a bag or leaves a bag in a scene. In train stations and transportation hubs, this is common to identify potential bomb threats.
Trip Wire Detection of a person cross a line (directionally or holistically) in a field of view.
At airports, a trip wire is used to detect someone going the wrong direction at an exit door from arrivals to baggage claim. Detect person crossing fence line, such as a border.
People Counting Counting the number of people crossing a set line. In a entry way of a retail store, getting an idea of how many people are entering store over certain time period.
Piggy Backing Identifying if more than 1 person enters a building at a time when only 1 badge swipe is used.
Used in building management scenarios in conjunction with the access control system. Genetec and Next Level are good examples.
License Plate Identifying and exporting the license plate numbers of a car. Use typically at main gates of secure facilities or in transportation scenarios such as highways to help police identify where a particular suspect might be.
Facial Recognition Analytics tied to identify a person or just pass the facial information to another system.
High security areas where identification of certain suspects is prevalent. Casinos using this to identify known watch list entities.
Retail Loitering & Traffic Analysis
Perimeter Trip Wire People Counting
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Deliver Better Health Care Big Data Analytics Accelerates the Path to Health Care
TRADITIONAL DATA LEVERAGED
LOW
HIGH
Quality
of Care
Legacy System
Treatment Pathways on
Summary Data
Database BI Reporting
Treatment Pathways on All the Data
Delivering 10 Years Of Data In Seconds
BIG DATA LEVERAGED
Big Data Analytics
Associative Rule Mining and User Clustering Improves Pathways
External Data Sources Enable Personalized Medicine
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Imaging Environment High Level Overview
• CT Scans • Ultrasound • Mammography • MRI • etc.
Modality/Device Application Database
3
1 2
Application Server
DR Image Archive Image Archive
Viewing Workstation
Image Cache
1 2 3
High Level Workflow Explanation
PACS Application updates DB, and then writes Image to Image Cache and Image Archive CDICOM)
Image captured from modality and sent to Application Server
Viewing workstations access Application Server for output from modality (MRI, CT Scan etc)
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Typical PACS Environment
Image Viewer
Facility A PACS
Other Modalities
Image Viewer
Facility B PACS
Other Modalities
Image Viewer
Cardiology Imaging
Image Storage Image Storage Image Storage
Image Viewer
Other Imaging (Ophth / Endo / Derm)
Image Storage
Facility A PACS
Other Modalities
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Vendor Neutral Archive (VNA) Environment
Unified Image Viewer
Facility A PACS
Other Modalities
Facility B PACS
Other Modalities
Cardiology Imaging Other Imaging
(Ophth / Endo / Derm)
Central Vendor Neutral Archive
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A
cloud compute cloud
storage
Local researchers and Analysts
Collaborators
Instruments onsite
PHI
Experimental Data
Annotation DB
Tier 2 Storage Isilon NL series
Tier 1 Storage Isilon S and X series Aspera
HPC
Remote Data Acquisition (Sequencing Service Provider)
Tier 2 Storage Isilon NL series
ARCHIVE
HOME DIR
HPC
ANALYTICS
SYNCPLICIY PANORAMA
VIDEO SURVEILANCE
Mutualisation des Données de Recherche
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Architecture en Cluster : Simplification
Clustered Storage Layer
Intra-cluster Communication
Layer
Servers
Client/Application Layer Ethernet Layer
Servers
Servers
1GbE 10GbE
1GbE 10GbE Infiniband
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Centralisation du Stockage
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Poles universitaires / CHU
eLearning
Production Media
Animation
Archive
Surveillance
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Bertrand Ounanian bertrand.ounanian@emc.com Benjamin Coutière benjamin.coutiere@emc.com 07 86 87 38 53