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ASTRI Proprietary
Cloud Computing and Big Data
Dr. James LeiProgram DirectorEnterprise and Consumer ElectronicsASTRI
Hong Kong Applied Science & Technology Research Institute
• Location– Hong Kong Science & Technology Parks (HKSTP)
• Mission– High quality R&D for transfer to industry– Develop technical human resources and brings together
industry and university R&D assets to enhance Hong Kong’s competitiveness
• Staffing– 600+ staff– 25% PhD, 60% MS degrees– Patents: +200 granted– Technology transfer: +400– Spin-off: 4
• Research Focus– 80% sustainable technology– 20% disruptive technology
Enterprise & ConsumerElectronics
H.264 HDTV Compression
WiFi SIP Phone
Home Media Centre
Mobile TV
WiFi PMP
Dual-modePhone
IP STB
Multimedia over IP
H.264 AAC-HE
IC Design
Communication ICCommunication IC
System on a Chip
charger cameraHigh Voltage AnalogHigh Voltage Analog
RF, CMOS
DMB-T/H
Fixed DTV mobile DTVH.264
IPTVSTB PMPMultiMulti--Media ICMedia IC
Design ServiceIP Hardening MCUMCU
8051 MCU32-bit RISC MCU
Analog-MixedSignal Design
Low Voltage AnalogLow Voltage Analog
Material & Packaging Technologies
Optical Sensor
System in Packaging
LED TV Backlighting
LED General Lighting
Embedded Optical Link
Photonic Package
CommunicationsTechnologies
Re-chargeable & Env Friendly Battery
Wireless Home AV Gateway P2P/P2MP, SD/HD
UWB MAC, Base Band
MIMO
WiMAX/B3G/4G Edge Node, CWMS
Re-configurable Core
Advanced Antenna
Broadband Access
New Anode Material
Internal Antenna for 2.5G/3G, WiFi, DxB, WUSB
Turbo Charged WiFiWiFi/WiMaxWiFi/WiMax/DxB
OFDM
ASTRITechnology
Programs
ASTRI Proprietary
Big Data Overloading
• Everyday, we create 2.5 quintillion (10^18) bytes data - 90% data in the world has been created in last 2 years
• 5 billion mobile phones – 60% humans (5.4B) sent text by phone; In 2010, 193K text sent per second
• 30 B pieces content shared on Facebook every month• 10% all photos ever taken were shot in 2011
• 235T data collected by US Library of Congress(4/2011)• 15 out of 17 sectors in U.S. have more data per company
than Library of Congress
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ASTRI Proprietary
Big Data’s Cloud Time
• By 2016, 1/3 digital content at cloud (Gartner)• 1 Trillion files stored in Amazon S3; 650K peak requests
per second
• Q2/2012, Amazon retails profit $1.59B, cloud service $1.75B
• By 2015, 40% big corp. will connect internal social network with Facebook
• 1.8 zetta bytes (10^21) data used in 2011• From beginning of recorded time to 2003, 5 billion
gigabytes data created.- In 2011, same amount every 2 days- By 2013, every 10 minutes
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ASTRI Proprietary
• Cloud computing market $55B by 2014, $241B by 2020 (Forrester)• Cyber security market $63B in 2011, $120B by 2017 (MarketsandMarkets)
- Cloud and CPE managed security service $18B by 2016 (Infonetics)- Cloud security software market $241M in 2010, $963M in 2014
• Cloud computing application in China’s financial industry: RMB 15.6B (2011)
• 960,000 new jobs needed in AP in next 3 years, 2/3 shortage (Gartner-13/11/12)• Promoting cloud computing is part of Hong Kong Government ICT Strategy
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Market Opportunity
Global cloud security software market (US$ million)– TechNavio Analysis
ASTRI Proprietary
Real Case: 2012 London Olympics Online Broadcasting(PC/Mac/iPhone/Android)
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• 1.3TB total content• 117.5TB user received
data
• 60GB system data
ASTRI Proprietary
• A computational paradigm meeting the secure computation and communication requirements for cloud applications with big data nature
• Three basic elements:• Built-in end-to-end security
- security, cryptography, coding, system, hardware
• Smart divide between computation & communication - networking, complex system, web technologies, cloud, mobile
• Large transactional process with intelligence- statistics, stream database, machine learning, pattern recognition
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Cloud Security Computation
9 ASTRI Proprietary
Digital Data Life Cycle
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Data GenerationData Generation Aggregate DataAggregate Data Analyze DataAnalyze Data Consume DataConsume Data
Content DataNumerical DataInformationTextualSignal (audio, music)Video Multi-modal
Encryption formatBiometrics basedTarget delivery
Data Re-generation
3rd party Service
Content StorageContent Search
Transactional Data
Data FusionData processing
Compress DomainEncryption Domain
Client Platform
Content & Transaction Data
StorageUsageDigital RightsContent Protection
ASTRI Proprietary
NetworkSecurity
Online VideoBroadcasting
Manufacturing EquipmentMonitoring
High Frequency AlgorithmicTrading
Cloud Computation and Big Data Applications
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OTT TV / Internet TV – Challenges & Opportunities
- Can deliver content/service to all users (billions)- No lock-up or switch service provider by those users- No geography or operator service boundary- Content protection (same level as PayTV service)- Lots of interesting problems (hard problems) – potential disruptive technologies
CP/SP/Carrier CP/SP/Carrier
Operator/SPOperator/SP
Operator/SPOperator/SP
Managed P2PStreaming
All Internet Users
XX X
P2P Server
Key Server
Video Serverw/ Encryption
i-Share Content Delivery Platform
Control &Protection
Media Content Server• Real-time IPTV & VOD • Stream, download, push• Fractional Media Codec
Packetized DRM• DGKBE Crypto• Hardware binding• Scalability/efficiency/storage• TV/STB, PC, mobiles
Media Delivery Server• C/S, CDN, Managed P2P • Robust, error recovery• HTTP, RTSP, RTMP, & P2P End User Quality of
Experience • Identity binding• Packetized rights • Multiple screen support• Auto access & trading
Content Encode &Programming Consumption
MediaDistribution
Broadcast+VOD
Central Mgmt SysP2P+DRM+BOSS
CP1
User
VOD
Broadcast
Broadcast+VODCDN
CP2
CP3
CP4
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Application: China Internet TV Platform –Targeting Billion‐level TV users
Hunan Hefeng Internet TV EPG
H.264 Encoder
Broadcast content
Video Server
PCSTB
DRM Server
User Authentication
Key ServerInternet
Encrypted Broadcast TV
Encrypted Broadcast TV
1. subscription
2. Key query
3. Decryption Key delivery
4. Decryption & Play
Bottleneck –Server Bandwidth
No peer forwarding -Each copy unique
Limitation of Current PKI based Client/Server DRM
Traditional TV vs Internet TV
SmoothPlay
Frequent Buffering/re-buffering
Longer BufferDelay
Screen blackout
Experienced by the tens of thousands of users at the same time.
Unless something can be measured, it cannot be improved!
Systematic Approach for QoE ModelingLarge-scale live P2P testing is prohibitive! Systematic methodology for simulation and modeling for algorithm enhancement!
Challenge: how to get user’s viewing quality?
LearningTools/
P2P ModelingTool
Controlled Viewing Environment
Build Trust Compute Model for P2P Streaming• Mathematical model for P2P Video Streaming QoE
- Collecting all transactional data between peers, and peers/trackers • How reliable and trustworthy a peer is
- calculate its contribution to others & its own QoE (lots of factors)• Peer trust model is developed to help P2P video streaming
- P2P + learning mechanism apply to general cloud computing
18 Long & short delay effects.
User experience, represented by MOS (Mean Opinion Score) - calculated from the real network traffic data (i.e., discontinuity D and playback bit-rate B).
Online Real‐time Olympics Streaming• 2008 Beijing & 2012 London Olympics Games
• Real-time, large users, diverse clients, volume of data
• Quality of Experience Modeling via Trust Compute Model
• 1.3TB Total Content, 117.5TB User Received data, 60GB System Data
• Publications:• “Request-Peer Selection for Load-Balancing in P2P Live
Streaming Systems”, IEEE WCNC 2012• “Closest Playback-Point First: A New Peer Selection Algorithm
for P2P VoD Systems”, IEEE GlobeCom 2011• “Assessment on B-D Tradeoff of P2P Assisted Layered Video
Streaming”, 2011 VCIP• "Perceptual Quality Assessment of P2P Assisted Streaming
Video”, 2010 PV• "Designing QoE Experiments to Evaluate Peer-to-Peer
Streaming Applications", 2010 VCIP• “Survey of P2P File Downloading and Streaming Protocol”, IETF
Draft, 2010• “P2P Layered Streaming for Heterogeneous Networks in Peer-
to-Peer Streaming”, IETF Draft, 2010• “The Secure P2P Streaming Protocol”, IETF Draft, 2010
Technical Challenges
Computational complexity for application system Provide simple programming interface and management for processing
data streams Design a cluster with high availability that can scale using commodity
hardware Distributed computational security model
Build trust computing model from target application requirement Industry standards for inter‐operability
Large data processing (non traditional databases) Stream database algorithms for diverse range of data types Minimize latency using local memory in each processing node and
avoiding disk I/O bottlenecks Overload control mechanism for burst messages
Fault tolerance / fail‐over protection No central node with dedicated or specialized responsibilities, greatly
simplifies deployment and maintenance