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

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Thank You

James Leilei@astri.org