Big Data Performance Growth on the Rise

1

Impact of Big Data growth On Transparent Computing

Michael A. Greene

Intel Vice President, Software and Services Group, General Manager, System Technologies and

Optimization

2

Transparent Computing (TC)

TC is a user controlled cloud computing.

– Prof. Zhang Yaoxue

3

Transparent Computing Vision

User can get any info, any application or any

Operating System from any devices transparently.

4

Transparent Computing (TC) is facing Data growth challenges in cloud

Data

Data is the Key driver

behind TC services

5

Data Collection

Transparent Computing (TC) Challenges

TC Data challenges comes from 4 areas

Data Process

Data Storage

Data Access

Data

6

Billions

connected users sharing

5.3B

CELL PHONES

629M

FACEBOOK

364M

HOTMAIL

663M

SKYPE

273M

YAHOO

7

Billions

connected users sharing

5.3B

CELL PHONES

629M

FACEBOOK

364M

HOTMAIL

663M

SKYPE

273M

YAHOO

8

Billions

connected users sharing

5.3B

CELL PHONES

629M

FACEBOOK

364M

HOTMAIL

663M

SKYPE

273M

YAHOO

>1500 Exabytes

of cloud traffic

9

Billions

connected users sharing

5.3B

CELL PHONES

629M

FACEBOOK

364M

HOTMAIL

663M

SKYPE

273M

YAHOO

>1500 Exabytes

of cloud traf c

10

>1500 Exabytes

of cloud traf c

Billions

connected users sharing

5.3B

CELL PHONES

629M

FACEBOOK

364M

HOTMAIL

663M

SKYPE

273M

YAHOO

1400 Exabytes

of new integrated systems data

11

>1500 Exabytes

of cloud traf c

Billions

connected users sharing

5.3B

CELL PHONES

629M

FACEBOOK

364M

HOTMAIL

663M

SKYPE

273M

YAHOO

1400 Exabytes

of new integrated systems data

12

1400 Exabytes

of new integrated systems data

>1500 Exabytes

of cloud traf c

Billions

connected users sharing

5.3B

CELL PHONES

629M

FACEBOOK

364M

HOTMAIL

663M

SKYPE

273M

YAHOO

690% Growth

in storage capacity by 2015

Big Sensed Data

Big Corp Data Big Web Data

Structured Data Unstructured Data

Corporate Data

Time

Volume

13

1400 Exabytes

of new integrated systems data

>1500 Exabytes

of cloud traf c

Billions

connected users sharing

5.3B

CELL PHONES

629M

FACEBOOK

364M

HOTMAIL

663M

SKYPE

273M

YAHOO

690% Growth

in storage capacity by 2015

Big Sensed Data

Big Corp Data Big Web Data

Structured Data Unstructured Data

Corporate Data

Time

Volume

14

690% Growth

in storage capacity by 2015

Big Sensed Data Big Corp Data Big Web Data

Structured Data Unstructured Data

Corporate Data

Time Volume

1400 Exabytes

of new integrated systems data

>1500 Exabytes

of cloud traf c

Billions

connected users sharing

5.3B

CELL PHONES

629M

FACEBOOK

364M

HOTMAIL

663M

SKYPE

273M

YAHOO

What insights can we derive?

REPORTING ANALYSIS

MONITORING PREDICTION

COMPLEXITY

BUSINESS VALUE

Are you looking at Big Data?

75% Yes

5% No, but No on radar 20%

HOW ARE YOU APPROACHING

THE OPPORTUNITY?

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Data

The Big Data Platform

Big Data Technology can solve TC

Data Challenges

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Distribute analytics to the edge sensors/devices and drive a standards based connected, managed and secure architecture

Drive innovation in big data applications by providing optimized software stacks and services

Foster the growth of big data through partner collaboration, focused on usage model examples and reference deployment architectures

Intel Role in Big Data

Invest in solution research and academia collaboration

Accelerate big data analytics through faster and more effective CPU,

storage, I/O and network architectures

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Intel® Intelligent Systems Framework:

Simplifying the Internet of Things

DRIVING SECURE

INTEROPERABILITY UNLOCKING EDGE DATA FILTERING DATA

Billions of devices that need to share data with each other

and the cloud

Edge systems need to react to streaming data

in real time

Data volume outpacing network and storage

efficiency

Data Collection

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Up to four channels DDR3 1600 MHz memory

Up to eight cores Up to 20 MB cache Integrated

PCI Express*

3.0 Up to 40 lanes per socket

Platform and Software Optimizations for Hadoop

1 Performance comparison using best submitted/published 2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012.

2 Source: Intel internal measurements of average time for an I/O device read to local system memory under idle conditions comparing Intel® Xeon® processor E5-2600 product family (230 ns) vs.. Intel® Xeon® processor 5500 series (340 ns). See notes in backup for configuration details

* Other names and brands may be claimed as the property of others

•

• Up to 80% Performance Boost vs. Prior Generation – Intel® AVX - Reduce Compute Time

– Intel Turbo Boost

• Hadoop Optimizations

– Built on Open Source Releases

– Custom Tuning for Data Types and – Scaling Approaches

Data Process

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Intel & Cloudera Strategic Partnership

CDH to be Performance-optimized for Intel Architecture

Support for Intel CPUs, Ethernet, SSD, security & future technologies Promote CDH as the

Hadoop Distribution of choice

Largest strategic shareholder in Cloudera

Data Process

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“In Memory analytics”

are “Game Changing”

Near Real-time Insight Enabled by In-Memory Solutions

HANA

TimesTen In- Memory Database

Business Intelligence Enterprise Edition

+ +

Architecting for In Memory Model

VOLTDB 20 node VoltDB system can do what a 1000 node Hadoop cluster can do … Michael

Stonebreaker,

Objectivity

GraphDB SolidDB

Low Cost Memory Technology

$0

$10,000

$20,000

$30,000

$40,000

$50,000

Q4 2010 (DRAM) Q4 2016 (DRAM) 2016 (CR)

$/TB 20x Reduction

50 500 5000

1 2 4 8

Running time (s)

Socket Count

SAP HANA* Scalability

Customer Workload

Ideal

8S Glueless

Near-perfect scaling on Intel Xeon processor E7 family

Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. Source: http://www.intel.com/content/www/us/en/high-performance-computing/high-performance-computing-xeon-e7-analyze-business-as-it-happens-with-sap- hana-software-brief.html

Data Process

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Intelligent Storage Pays Off De-duplication

Intelligent Tiering Thin Provisioning Real Time

Compression

BEFORE DE-DUPLICATION AFTER

APPLI 1 APPLI 2 APPLI 3

TRADITIONAL

ALLOCATION THIN PROVISIONING

ALLOCATED BUT FREE USED ALLOCATED BUT FREE

USED

USED ALLOCATED BUT FREE

APPLI 1 APPLI 2 APPLI 3 SYSTEM-WIDE

CAPACITY RESERVED

Up to 80% data reduction ²

95% smaller backup ¹

Up to 80% ^reduction

in disk expenses ³

1

IBM storage simulcast, November 9, 2011

2

BM storage simulcast, November 9, 2011

3

Dell “Fluid Data Storage: Driving Flexibility in the Data Center”, February 2011

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Intel IT study “Solving Intel IT’s Data Storage Growth Challenges

Up to 25% reduction in storage CapEx growth ⁴

Data Storage

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Visibly Mobile Performance

18 Intel® Virtualization Technology requires a computer system with an enabled Intel® processor, BIOS, virtual machine monitor (VMM) and, for some uses, certain computer system software

enabled for it.

Work Station Performance For Right Deep Model Generation for Analytics

Processes

Visibly Mobile Data Productivity

Secure Media, Data,& Assets Collaboration

Insight &

Productivity Responsiveness

Data Access

Flexible End Point Solutions with client application support that allow fast and efficient data modeling,

scoring and direct data access from any location

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“New, Open ” looking to disrupt “Traditional”

Data Management Analytics

Tr ad iti onal Ne w & or Op en

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Intel’s contribution to Open Source

Enable open source operating environments to run best on Intel architecture

Foster open source ecosystems and develop new markets for Intel and its partners

UPSTREAM DOWNSTREAM

Alliances Foundations Code

Capital

OEM | Service Provider |

Enterprise

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Big Data Use Cases

Consumer Behavior Security &

Risk Management Operational

Efficiency

Location Aware Ad Placement Buyer Protection Program

Personalized Preventive Care

Claim Fraud Reduction Traffic

Optimization Smart

Energy Grid

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Collaboration with CSU on TC and Big Data

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1 2 3

 Big Data helps to solve the data challenges for Transparent Computing

 Intel is well positioned from software stack and platform basis

 Intel is committed to collaborate with partners in new technology to address more demanding requirements of the future

Summary

Thank You