Řešení postavená na IBM System x serverech - HPC, Cloud, Business Analytics, SAP HANA, Big Data,

Řešení postavená na IBM System x serverech - HPC, Cloud, Business Analytics, SAP HANA, Big Data, Martina Kocourková & Milan Král, IBM STG System x Univerzita, Olomouc, 12.6.2013 1

Agenda • Úvod • IBM Technical Computing – HPC, Deep Computing • Co to je? • iDataPlex a Flex Cluster řešení • Podpůrné SW (Platform Computing, GPFS...) • Cloud/ Virtualizace/ Automatizace • Standartizace – Gartner • SmartCloud Entry • SCEntry Demo • BAO a Big Data • Klasická architektura BA • In Memory koncept • SAP HANA • Cognos/ DB2 BLU • Big Data • Závě ěr 2

IBM Technical Computing – HPC, Deep Computing

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Hlavní atributy IBM Technical Computing řešení • • •

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Technical Computing již dávno není • pouze o tzv. high-end superpočítačích Překonání tradičních bariér HPC díky řešení od IBM Podniky a organizace napříč odvětvím, geografickým umístěním a velikostí mohou díky IBM využít sílu Technical Computing řešení jako svou konkurenční výhodu Nižší náklady a vyšší produktivita díky maximálnímu využití výpočetní kapacity a výkonu Snížení rizik díky optimalizovaným „end-to-end“ řešením šitým na míru potřebám zákazníků

IBM Technical Computing řešení zahruje infrastruktutní systémy, software a služby: – IBM Platform Computing™ – IBM GPFS™ – IBM System Storage® DCS3700 – IBM Intelligent Cluster™ – IBM® System x® servers

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IBM System x portfolio Enterprise x3950 X5, x3690 X5

BladeCenter iDataPlex Server consolidation, large virtualization and enterprise workloads

dx360 M4

Massive scalescale-out HPC, cloud, grid, energy efficiency

Scale Up

BC H, HS23, HX5 Infrastructure integration and simplification, energy efficiency

PureSystems

Scale Out

Single, infrastructure applications

x3550 M4, x3650, x3750 M4

System x Rack and Tower 5

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iDataPlex – Navržen pro větší flexibilitu datových center Široké portfolio komponent, které se přizpůsobí výpočetním potřebám Vašich zákazníků Rack Management Appliance

2U Chassis

Server Tray Dual GPU IOTray

iDataPlex Rear Door Heat Exchanger

Storage Tray Storage Drives & Network Options

Switches (front)

PDUs (rear) 3U Chassis

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IBM Flex System Enterprise Chassis Enterprise Chassis Design

Chassis

Fans

CMM

Standard Node bays 14 Node Bays (7 Full Wide)

Infrastruktura

Nejširší konektivita na trhu včetně podpory technologií Ethernet, FC, FCoE, ISCSi a IB

☺ Flexibilní architektura (flexibilní rozšiřování výpočetního výkonu i úložných kapacit)

☺ Jednoduchá integrovaná správa a nižší provozní N ☺ Nyní podporuje 40 Gb Ethernet, připraveno pro 100 Gb

Power Supplies (6X)

10 U REAR Scalable Switch Bays

• • • • • •

Integrated Integrated Storwize V7000 Storage*

FRONT

4 pozice pro switche 10U Chassis, 14 obsaditelných pozic 6 pozic pro napájecí zdroje Až 8 ventilátorů Light Path diagnostika Integrovaný chassis management modul 7

Integrace bez kompromisů, navrženo pro další dekádu Flex System x240 Compute Node

Výpoč četní výkon

Infrastruktura

Flex System x220 Compute Node

Systemové Portfolio připraveno na velké pracovní zátěže ☺ Snížení nákladů na pořízení nových serverů prostřednictvím virtualizace/ konsolidace ☺ Flexibilita nasazení a možnost integrace se stávajícím HW

Flex System x222 Compute Node* Flex System x440 Compute Node Flex System p260 Compute Node IBM 2S PowerLinux Compute Node Flex System p460 Compute Node IBM PCIe Expansion Node 8

* 2Q13 target

IBM Confidential

Platform Computing

IBM Platform Product Family Platform HPC for System x: • easy-to-use cluster management, User-friendly • GPU scheduling and reporting • Robust commercial MPI library, including IBM Platform MPI

Platform LSF: • • • •

Workload management for HPC Optimal utilization: reduced infrastructure cost High throughput: faster time to results Policy and resource-aware scheduling

Platform Symphony: Low-latency grid management platform for: • distributed computing • big data analytics with sophisticated resource sharing. Deploy standalone or with IBM’s InfoSphere BigInsights.

Platform Cluster Manager: • Quickly provisions, runs, manages and monitors HPC clusters with unprecedented ease • Single & MultiTenant

This presentation is intended for the education of IBM and Business Partner sales personnel. It should not be distributed to customers. 9 © 2013 IBM Corporation 9

Platform Computing

IBM GPFS GPFS

Overview IBM GPFS is a high-performance, distributed, parallel file system that is complementary to both IBM Platform Symphony and IBM Platform LSF

GPFS

GPFS

Key Capabilities •High-performance parallel file system •Supports multiple operating environments and interconnects •A single global namespace •Automated data lifecycle management & archival •Highly reliable storage •File-placement option optimized for Hadoop MapReduce

Maintain a single view of data enterprise-wide to promote collaboration

Local Area Network (LAN)

Client Benefits •Improve application performance and resource utilization by removing data-related bottlenecks •Supports improved collaboration across geographies by providing a global view of data with efficient remote access •Improves cost-efficiency by automatically moving infrequently accessed data to more cost-efficient storage (ie. tape) •Simplification - use a single filesystem for traditional applications as well as Hadoop MapReduce applications

GPFS

Dramatically improve file system performance to remove data bottlenecks

This presentation is intended for the education of IBM and Business Partner sales personnel. It should not be distributed to customers. 10 © 2013 IBM Corporation 10

Platform Computing

Traditional Computing - Capacity Approach Utilization

Invisible cost of delay to productive use &utilization Extended learning period for users

Extended time to fully operational system

It takes a long time to “Sweat the Assets”

Open source management software

Time in months

This presentation is intended for the education of IBM and Business Partner sales personnel. It should not be distributed to customers. © 2013 IBM Corporation 11

Platform Computing

IBM Platform Computing - Productivity Approach

Utilization Faster time to full system readiness

Better throughput & utilization

Faster time to full user productivity

Strong policies drive allocation of resources

“Sweat the Assets” much earlier

Open source management software Platform Computing

Time in months


Platform Computing

The Result: Lower Cost of Ownership 3-year management software TCO for a 32-node cluster Open source cost less, but…

Lost productivity due to integration, troubleshooting & user learning curve

Costly to integrate software components

Source: “The Hidden Cost of Open Source” white paper, Platform Computing, 2012 This presentation is intended for the education of IBM and Business Partner sales personnel. It should not be distributed to customers. © 2013 IBM Corporation 13

Platform Computing

Pro více informací... IBM Technical computing web Clusters for Dummies (brožura) Hadoop for Dummies (brožura)


Konec HPC části…

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© 2013 IBM Corporation

Cloud & IBM Technologie

Cloud becomes top CIO technology priority in 2011 Gartner 2011 CIO priorities

Source: Gartner – Reimagining IT: The 2011 CIO Agenda 17


TCO kalkulace pro konsolidaci a privátní cloud Známé (příp. odhadnuté) náklady: • hw, sw • hw a sw údržbu • facility, energie, prostory • lidské zdroje, ostatní OPEX

Obtížně vyčíslitelné benefity: • ‚hodnota‘ rychlosti poskytnutí služby/provisioning? • ‚hodnota‘ interního billingu, reportingu?

Business Case Summary Total CPU Cores Used CPU Cores Total CPU Sockets #Logical Servers #Physical Servers Ave.Log.Srv RIP Total RIP Capacity Total RIP Workload Ave %Utilization

Current 8,530.00 8,530.00 4,394.0 4441.00 1943.00 1,267.9 5,630,837.1 1,185,482.0 22%

Annual Operating Costs (AOC) Staff Cost Code System Software M&S Hardware Maint Space Electric Staff Depreciation Total AOC est.potential saving /yr

Today 5,623,864 0 210,832 995,135 1,617,461 0 8,447,292

One Time Costs (OTC)

BAU + Cloud 3024 3000 756 4346 378.00 974.2 4,233,750.0 1,185,482.0 28%

Blades + Cloud 2224 1947 278 4346 139.00 547.4 2,378,990.6 1,185,482.0 50%

R O Z V

Cloud 7,727,281 0 36,275 392,319 1,455,715 0 9,611,590 -1,164,298

Fewer servers 7,405,822 0 4,288 260,351 1,310,143 0 8,980,604 -533,312

CloudBurst 1638 1561 273 4346 12.07 547.8 2,380,525.0 1,185,482.0 50%

Change -81% -82% -94% -2% -99% -57% -58% 0% 124%

3 Year Projection Millions

Sizing

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Transition

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Hardware Maint

35 Space 30 Electric 25

CloudBurst 1,214,658 0 11,696 263,086 1,179,129 0 2,668,569 5,778,723

Software Purchase Hardware Purchase Transition Total OTC Write-Off Net Cash Investment

0 13,529,500 0 13,529,500 0

0 2,835,000 0 2,835,000 0 -10,694,500

0 3,118,635 0 3,118,635 0 -10,410,865

0 6,818,058 0 6,818,058 0 -6,711,442

OTC + 3x AOC 3 yr saving Payback Period

38,871,376

31,669,770 7,201,606 9yr2m

30,060,448 8,810,928 19yr6m

14,823,765 24,047,611 -2yr10m

-78% 0% -94% -74% -27% 0% -68%

Depreciation

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Staff

10

Software Purchase

5 Hardware Purchase 0 C

3

2

1

System Software M&S

3 Year Projection

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Project Time 0yr0m


Základní „IBM produkty“ v oblasti budování Cloud Computingu

Foundation

Services

Solutions

IBM Smart Cloud Entry Technologie pro vybudování 19 Cloudu

Platforma IBM Datových Center nabízející služby Veřřejného (Public) Cloudu

Software jako služžba © 2013 IBM Corporation

Smart Cloud Entry: ‚Entry‘ řešení cloudu

Verze 3.0 červen 2013 Samoobslužný portál Katalog služeb Workfow Provisioning a expirace Měření a reportování Intuitivní rozhraní Snadno nasaditelné Privátní cloud VMware KVM a PowerVM + nově HyperV 20

Demo v IBM Inovation Centru, Praha Chodov


Závěr Cloud části…

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BAO: In-Memory, HANA, Big Data

Klasická architektura In Memory koncept In Memory řešení SAP HANA BigData MapReduce/Hadoop/BigInsight InfoSphere Streams

BigData Demo v IBM IIC nebo TeC v přípravě

CIO #1 Concern Business Analytics

83% 76%

Virtualization 71%

Risk Management & Compliance Mobility Solutions

68%

Customer & Partner Collaboration

68%

Self-service Portals Application Harmonization

64%

Business Process Management

64%

SOA / Web Services Unified Communications

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66%

Source: IBM Global CIO Study, n = 2345

61% 60%

Komponenty

Sizing!!! Architektura Data Warehouse

Cubing Services Agregace Indexes

ETL

Operational Source Systems Structured/ Unstructured Data

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Implementation Services

DB2 Utilities Suite


In-Memory koncept: Rychlost RAM, SSD a pevných disků Non-Persistent, Volatile Processors

Memory

Very, very, very, very, very fast

Very, very, very fast

< 10’s ns

~100 ns

Persistent, Non-Volatile Disk

SSD

Fast

~200,000 ns

Very, very slow comparatively

1,000,000 8,000,000 ns

In-Memory DBPřístupová rychlost Klasická DB

~1 second

~33 minutes

~ 12.5 hours

Srozumitelnější pro člověka…


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IBM In Memory řešení Cognos TM1 IBM SAP HANA Appliance IBM DB2 BLU IBM Informix In-Memory … a další real-time calculations, what-if and ad-hoc analysis, data refresh and write-back In-memory: the whole multidimensional database runs in the physical memory so everything is incredibly fast Real-time both data refresh and calculations run real-time 26


IBM Systémy pro koncept SAP HANA velikosti/stavební bloky/možžnosti rozšířření Scale-up… ažž na XXL (další slide) Size

XS

S

S+

M

L

Building Block

x3690 X5

x3690 X5

x3950 X5

x3950 X5

x3950 X5

Part Number (Sys x - config)

7147-HAx

7147-HBx

7143 - HAx

7143 - HBx

7143 – HBx + 7143 – HCx

Intel CPU

2 x E7 2870

2 x E7 2870

2 x E7-8870

4 x E7-8870

8 x E7-8870

RAM

128 GB DDR3 (8 x 16GB)

256 GB DDR3 (16 x 16GB)

256 GB DDR3 (16 x 16 GB)

512 GB DDR3 (32 x 16 GB)

1 TB DDR3 (64 x 16 GB)

Log Storage

10 x 200 GB 1.8‘‘ MLC SSD

10 x 200 GB 1.8‘‘ MLC SSD

1.2 TB ioDrive2 FusionIO

1.2 TB ioDrive2 FusionIO

2 x 1.2 TB ioDrive2 FusionIO

Data Storage

-

-

8 x 900 GB 10k SAS HDD



Storage Ctrl

2 x M5015

2 x M5015

1 x M5015

1 x M5015

2 x M5015

Data / Log Storage Summary

1.6 TB RAID 5 data and log storage

1.6 TB RAID 5 data and log storage

5.4 TB RAID5 data storage 1 TB log storage



Ethernet

4 x 10 GbE, 6 x 1 GbE

4 x 10 GbE, 6 x 1 GbE

4 x 10 GbE, 6 x 1 GbE

4 x 10 GbE, 6 x 1 GbE

8x 10 GbE, 12 x 1 GbE

Upgrade Option Scale Up or Scale out

XS - > S

Up to 16 node scale out with HA, SAP certified

S+

M L (with L-option) Up to 56 node scale out with HA, SAP certified

L XL or XXL Up to 56 node scale out with HA, SAP certified

Software

Preload: SLES4SAP GPFS SAP HANA*

Preload: SLES4SAP GPFS SAP HANA



Preload: Addtl. GPFS lic.

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M

Scale-out… ažž 16x node S nebo 56x M/L


IBM Systems solution for SAP HANA Scale-up pokrač čování

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Size

XL

XXL

Building Block

x3950 X5

x3950 X5

Part Number (Sys x - config)

tbd

tbd

Intel CPU

8 x E7-8870

8 x E7-8870

RAM

2 TB DDR3 (128 x 16 GB or 64 x 32 GB)

4 TB DDR3 (128 x 32 GB)

Log Storage



Data Storage



Storage Ctrl

2 x M5015

2 x M5015, 1 x M5025

Data / Log Storage Summary



Ethernet

8 x 10 GbE, 12 x 1 GbE

8x 10 GbE, 12 x 1 GbE

Upgrade Option Scale Up or Scale out

XL XXL (with additional memory, ioDrive2, M5025, EXP 2524)

Software




InfoSphere Streams: In-Motion Vs Traditional Analytics Stream Analytics RTAP Non-Traditional / Non- Relational Data Sources

Ultra Low Latency Results

In-Motion Analytics

Audio, Video, emails…

OLAP / OLTP Traditional / Relational Data Sources

Traditional Analytics

(Alpha)Numeric, text…

Warehouse

Streams DOES NOT store data for analysis Traditional data is finite, saved and known. Streaming data is NONE of these

Results

Streams: Industry Focus

Government

Telco

•Security, foreign intelligence, foreign surveillance and cyber security •Complex analysis of data in motion such as audio, video, email etc. •Structured and unstructured data that require highly time sensitive •Security is the #1 spending initiative for the US Gov’t.

•Real time insights into network and subscriber behavior •Improve network performance, reduce customer churn, •Increase loyalty & appeal, prevent spam, •Focus on countries w/ emerging growth leapfrogging hw phone lines •Subscriber base doubling every 2 years.

FSS Algorithmic & high frequency trading, fraud detection in real time .Trading companies reducing costs as well as to add more value to their offerings. Brokerage firms looking to find ways to add significant value to their customers

Energy / Utilities Healthcare •Smart meters, sensors, smart grid •Analysis in real time for energy savings and prevention of outages. •Analytics and complex logic & the need for extreme speed to take action. •Significant ROI is emerging.

•Real time predictive modeling & fast analysis on “Big Data •Analytics on large volumes of physiological data from increasingly sophisticated sensors and medical devices. ie:Patient Monitoring in ICU

BIG DATA: Hadoop Map-Reduce BigInsight • ‚Stream‘ dat • Nestrukturovaná data • Strukturovaná data

BIG DATA: Příklad infrastrukturní architektury

• Paralelní úlohy • HDFS • GPFS • inter-node traffic

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Konec BAO části

Otázky?

Děkujeme za pozornost [email protected] [email protected] 34


Řešení postavená na IBM System x serverech - HPC, Cloud, Business Analytics, SAP HANA, Big Data,

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