Grids and Clouds:
   Tackle the Limits of ICT
   Tackle the Limits of ICT
       Simon C. Lin ( 林誠謙 )
Academia Sinica Grid...
Future of Computing?

•   Computing is about the application of
    Information and Communication Technology

•   “Cambria...
Water, water, everywhere,
nor any drop to drink
S. T. Coleridge, 1797
S. T. Coleridge, 1797
S. T. Coleridge, 1797
Actual Internet Map




•   It looks amazingly similar to
    Neural Net in terms of
    Connectivity
•   It’s a Scale-Fre...
The Internet Hourglass
              Voice, Video, P2P, Email, youtube, ….
              Protocols – TCP, UDP, SCTP, ICMP,...
Heterogeneity


                                         We have to extend the waist and host
       Application         ...
Serge Fdida, ICT’08, Lyon, Nov.08




                             Vision
 “The     Internet only just works”!

 Explore...
There are Limits to
   Everything ...
Exponential Growth World

                                                                       Optical Fibre
           ...
The Impact of Scaling


• All positive benefits bundled
      together
        • Reduced component cost
        • Increase...
The Data Deluge
•   A large novel: 1 Mbyte; The Bible: 5 Mbytes
•   A Mozart symphony (compressed): 10 Mbytes
•   A digita...
Moore’s Law in HPC Machines
         Li m
                i t So
                      on!
                          >   >>
100
      -10
          00x
                Li m
                       i t! >
                                >>




    ...
Power Consumption Challenge for
   More Computing Power


                           <<< Limit!
<<
     <
         Ar
              ch
                   .L
                        im
                             i t!
Tera → Peta Bytes
                        Enabling Grids for E-sciencE




       • RAM time to move                      ...
From Optimizing Architecture to
       Optimizing Organisation

• High-performance computing has
  moved from being a prob...
Simulated Time Can Get Rough




                   << <
                          Proc
                                 ....
Small World Can Cure ...

• This is similar to the roughening
  surfaces of crystalline materials grown
  or the liquid su...
How Large the
Computing Could Be?
•        Quality:
                                     –        Monitoring via Nagios - distributed via official releases,...
SETI@home: 1,000,000 CPUs
            “Poor man’s Grid”




                                                      Voluntee...
Folding@home: >1 petaflop




                                                 Often of Philanthropic Nature
Sony pre-inst...
LHC@home: >3000 CPU-years,
              >60K Volunteers
              >60K Volunteers




                               ...
Volunteer Thinking
Cost of 1 TFLOPS-year
•   Cluster: $145K
     –   Computing hardware; power/AC infrastructure;
         network hardware; ...
Performance
•   Current
     –   500K people, 1M computers
     –   6.5 PetaFLOPS (3 from GPUs, 1.4 from PS3s)
•   Potenti...
History of volunteer computing
     1995            2000               2005             now
         distributed.net, GIMP...
Max CERN/T1-ASGC Point2Point Inbound : 7.3 Gbps
                                  ASGC Introduction

                     ...
由歐洲到台灣每秒能傳送兩部大英百科
全書 -- 歷史記錄

        Max CERN/T1->ASGC Inbound : 7.3 Gbps,
               and Outbound : 5.9 Gbps




   ...
ASGC Profile
• Mission: Building sustainable research and collaboration
  infrastructure
• Objectives:
 • Support research...
e-Science Support @ ASGC

• Strategy
  • Collaborate by sharing: goals, resources, solutions, ...
  • Resource integration...
Resource Status for WLCG


WLCG    CPU(KSI2K)     Disk(TB)        Tape (TB)
2007      1,700          900             800
2...
ASGC 是 EGEE 和 WLCG 的亞太維運中心
• Mission
    •   Provide deployment support facilitating Grid expansion
    •   Maximize the a...
Networking Backbone in Asia (GLIF)




                                     41
Taiwan-ASGC   WLCG/EGEE/EUAsiaGrid
  BEIJING-
               Partners in Asia Pacific
                                    ...
e-Science Application Support
•   Design of collaboration model
•   High Energy Physics
     – ATLAS and CMS Tier1 Center ...
Credit:
Y-T Wu                                  EGEE Biomed DC II – Large Scale
                                        Vi...
Bio-Portal and Virtual Screening Services
             Best Demo Award of EGEE’07 Conference

• x




                    ...
Large Hadron Collider
         LHC@CERN for
      High Energy Physics
PSROC Annual Meeting,     23-
Jan-2007, P. Jenni (CE...
Earthquake Wave Simulation


Simulation of 2004
Earthquake in Taipei
Basin, w/o Geological
Structure consideration




   ...
Cloud Computing
Is It Cloud Computing?
•   It is potentially a form of Grid Computing

•   It is often associated with collections of Virt...
Is It Buzzword evolution?


One distributed computing
buzzword per decade

Metacomputing (~1987, L. Smarr)

Grid computing...
Cloud hype 1




               62
Cloud hype 2
It’s stupidity. It’s worse than stupidity: it’s a
marketing hype campaign. Somebody is saying
this is inevita...
Cloud hype 3
--- Cloud computing
--- Grid computing




                                     64
Cloud hype 4

The Open Cloud Manifesto

“The industry needs an objective,
straightforward conversation
about how this new ...
Why Cloud Computing ?
     • Innovative business models require a utility-like intelligent
                    Because it ...
Why now ?
     • Broadband networks
     • Adoption of Software as a Service
        – Salesforce.com
        – Web 2.0 mi...
Is it really new ?
      •   Massive scale resource sharing over the Internet
      •   Sound a lot like grid computing, y...
Definitions 1


Enterprise Grid      Service Grid   Clouds




                                             69
Definitions 2


Service grids are systems that federate, share and coordinate distributed
resources from different organiz...
Definitions 3



Cooperative harvesting     Contract harvesting
   (Service Grid)               (Cloud)




              ...
Grids vs. Clouds 1




                     72
Grids vs. Clouds 2




                     73
Grids vs. Clouds 3


            Cost of 1 Teraflop-year

Cloud: $1.75M
Amazon EC2 rates

Cluster: $145K
Hardware (computi...
Conclusion

“ IT is a cost center, after all, not so dissimilar from janitorial and cafeteria services,
both of which have...
Opportunities
Grid & Cloud: opportunities

Many cloud offerings = competition
Amazon Elastic Cloud Computing (EC2); IBM Blue Cloud; Micr...
Where would clouds come in?
                           Enabling Grids for E-sciencE



   • Majority of EGEE’s CPUs are at...
Production & Volunteer grids
                           Enabling Grids for E-sciencE




EGEE-III INFSO-RI-222667         ...
Extendable Infrastructure
                           Enabling Grids for E-sciencE




EGEE-III INFSO-RI-222667            ...
Conclusion
Grid Computing
My preferred definition:


Grid computing is distributed computing
performed transparently across multiple
...
Grid - its really about collaboration!


• It’s about sharing
  and building a vision
  for the future
    – And it’s abou...
Conclusion


• Both Grids and Clouds are evolutions of Virtualisation
• Virtulaisation will provide enormous flexibility i...
90
91
92
Embracing a new vision for Science

 Virtual Research Communities
 Improved scientific process… role of simulation
 Cro...
What is e-Science?

• “e-Science is about global collaboration in
  key areas of big science and the next
                ...
http://www.rcuk.ac.uk/escience/

• What is meant by e-Science? In the future, e-
  Science will refer to the large scale s...
Grid Concept
• Analogy with the electrical power grid
    –   “On-demand” access to ubiquitous distributed computing
    –...
Attributes of On-demand Computing

• Open, Integrated, Virtualised, Autonomic
• Open: Open Source model and Open
  Archite...
Grid computing is NOT…
• …launching isolated jobs onto medium-sized or big iron, as is the case for

  • most work being d...
Who invented WWW ?

• Tim Berners-Lee of CERN proposed
  the idea of W W in 1989, a revised
                 W
  version w...
WWW Concept, CERN 1989,
       1990




                          100
First W W Brow
       W      ser, Dec
         1990




                         101
The Rest is History

• 1991, CERN distributed the first
  W W system to High Energy
    W
  Physics circle, followup by
  ...
Paradigm Shift requires New Tools and
         New Thinkings

• due to Thomas Kuhn, “The Structure of Scientific
  Revolut...
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙
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Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙

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雲端運算其實就是一種分散式運算,跟許多人熟知的網格運算(Grid Computing)非常相似,都是科技自然演化下為了突破當時運算架構限制的創新技術,兩者應用層面不同,並不會有相互排斥的問題。中研院網格計畫主持人林誠謙表示:「網格運算著重在硬體平台之間的相互合作,以創造更快、更高的運算能力,而雲端計算則是讓個人電腦與行動裝置能透過網路,隨時隨地都能存取應用伺服器所提供的服務。」
在網格運算投入非常多心力的IBM,近年來不但轉型成為資訊服務提供商,也非常積極投入雲端運算的領域,並且在2008年與IDA在愛爾蘭共同成立歐洲第一個雲端運算中心。

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  • Moore’s Law – Individual computers double in processing power every 18 monthsStorage Law – disk storage capacity doubles every 12 Months Gilder’s Law – Network bandwidth doubles every 9 months (but is harder to install)This exponential growth profoundly changes the landscape of Information technology(High-speed) access to networked information becomes the dominant feature of future computingFor large-scale images: secure remote access eventually becomes routine
  • 1. Moore’s Law – Transistor Density doubles thus Individual computers double in processing power every 18 months2. This exponential growth profoundly changes the landscape of Information Technology
  • 1. Moore’s Law implies 101.6 times growth in 10 years, 256 times in 12 years2. In the last 10 years, in particular, 10,000 times performance gain; this is mainly due to clever use of Parallel programming in some application area3. How to write and Ease of Use of Massively Parallel Computers are essential!
  • 1. Higher transistor integration and higher frequency alone result too much heat for more performance.2. One needs a holistic approach to deal with heat problem3. Multicore with multithreading architecture helps to spread power dissipation over a greater area
  • Since the global e-Infrastructure is establishing quickly, we believe it is an excellent opportunity to take advantage of sharing and collaboration to bridge the gap between Asia and the world as well as creating new regional cooperation opportunities in Asia Pacific. ASGC as one of the earliest partner in Asia
  • ASGC is actually a center for e-Science, HEP is the largest and first (power) user of ASGC, and we also learnt a lot from HEP community for collaboration EUAsiaGrid: with 11 Asia partners & 4 European partners
  • Are we able to do e-Science ? sharing sometimes also means complement)
  • from site deployment, certification, services and to sustainable operationsOur participation to other EGEE activities, such as application support and development, dissemination and training will not be discussed here. Only the sustainable model for AP regional is raised here.
  • AARNet also has 2 x 10Gb to US
  • [Introduce the ASGCNet backbone first] first 10G network between Asia and Europe, 2.5G link to US and extend to NL as the Europe link backup. In Asia, we upgrade to have 2.5 G links to both JP and HK from 2008, and another 622Mb link to Singapore. We are keen to share all the network with APAN and TEIN and Asia partners.
  • What we are doing in Taiwan for EUAsiaGrid and the regional collaboration is to provide gLite infrastructure, and to enable e-Infrastructure services for users
  • There is also an important component of EGEE-II in Business Partners and Industry Take-up such as Industry Task Force and Industry Forum. It also organises EGEE Business Associates (EBA).
  • Prediction of earthquake is still not possible at this moment, through the simulation of wave propagation and impacts with assumed hypocenter, we could understand the possible threat to areas scatter from the epicenter. Then, the strategy for mitigation and protection would be verified accordingly. Here is exmaple of simulation of 3D amplification effects in Taipei basin by the wave-field propagation simulation with considering the complex geometry structure, extraordinary hazards could be identified even the epicenter is far from the target area.
  • 1. For the un-precedented scale of collaboration, the High Energy Physics Community has the most experience.2. Strategically, the world-wide funding agencies use HEP community to build the 1st Global production Grid3. With the Hope that this will extend to other e-Science and HPC application areas by EGEE and OSG!
  • Transcript of "Cloud Computing,雲端運算-中研院網格計畫主持人林誠謙"

    1. 1. Grids and Clouds: Tackle the Limits of ICT Tackle the Limits of ICT Simon C. Lin ( 林誠謙 ) Academia Sinica Grid Computing, Taipei, Taiwan 11529 Simon.Lin@twgrid.org 28 April 2009, TrendMicro Clouds
    2. 2. Future of Computing? • Computing is about the application of Information and Communication Technology • “Cambrian explosions” in ICT, rich diversity in services, middleware, commodity hardware, devices and sensors
    3. 3. Water, water, everywhere, nor any drop to drink S. T. Coleridge, 1797 S. T. Coleridge, 1797 S. T. Coleridge, 1797
    4. 4. Actual Internet Map • It looks amazingly similar to Neural Net in terms of Connectivity • It’s a Scale-Free Network • Everything over IP • Always-On • Everywhere
    5. 5. The Internet Hourglass Voice, Video, P2P, Email, youtube, …. Protocols – TCP, UDP, SCTP, ICMP,… Everything on IP Application Changing/updating layer the Internet core is difficult or Homogeneous impossible ! networking abstraction (e.g. Multicast, Mobile IP, QoS, …) IPv6 IP IPvX Link layer Disruptive IP on approaches need Everything a disruptive Ethernet, WIFI (802.11), ATM, architecture SONET/SDH, FrameRelay, modem, ADSL, Cable, Bluetooth… 5 martin.may@thomson.net
    6. 6. Heterogeneity  We have to extend the waist and host Application more paradigms layer  Future networks have to scale in size AND functionality Pub/Sub Sensor Home NW Generic ???  Enable network evolution IP CLEAN SLATE framework  Federation instead of homogeneous Link abstraction layer We need a framework that is able to host multiple networks ANA PROJECT (J02) 6 martin.may@thomson.net
    7. 7. Serge Fdida, ICT’08, Lyon, Nov.08 Vision  “The Internet only just works”!  Explore the possible Future(s) of the Internet • The future Internet might be Polymorphic • Multiple Federated Internets • Content, Wireless, DTN, Things, …  What is the foundation of this future? – Is there a Network Science? – Experimentally driven research & testing The Disappearing Internet! 3 7
    8. 8. There are Limits to Everything ...
    9. 9. Exponential Growth World Optical Fibre Doubling Time Performance per Dollar Spent (months) (bits per second) Gilder’s Law (32X in 4 yrs) 9 12 18 Data Storage (bits per sq. inch) Storage Law (16X in 4yrs) Chip capacity (# transistors) Moore’s Law (5X in 4yrs) 0 1 2 3 4 5 • Rock's Law: the cost of leading-edge plant Number of Years doubles every three years (now at 3-4 billion USD) Triumph of Light – Scientific American. George Stix, January 2001 9 Induction: What are Grids and e-Science? –May 18th, 2004 -
    10. 10. The Impact of Scaling • All positive benefits bundled together • Reduced component cost • Increased functionality • Higher performance • Reduced component size • Smaller energy consumption • The Information Revolution • Rapid expansion of the use of integrated circuits in all products and services • Wide penetration of digital technologies in all areas of society and economy Quality-adjusted price of semiconductors Source: Aizcorbe, Oliner, Sichel (2006): Shifting trends in semiconductor prices and the pace of technical progress. Finance and Economics Discussion Series, Federal Reserve Board, Washington, D.C. © I. Tuomi 25 Nov. 2008 page:
    11. 11. The Data Deluge • A large novel: 1 Mbyte; The Bible: 5 Mbytes • A Mozart symphony (compressed): 10 Mbytes • A digital mammogram: 100 Mbytes • OED on CD: 500 Mbytes, Digital movie (compressed): 10 Gbytes • Annual production of refereed journal literature (∼20 k journals; ∼2 M articles): 1 Tbyte • Library of Congress: 20 Tbytes • The Internet Archive (10 B pages) (From 1996 to 2002): 100 Tbytes • Annual production of information (print, film, optical & magnetic media): <<< Li m 1500 to 3000 Pbytes. From 2008, annual production will be greater than total available storage space! it and C hallenge • All Worldwide Telephone communication in 2002: 19.3 ExaBytes. ! Global Internet traffic will reach EB order next year! • Moore’s Law enables instruments and detectors to generate unprecedented amount of data in all scientific disciplines 11
    12. 12. Moore’s Law in HPC Machines Li m i t So on! > >>
    13. 13. 100 -10 00x Li m i t! > >> 13
    14. 14. Power Consumption Challenge for More Computing Power <<< Limit!
    15. 15. << < Ar ch .L im i t!
    16. 16. Tera → Peta Bytes Enabling Grids for E-sciencE • RAM time to move • RAM time to move – 2 months – 15 minutes • 1Gb WAN move time • 1Gb WAN move time – 14 months ($1 million) – 10 hours ($1000) • Disk Cost • Disk Cost – 6800 Disks + 490 units + 32 – 7 disks = $5000 racks = $7 million (SCSI) • Disk Power • Disk Power – 100 Kilowatts – 100 Watts • Disk Weight • Disk Weight – 33 Tonnes – 5.6 Kg • Disk Footprint • Disk Footprint – 60 m2 <<< – Inside machine Li m i t to Sc a ling! May 2003 Approximately Correct Distributed Computing Economics Jim Gray, Microsoft Research, MSR-TR-2003-24 INFSO-RI-508833 EGEE Training Materials, 2004 16
    17. 17. From Optimizing Architecture to Optimizing Organisation • High-performance computing has moved from being a problem of optimizing the architecture of an individual supercomputer to one of optimizing the organization of large numbers of ordinary computers operating in parallel. Scott Kirkpatrick, SCIENCE Vol. 299, 2003, p668 17
    18. 18. Simulated Time Can Get Rough << < Proc .#L imit ! 18
    19. 19. Small World Can Cure ... • This is similar to the roughening surfaces of crystalline materials grown or the liquid surface in a very week gravitational field • Roughening becomes a problem when measurements must be continuously extracted from the complex simulation • Temporal roughening can be eliminated by synchronising with SuperNode in a Small World 19
    20. 20. How Large the Computing Could Be?
    21. 21. • Quality: – Monitoring via Nagios - distributed via official releases, configured through YAIM, integrated with other tools Enabling Grids for E-sciencE – Gradual implementation of Service Level Agreement with sites – Result: 85% of sites are now above the 75% availability threshold • Geographical expansion: – now have production sites all across Asia: Australia, China, India, Japan, Korea, Malaysia, Pakistan, Taiwan, Thailand  In certification: Indonesia, Philippines, Vietnam ~280 sites 45 countries >80,000 CPUs >25 PetaBytes >14,000 users >250,000 jobs/day EGEE-III INFSO-RI-222667 EGEE - Bob Jones - OGF25/User Forum - 2-6 March 2009
    22. 22. SETI@home: 1,000,000 CPUs “Poor man’s Grid” Volunteer Computing BOINC (Berkeley Open Infrastructure for Network Computing) platform launched in 2003. General-purpose open-source platform for client-server model of distributed computing. >50 volunteer computing projects today in a wide range of sciences. Not all use BOINC.
    23. 23. Folding@home: >1 petaflop Often of Philanthropic Nature Sony pre-installs folding@home on Playstation-III – user can choose to run in background. 50k PS3s make first distributed petaflop machine (Guiness World Record Sept 2007).
    24. 24. LHC@home: >3000 CPU-years, >60K Volunteers >60K Volunteers Citizen Cyber-science Projects Fortran program Sixtrack simulates LHC proton beam stability for 10 -106 orbits. 5 Include real magnet parameters, beam-beam effects. Predict stable operation conditions.
    25. 25. Volunteer Thinking
    26. 26. Cost of 1 TFLOPS-year • Cluster: $145K – Computing hardware; power/AC infrastructure; network hardware; storage; power; sysadmin • Cloud: $1.75M • Volunteer: $1K - $10K – Server hardware; sysadmin; web development
    27. 27. Performance • Current – 500K people, 1M computers – 6.5 PetaFLOPS (3 from GPUs, 1.4 from PS3s) • Potential – 1 billion PCs today, 2 billion in 2015 – GPU: approaching 1 TFLOPS – How to get 1 ExaFLOPS: • 4M GPUs * 0.25 availability – How to get 1 Exabyte: • 10M PC disks * 100 GB
    28. 28. History of volunteer computing 1995 2000 2005 now distributed.net, GIMPS SETI@home, Folding@home Applications Climateprediction.net Predictor@home IBM World Community Grid Einstein@home Rosetta@home ... Academic: Bayanihan, Javelin, ... Middleware Commercial: Entropia, United Devices, ... BOINC
    29. 29. Max CERN/T1-ASGC Point2Point Inbound : 7.3 Gbps ASGC Introduction 1. Most Reliable T1: 98.83% 2. Very Highly Performing and most Stable Site in CCRC08 Asia Pacific Regional A Worldwide Grid Operation Center Infrastructure >280 sites, 45 countries >80,000 CPUs, >25 PetaBytes >14,000 users, >200 VOs >250,000 jobs/day Best Demo Award of EGEE’07 Grid Application Platform Avian Flu Drug Discovery Large Hadron Collider (LHC) Lightweight Problem Solving Framework 34
    30. 30. 由歐洲到台灣每秒能傳送兩部大英百科 全書 -- 歷史記錄 Max CERN/T1->ASGC Inbound : 7.3 Gbps, and Outbound : 5.9 Gbps 35
    31. 31. ASGC Profile • Mission: Building sustainable research and collaboration infrastructure • Objectives: • Support research by e-Science, on data intensive sciences and applications require cross disciplinary distributed collaboration • Milestone • Operational from the deployment of LCG0 since 2002 , and takes the Tier-1 Center responsibility from 2005 • Federated Taiwan Tier-2 center -- Taiwan Analysis Facility (TAF) is also collocated in ASGC • Rep. of EGEE e-Science Asia Federation, while joining EGEE from 2004 • Providing Asia Pacific Regional Operation Center (APROC) services to Asia Pacific WLCG/EGEE sites from 2005 • Initiate Avian Flu Drug Discovery Project and collaborate with EGEE in 2006 • Start of EUAsiaGrid Project from April 2008 3
    32. 32. e-Science Support @ ASGC • Strategy • Collaborate by sharing: goals, resources, solutions, ... • Resource integration for effective utilization • Enabled by e-Infrastructure services • Scalable Resource & flexible solution: working with Users • Internet-wide Collaboration Environment • High performance networking • Service Grid + Desktop Grid + Cloud --> new technology • Target Applications: HEP, Life Science, Earth Science, Climate Change, and long-term preservation 38
    33. 33. Resource Status for WLCG WLCG CPU(KSI2K) Disk(TB) Tape (TB) 2007 1,700 900 800 2008 3,400 1,500 1,300 5,500 2,500 1,300 2009 5,000 3,000 3,000 2010 7,000 3,500 3,500 2011 8,000 4,000 4,500 2012 9,000 4,500 4,500 2013 10,000 4,500 4,500 39
    34. 34. ASGC 是 EGEE 和 WLCG 的亞太維運中心 • Mission • Provide deployment support facilitating Grid expansion • Maximize the availability of Grid services • Supports EGEE sites in Asia Pacific since April 2005 • 26 production sites in 10 countries, 12 VOs • Over 5,300 CPU Cores, 3 PetaByte Disk Space by end of 2008 • Runs ASGCCA Certification Authority since 2003 • Middleware installation support • Production resource center certification • Operations Support • Monitoring • Diagnosis and troubleshooting • Problem tracking • Security
    35. 35. Networking Backbone in Asia (GLIF) 41
    36. 36. Taiwan-ASGC WLCG/EGEE/EUAsiaGrid BEIJING- Partners in Asia Pacific PK-PAKGRID PK-NCP Tokyo- LCG2 PKU IP Transit BEIJING- CERNET LCG2 SINET KEK1 SDU- CSTNET LCG2 WIDE KEK2 HARNET NL JP HK-HKU STM-64 STM-16 MY- IN-TIFR MIMOS HKIX STM-16 I2 / HK TW US GN2 TH-NETEC, STM-16 IN-VECC HAII STM-4 PH- AdMU, ASTI SG TW-THU VN-IAMI TWAREN/ KREONET TANET TW- NTCU KISTI- GCRT AARNET TW-NCUCC KISTI- AU- HEP AU- TW- UNIMELB- SG-NGO TW-FTT TW-IPAS TW-NIU ATLAS NCUHEP LCG2 KNU
    37. 37. e-Science Application Support • Design of collaboration model • High Energy Physics – ATLAS and CMS Tier1 Center and APROC Services, Geant4, and other experiments • Life Science Application – Support of gLite-enabled Virtual Screening Service – Improve autodocking drug discovery performance & scalability with the DIANE2 – Initiate Avian Flu Data Challenge II Refine project at EUAsiaGrid • Disaster Mitigation – Provide wave simulation applications of earthquake and tsunami, for the mitigation planning and evaluation – Build up GEOGrid on earthquake in corporation with Taiwan Earthquake Data Center. – Related EGEE applications survey (e.g., DEGREE) and collaboration • Carbon Flux Application – Promote the Grid Application Platform (GAP) to the local ecologist – Migrate Carbon Flux data and integrate SRM-SRB interface in GAP system and 2 43 gLite
    38. 38. Credit: Y-T Wu EGEE Biomed DC II – Large Scale Virtual Screening of Drug Design Enabling Grids for E-sciencE • Biomedical goal – accelerating the discovery of novel potent inhibitors thru minimizing non-productive ct! trial-and-error approaches oje d! – improving the efficiency of high throughput Pr lise screening OM obi I SD U m W CP d an 000 • Grid goal an ! 5, aiw eks a grid- , T We – massive throughput: reproducing GC of preparation enabled in silico process (exercised in DC H5 I) with a shorterAS n 4 time N1 i by ars d te -Ye – interactive feedback: evaluating an a iti C U alternativePlight-weight grid application In 7 framework (DIANE) 13 Credit: Y-T Wu EGEE-II INFSO-RI-031688 EU-SEA Singapore 2006
    39. 39. Bio-Portal and Virtual Screening Services Best Demo Award of EGEE’07 Conference • x Total computing power used 137 cpu-year 8 variants against 400k compounds 45
    40. 40. Large Hadron Collider LHC@CERN for High Energy Physics PSROC Annual Meeting, 23- Jan-2007, P. Jenni (CERN) The ATLAS and CMS Experiments at LHC
    41. 41. Earthquake Wave Simulation Simulation of 2004 Earthquake in Taipei Basin, w/o Geological Structure consideration 47
    42. 42. Cloud Computing
    43. 43. Is It Cloud Computing? • It is potentially a form of Grid Computing • It is often associated with collections of Virtual machines, aiming at 100 times larger than Data Centres • Usually, clouds do not cross administrative domain • Opportunity to change from Provider focused to User focused • Pushed by Vendours
    44. 44. Is It Buzzword evolution? One distributed computing buzzword per decade Metacomputing (~1987, L. Smarr) Grid computing (~1997, I. Foster, K. Kesselman) Cloud computing (~2007, E. Schmidt ?) 61
    45. 45. Cloud hype 1 62
    46. 46. Cloud hype 2 It’s stupidity. It’s worse than stupidity: it’s a marketing hype campaign. Somebody is saying this is inevitable — and whenever you hear somebody saying that, it’s very likely to be a set of businesses campaigning to make it true. Richard Stallman, quoted in The Guardian, September 29, 2008 The interesting thing about Cloud Computing is that we’ve redefined Cloud Computing to include everything that we already do. . . . I don’t understand what we would do differently in the light of Cloud Computing other than change the wording of some of our ads. Larry Ellison, quoted in the Wall Street Journal, September 26, 2008 63
    47. 47. Cloud hype 3 --- Cloud computing --- Grid computing 64
    48. 48. Cloud hype 4 The Open Cloud Manifesto “The industry needs an objective, straightforward conversation about how this new computing paradigm will impact organizations, how it can be used with existing technologies, and the potential pitfalls of proprietary technologies that can lead to lock-in and limited choice.” OpenCloudManifesto.org Illustration by David Simonds The Economist April 09 65
    49. 49. Why Cloud Computing ? • Innovative business models require a utility-like intelligent Because it makes sense !!! infrastructure that embraces complexity to be successful in the competitive, fast-paced, services-based global economy. – ECONOMICS: Small up front investment and can be billed by consumption. Reduction of TCO allows clients to pursue operational efficiency and productivity. SaaS PaaS IaaS – RISK MANAGEMENT: Small up front commitment allows clients to try many new services faster and choose. This reduces big failure risks and allows clients to be innovative. – TIME TO MARKET: Adopt new services quickly for pilot usages and scale quickly to global scale. – INFORMATION SOCIETY: Value-added information generated by collection and analysis of massive amounts of unstructured data. – UBIQUITOUS SOCIETY: Accessible via a heterogeneous set of devices (PC, phone, telematics..) 66 www.reservoir-fp7.eu OGF 25/EGEE User Forum, Catania Italy March 2-6, 2009
    50. 50. Why now ? • Broadband networks • Adoption of Software as a Service – Salesforce.com – Web 2.0 mindset • Fast penetration of virtualization technology for x86- based servers – Virtual appliances – General purpose on-line virtual machines that can do anything 67 www.reservoir-fp7.eu OGF 25/EGEE User Forum, Catania Italy March 2-6, 2009
    51. 51. Is it really new ? • Massive scale resource sharing over the Internet • Sound a lot like grid computing, yet … Grid Cloud • Highly specialized resources that Reducing CAPEX, OPEX, time to • need to be shared by thousands market [researchers] • Millions of users that share to • Large data sets save not for the sake of sharing • Sharing is a goal • Providers want market share and • In many cases, providers are customer lock-in also consumers • Need for interoperability driven by • Interoperable by design customers 68 www.reservoir-fp7.eu OGF 25/EGEE User Forum, Catania Italy March 2-6, 2009
    52. 52. Definitions 1 Enterprise Grid Service Grid Clouds 69
    53. 53. Definitions 2 Service grids are systems that federate, share and coordinate distributed resources from different organizations which are not subject to centralized control, using standard, open, general-purpose protocols and interfaces to deliver non-trivial qualities of service. Service grids are used by Virtual Organisations, thematic groups of users crossing administrative and geographical boundaries. Cloud computing is an on-demand service offering a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or software services) in a pay-per-use model. Clouds are usually commercial and use proprietary interfaces. 70
    54. 54. Definitions 3 Cooperative harvesting Contract harvesting (Service Grid) (Cloud) 71
    55. 55. Grids vs. Clouds 1 72
    56. 56. Grids vs. Clouds 2 73
    57. 57. Grids vs. Clouds 3 Cost of 1 Teraflop-year Cloud: $1.75M Amazon EC2 rates Cluster: $145K Hardware (computing, network, storage); power; infrastructure; sysadmin Volunteer: $1K - $10K Server hardware; sysadmin; web development 74
    58. 58. Conclusion “ IT is a cost center, after all, not so dissimilar from janitorial and cafeteria services, both of which have long been outsourced at most enterprises. Security concerns won't necessarily prevent companies from wholesale outsourcing of data services: businesses have long outsourced payroll and customer data to trusted providers. Much will depend on the specific company, of course, but it's unlikely that smaller enterprises will resist the economic logic of utility computing. Bigger corporations will simply take longer to make the shift.” Nicholas Carr in The Big Switch: Rewiring the World, from Edison to Google 77
    59. 59. Opportunities
    60. 60. Grid & Cloud: opportunities Many cloud offerings = competition Amazon Elastic Cloud Computing (EC2); IBM Blue Cloud; Microsoft Azure Services Platform; Sun Open Cloud Initiative; Google App Engine; Salesforce.com Force.com Cloud; GoGrid Cloud Hosting; RackSpace Cloud Hosting; Flexiscale Utility Computing on Demand… Some academic/open-source variants emerging Eucalyptus; Nimbus, OpenNebula,… Experiments in Grid + Cloud BalticCloud,StratusLab, VirtCloud, …some to be discussed in this session 79
    61. 61. Where would clouds come in? Enabling Grids for E-sciencE • Majority of EGEE’s CPUs are at the sites • The interaction with site resources is ‘simple’ In: Authorization, Compute & Storage Out: Monitoring & Accounting • Additional Site Requirements VO specific site services VO specific applications deployed to the worker nodes • Provide classical ‘cloud’ scale out capability Potentially, with VO specific worker nodes EGEE-III INFSO-RI-222667 EGEE - Bob Jones - OGF25/User Forum - 2-6 March 2009
    62. 62. Production & Volunteer grids Enabling Grids for E-sciencE EGEE-III INFSO-RI-222667 EGEE - Bob Jones - OGF25/User Forum - 2-6 March 2009
    63. 63. Extendable Infrastructure Enabling Grids for E-sciencE EGEE-III INFSO-RI-222667 EGEE - Bob Jones - OGF25/User Forum - 2-6 March 2009
    64. 64. Conclusion
    65. 65. Grid Computing My preferred definition: Grid computing is distributed computing performed transparently across multiple administrative domains Notes: Computing means any activity involving digital information -- no distinction between numeric/symbolic, or numeric/data/viz Transparency implies minimal complexity for users of the technology Peter Coveney (P.V.Coveney@ucl.ac.uk)
    66. 66. Grid - its really about collaboration! • It’s about sharing and building a vision for the future – And it’s about getting connected • It’s about the democratization of science • It takes advantage of Open Source! Source: Vicky White
    67. 67. Conclusion • Both Grids and Clouds are evolutions of Virtualisation • Virtulaisation will provide enormous flexibility in both Grids and Clouds management • Grids and Clouds are opportunity to each other! • The ICT is always changing, nothing is guaranteed to prevail • Taking advantage of your Core Competence is the most important issue! 89
    68. 68. 90
    69. 69. 91
    70. 70. 92
    71. 71. Embracing a new vision for Science  Virtual Research Communities  Improved scientific process… role of simulation  Cross-disciplinarity  Data deluge - Wet-labs versus ICT infrastructures Application pull revolution networking in science & value added of distributed engineering, Technology push grids collaborative research instrumentation research & (virtual organisations) computing education data curation… •••
    72. 72. What is e-Science? • “e-Science is about global collaboration in key areas of big science and the next onal rnati ted generation of infrastructure that will of inte ra enable it” n ce orta d their integ im p ing grow d senso r s a n ms lec ts es a n e d tea e ref tellit ribut e-S cienc es, sa y dist ri ys is b by Dr John Taylor lab orato anal Director General of the UK Research Councils 94
    73. 73. http://www.rcuk.ac.uk/escience/ • What is meant by e-Science? In the future, e- Science will refer to the large scale science that will increasingly be carried out through distributed global collaborations enabled by the Internet. Typically, a feature of such collaborative scientific enterprises is that they will require access to very large data collections, very large scale computing resources and high performance visualisation back to the individual user scientists. 95
    74. 74. Grid Concept • Analogy with the electrical power grid – “On-demand” access to ubiquitous distributed computing – Transparent access to multi-petabyte distributed data bases – Easy to plug resources into – Complexity of the infrastructure is hidden • “When the network is as fast as the computer's internal links, the machine disintegrates across the net into a set of special purpose appliances” (George Gilder) Source: Ian Foster 96
    75. 75. Attributes of On-demand Computing • Open, Integrated, Virtualised, Autonomic • Open: Open Source model and Open Architecture • Integrated and Virtualised: Invisible Computing, hide all system complexities • Autonomic: Integrate redundant and replicated commodity components to create a highly-available system instead of expensive hardware solutions 97
    76. 76. Grid computing is NOT… • …launching isolated jobs onto medium-sized or big iron, as is the case for • most work being done on TeraGrid machines • most work being done on National Grid Service What added value is there to “grid-enabling” NGS and TeraGrid machines? • …talking about middleware, specifications and standards Point of information: User No. 000002 of OGSA-DAI on the NGS works in my group • What has been happening in all those “informatics” projects that has advanced grid computing? Peter Coveney (P.V.Coveney@ucl.ac.uk)
    77. 77. Who invented WWW ? • Tim Berners-Lee of CERN proposed the idea of W W in 1989, a revised W version w Robert Cailliau next year ith • WWW is to solve the difficulty of academic information sharing, you don’t have to login to separate accounts to get informtion • “The philosophy that academic information is for all, and it is our duty to make information available. WWW... ” --- Tim Berners-Lee, 1991 99
    78. 78. WWW Concept, CERN 1989, 1990 100
    79. 79. First W W Brow W ser, Dec 1990 101
    80. 80. The Rest is History • 1991, CERN distributed the first W W system to High Energy W Physics circle, followup by universities and research institutes • Dec 1991, SLAC built the first W eb Server in US • 1993, NCSA developed an easier to use M osaic Brow ser • 1994, “Year of the Web”. 10,000 102 servers, 2,000 commercial, 10 Million
    81. 81. Paradigm Shift requires New Tools and New Thinkings • due to Thomas Kuhn, “The Structure of Scientific Revolutions”, 1962 • Paradigm is a framework of thoughts • Physics originated from common sense and rationality • Greek knew the radius of Earth • Aristotle’s Force and Motion • Galilei’s Mechanics (Metaphor: clock and watch) • Matter and Energy (Heat Engine and Industrial Revolution) • If Computer is the metaphor of 21st Century, what is the Physics behind? 103
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