Grid Computing Overview

1. Grid Computing Overview Federating Compute and Storage Resources to  Accelerate Science and Aid Collaboration Ian Stokes‐Rees, PhD Harvard Medical School, Boston, USA http://portal.sbgrid.org ijstokes@hkl.hms.harvard.edu

2. Slides and Contact ijstokes@hkl.hms.harvard.edu http://linkedin.com/in/ijstokes http://slidesha.re/ijstokes-grid2011 Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

3. Slides and Contact ijstokes@hkl.hms.harvard.edu http://linkedin.com/in/ijstokes http://slidesha.re/ijstokes-grid2011 http://www.sbgrid.org http://portal.sbgrid.org http://www.opensciencegrid.org Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

4. ScientiFic Research Today • International collaborations • IT becomes embedded into research process: data, results,  analysis, visualization • Crossing institutional and national boundaries • Computational techniques increasingly  important • ... and computationally intensive techniques as well • requires use of high performance computing systems • Data volumes are growing fast • hard to share • hard to manage • ScientiFic software often difFicult to use • or to use properly • Web based tools increasingly important • but often lack disconnect from persisted and shared results Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

5. SBGrid Consortium Cornell U. Washington U. School of Med. R. Cerione NE-CAT T. Ellenberger B. Crane R. Oswald D. Fremont S. Ealick C. Parrish Rosalind Franklin NIH M. Jin H. Sondermann D. Harrison M. Mayer A. Ke UMass Medical U. Washington T. Gonen U. Maryland W. Royer E. Toth Brandeis U. UC Davis N. Grigorieff H. Stahlberg Tufts U. K. Heldwein UCSF Columbia U. JJ Miranda Q. Fan Y. Cheng Rockefeller U. Stanford R. MacKinnon A. Brunger Yale U. K. Garcia T. Boggon K. Reinisch T. Jardetzky D. Braddock J. Schlessinger Y. Ha F. Sigworth CalTech E. Lolis F. Zhou P. Bjorkman Harvard and Affiliates W. Clemons N. Beglova A. Leschziner G. Jensen Rice University S. Blacklow K. Miller D. Rees E. Nikonowicz B. Chen A. Rao Y. Shamoo Vanderbilt J. Chou T. Rapoport Y.J. Tao Center for Structural Biology J. Clardy M. Samso WesternU W. Chazin C. Sanders M. Eck P. Sliz M. Swairjo B. Eichman B. Spiller B. Furie T. Springer M. Egli M. Stone R. Gaudet G. Verdine UCSD B. Lacy M. Waterman M. Grant G. Wagner T. Nakagawa M. Ohi S.C. Harrison L. Walensky H. Viadiu Thomas Jefferson J. Hogle S.Walker J. Williams D. Jeruzalmi T.Walz D. Kahne J. Wang Not Pictured: University of Toronto: L. Howell, E. Pai, F. Sicheri; NHRI (Taiwan): G. Liou; Trinity College, Dublin: Amir Khan T. Kirchhausen S. Wong Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

6. Boston Life Sciences Hub • Biomedical researchers • Government agencies • Life sciences Tufts • Universities Universit y School of Medicin e • Hospitals Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

7. Study of Protein Structure  and Function Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

8. Study of Protein Structure  and Function 1mm Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

9. Study of Protein Structure  and Function 400m 1mm Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

12. Study of Protein Structure  and Function 400m 1mm Grid Overview - Ian Stokes-Rees 10nm ijstokes@hkl.hms.harvard.edu

13. Study of Protein Structure  and Function 400m 1mm 10nm • Shared scientiFic data collection facility • Data intensive (10‐100 GB/day) Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

14. Cryo Electron Microscopy • Previously, 110,000 images, managed by hand • Now, robotic systems collect millions of images • estimate 250,000 CPUhours to reconstruct model Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

17. Molecular Dynamics Simulations 1 fs time step 1ns snapshot 1 us simulation 1e6 steps 1000 frames 10 MB / frame 10 GB / sim 20 CPUyears 3 months (wall clock) Big Data - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

18. Molecular Dynamics Simulations 1 fs time step 1ns snapshot 1 us simulation 1e6 steps 1000 frames 10 MB / frame 10 GB / sim 20 CPUyears 3 months (wall clock) Big Data - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

19. Required: Collaborative environment for  compute and data intensive science

20. High Energy Physics Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

21. High Energy Physics 40 MHz bunch crossing rate 10 million data channels 1 KHz level 1 event recording rate 110 MB per event 14 hours per day, 7+ months / year 4 detectors 6 PB of data / year globally distribute data for analysis (x2) Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

22. Open Science Grid http://opensciencegrid.org • US National  Cyberinfrastructure • Primarily used for high  energy physics computing • 80 sites • ~100,000 job slots 5,073,293 hours ~570 years • ~1,500,000 hours per day • PB scale aggregate storage • ~ 1 PB transferred each day Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

23. Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

24. Home About Us Informations TNGP News Calendar Document Download Jobs Forums Photogallery Publications Blog Related Links สัมมนาวิชาการเทคโนโลยีกริดและคลาวด์ Guestbook ศูนย์ไทยกริดแห่งชาติ สํานักงานส่งเสริมอุตสาหกรรมซอฟต์แวร์ Contact Us แห่งชาติ (องค์การมหาชน) ร่วมกับมหาวิทยาลัยเทคโนโลยีพะ Travel จอมเกล้าธนบุรี (มจธ) เป็นเจ้าภาพในการจัด... Healthy 6 May 2011 academic ประกาศผลการแข่งขัน โครงการ Grid Technology Innovat... ทําการแข่งขันเมื่อวันที่ 12-13 กุมภาพันธ์ 2554 25 March 2011 โปรแกรม R สําหรับงานวิเคราะห์และวิจัยด้านสถิติ R เป็นซอฟ์ทแวร์ สําหรับใช้ในงานด้านวิเคราะห์และวิจัยทางด้าน สถิติซึ่งนิยมใช้กันในผู้ที่ต้องทํางานด้านวิจัยที่เกี่ยวข้องกับการ สมัครรับข่าวสาร ยกเลิก คํานวณทางด้านสถิติ 28 February 2011 รัฐบาลมีนโยบายปฏิรูปการทํางาน และเพิ่มศักยภาพ ทุกภาคส่วน ของรัฐ ให้เอื้ออํานวยต่อการเสริมสร้าง ความเข้มแข็ง ของภาค เอกชน โดยการผลักดัน ยุทธศาสตร์ การเสริมสร้างศักยภาพการ แข่งขัน และการพัฒนา ที่ยั่งยืนของประเทศ และต้องการ พัฒนา ประเทศไปสู่สังคม แห่งภูมิปัญญา และ การเรียนรู้ (Knowledge Based Society) http://www.thaigrid.net/ Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

25. SimpliFied Grid Architecture Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

26. Grid Opportunities • New compute intensive workFlows • think big: tens or hundreds of thousands of hours Finished in 1‐2 days • sharing resources for efFicient and large scale utilization • Data intensive problems • we mirror 20 GB of data to 30 computing centers • Data movement, management, and archive • Federated identity and user management • labs, collaborations or ad‐hoc groups • role‐based access control (RBAC) and IdM • Collaborative environment • Web‐based access to applications Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

27. Web Portals for Collaborative,  Multi‐disciplinary Research...

28. Web Portals for Collaborative,  Multi‐disciplinary Research... ... which leverage capabilities of federated  grid computing environments

29. The Browser as the  Universal Interface • If it isn’t already obvious to you • Any interactive application developed today should be web‐based with a  RESTful interface (if at all possible) • A rich set of tools and techniques • AJAX, HTML4/5, CSS, and JavaScript • Dynamic content negotiation • HTTP headers, caching, security, sessions/cookies • Scalable, replicable, centralized, multi‐threaded,  multi‐user • Alternatives • Command Line (CLI): great for scriptable jobs • GUI toolkits: necessary for applications with high graphics or I/O demands Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

30. What is a web portal? • A web‐based gateway to resources and data • simpliFied access • centralized access • uniFied access (CGI, Perl, Python, PHP, static HTML, static Files, etc.) • Attempt to provide uniform access to a range of  services and resources • Data access via HTTP • Leverage brilliance of Apache HTTPD and  associated modules Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

31. SBGrid Science Portal Objectives A.  Extensible infrastructure to facilitate  development and deployment of novel  computational workFlows  B. Web‐accessible environment for collaborative,  compute and data intensive science Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

34. Protein Structure Determination Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

36. Results Visualization and Analysis Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

37. Data Access

38. User access to results data Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

40. Experimental Data Access • Collaboration • Access Control • Identity Management • Data Management • High Performance Data Movement • Multi‐modal Access Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

41. About 2PB with 100 front end  servers for high  bandwidth parallel  File transfer Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

42. Globus Online: High Performance  Reliable 3rd Party File Transfer GUMS DN to user mapping VOMS VO membership portal cluster data collection lab file facility server Grid Overview - Ian Stokes-Rees desktop laptop ijstokes@hkl.hms.harvard.edu

44. Identity Management  and Security

45. Access Control Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

46. Access Control • Need a strong Identity Management environment • individuals: identity tokens and identiFiers • groups: membership lists • Active Directory/CIFS (Windows), Open Directory (Apple), FreeIPA (Unix) all LDAP‐ based Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

47. Access Control • Need a strong Identity Management environment • individuals: identity tokens and identiFiers • groups: membership lists • Active Directory/CIFS (Windows), Open Directory (Apple), FreeIPA (Unix) all LDAP‐ based • Need to manage and communicate Access Control policies • institutionally driven • user driven Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

48. Access Control • Need a strong Identity Management environment • individuals: identity tokens and identiFiers • groups: membership lists • Active Directory/CIFS (Windows), Open Directory (Apple), FreeIPA (Unix) all LDAP‐ based • Need to manage and communicate Access Control policies • institutionally driven • user driven • Need Authorization System • Policy Enforcement Point (shell login, data access, web access, start application) • Policy Decision Point (store policies and understand relationship of identity token   and policy) Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

49. Access Control • What is a user? • .htaccess and .htpasswd • local system user (NIS or /etc/passwd) • portal framework user (proprietary DB schema) • grid user (X.509 DN) • What are we securing access to? • Web pages? • URLs? • Data? • SpeciFic operations? • Meta Data? • What kind of policies do we enable? • Simplify to READ WRITE EXECUTE LIST ADMIN Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

50. UniFied Account Management Hierarchical LDAP database user basics passwords Standard schemas Relational DB user custom proFiles institutions lab groups Custom schemas Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

52. Harvard Catalyst brings together the intellectual force, technologies, and clinical expertise of Harvard University and its affiliates and partners to reduce the burden of human illness. Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

53. Architecture Diagrams

54. Service Architecture GlobusOnline UC San Diego @Argonne GUMS User GUMS GridFTP + glideinWMS data Hadoop factory Open Science Grid computations MyProxy @NCSA, UIUC monitoring interfaces data computation ID mgmt Ganglia scp Condor FreeIPA Apache DOEGrids CA Nagios GridFTP Cycle Server @Lawrence GridSite LDAP RSV SRM VDT Berkley Labs Django VOMS Globus pacct WebDAV Sage Math GUMS glideinWMS Gratia Acct'ing R-Studio GACL @FermiLab file SQL shell CLI server DB cluster Monitoring SBGrid Science Portal @ Harvard Medical School @Indiana Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

56. Acknowledgements & Questions • Piotr Sliz • Principle Investigator, head of SBGrid • SBGrid Science Portal • Daniel O’Donovan, Meghan Porter‐Mahoney • SBGrid System Administrators • Ian Levesque, Peter Doherty, Steve Jahl • Globus Online Team • Steve Tueke, Ian Foster, Rachana  Ananthakrishnan, Raj Kettimuthu  • Ruth Pordes • Director of OSG, for championing SBGrid Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

57. Acknowledgements & Questions • Piotr Sliz Please contact me  • Principle Investigator, head of SBGrid with any questions: • SBGrid Science Portal • Ian Stokes‐Rees • Daniel O’Donovan, Meghan Porter‐Mahoney • ijstokes@hkl.hms.harvard.edu • SBGrid System Administrators • ijstokes@spmetric.com • Ian Levesque, Peter Doherty, Steve Jahl • Globus Online Team Look at our work • Steve Tueke, Ian Foster, Rachana  • portal.sbgrid.org Ananthakrishnan, Raj Kettimuthu  • www.sbgrid.org • Ruth Pordes • www.opensciencegrid.org • Director of OSG, for championing SBGrid Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

58. Extra Slides Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

59. Grid Architectural Details • Resources • Information • Uniform compute clusters • LDAP based most common (not  • Managed via batch queues optimized for writes) • Local scratch disk • Domain speciFic layer • Sometimes high perf. network  • Open problem! (e.g. InFiniBand) • Fabric • Behind NAT and Firewall • In most cases, assume functioning  • No shell access Internet • Data • Some sites part of experimental  private networks • Tape‐backed mass storage • Disk arrays (100s TB to PB) • Security • High bandwidth (multi‐stream)  • Typically underpinned by X.509  transfer protocols Public Key Infrastructure • File catalogs • Same standards as SSL/TLS and  • Meta‐data “server certs” for “https” • Replica management Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

60. TeraGrid SBGrid User NERSC Community Open Science Grid National Federated Cyberinfrastructure Odyssey Facilitate interface  between community  and cyberinfrastructure Orchestra EC2 Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

61. Existing Security  Infrastructure • X.509 certiFicates • Department of Energy CA • Regional/Institutional RAs (SBGrid is an RA) • X.509 proxy certiFicate system • Users self‐sign a short‐lived passwordless proxy certiFicate used for “portable”  and “automated” grid processing identity token • Similarities to Kerberos tokens • Virtual Organizations (VO) for deFinitions of roles,  groups, attrs • Attribute CertiFicates • Users can (attempt) to fetch ACs from the VO to be attached to proxy certs • POSIX‐like File access control (Grid ACL)  Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

63. Data Model • Data Tiers • VOwide: all sites, admin managed, very stable • User project: all sites, user managed, 1‐10 weeks, 1‐3 GB • User static: all sites, user managed, indeFinite, 10 MB • Job set: all sites, infrastructure managed, 1‐10 days, 0.1‐1 GB • Job: direct to worker node, infrastructure managed, 1 day, <10 MB • Job indirect: to worker node via UCSD, infrastructure managed, 1  day, <10 GB Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

64. Data Management quota du scan tmpwatch conventions workFlow integration Data Movement scp (users) rsync (VO‐wide) grid‐ftp (UCSD) curl (WNs) cp (NFS) htcp (secure web) Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

65. red  push Diles green  pull Diles Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

66. red  push Diles green  pull Diles 1. user Dile upload Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

67. red  push Diles green  pull Diles 2. replicate gold standard 1. user Dile upload Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

68. 3. Autoreplicate red  push Diles green  pull Diles 2. replicate gold standard 1. user Dile upload Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

69. 4. pull Diles from UCSD to WNs 3. Autoreplicate red  push Diles green  pull Diles 2. replicate gold standard 1. user Dile upload Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

70. 4. pull Diles from UCSD to WNs 5. pull Diles from 3. Autoreplicate local NSF to WNs red  push Diles green  pull Diles 2. replicate gold standard 1. user Dile upload Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

71. 4. pull Diles from UCSD to WNs 5. pull Diles from 3. Autoreplicate local NSF to WNs 6. pull Diles from SBGrid to WNs red  push Diles green  pull Diles 2. replicate gold standard 1. user Dile upload Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

72. 4. pull Diles from UCSD to WNs 5. pull Diles from 3. Autoreplicate local NSF to WNs 6. pull Diles from SBGrid to WNs red  push Diles green  pull Diles 2. replicate gold standard 7. job results copied  back to SBGrid 1. user Dile upload Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

73. 4. pull Diles from UCSD to WNs 5. pull Diles from 3. Autoreplicate local NSF to WNs 6. pull Diles from SBGrid to WNs red  push Diles green  pull Diles 2. replicate gold standard 7. job results copied  back to SBGrid 8a. large job results  copied to UCSD 8b. later pulled to  1. user Dile upload SBGrid Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

81. “weak” solution 2nx5q2 Log Likelihood Gain MHC‐TCR: 2VLJ “strong” solution 1im3a2 Translation Z score Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

82. • NEBioGrid Django Portal • PyGACL Interactive dynamic web portal for  Python representation of GACL model  workFlow deFinition, submission,  and API to work with GACL Files monitoring, and access control • osg_wrap • NEBioGrid Web Portal Swiss army knife OSG wrapper script to  GridSite based web portal for File‐system  handle File staging, parameter sweep,  level access (raw job output), meta‐data  DAG, results aggregation, monitoring tagging, X.509 access control/sharing,  • sbanalysis CGI data analysis and graphing tools for  • PyCCP4 structural biology data sets Python wrappers around CCP4  • osg.monitoring structural biology applications tools to enhance monitoring of job set  • PyCondor and remote OSG site status Python wrappers around common  • shex Condor operations Write bash scripts in Python: replicate  enhanced Condor log analysis commands, syntax, behavior • PyOSG • xconDig Python wrappers around common OSG  Universal conFiguration operations Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

83. 10k grid jobs Example Job Set approx 30k CPU hours 99.7% success rate evicted - red 24 wall clock hours completed - green held - orange MIT 5292 UWisc 1173 1077 120 1657 3 662 Cornell 840 20 Buffalo 720 628 ND 76 407 47 421 Caltech 190 FNAL 1409 237 12 24 79 4 47 UNL 6 1159 3 HMS 60 20 Purdue 349 10,000 jobs 52 17 39 UCR RENCI local queue remote queue SPRACE 1216 running 316 248 Grid Overview - Ian Stokes-Rees 24 hours ijstokes@hkl.hms.harvard.edu

84. Job Lifelines Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

85. Typical Layered Environment Fortran bin • Command line application (e.g. Fortran) • Friendly application API wrapper Python API Map- • Batch execution wrapper for N‐iterations Multi-exec wrapper Reduce • Results extraction and aggregation Result aggregator • Grid job management wrapper Grid management • Web interface Web interface • forms, views, static HTML results • GOAL eliminate shell scripts • often found as “glue” language between layers Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

86. REST • Don’t try to read too much into the name • REpresentational State Transfer: coined by Roy Fielding, co‐author of  HTTP protocol and contributor to original Apache httpd server • Idea • The web is the worlds largest asynchronous, distributed, parallel  computational system • Resources are “hidden” but representations are accessible via URLs • Representations can be manipulated via HTTP operations GET PUT POST  HEAD DELETE and associated state • State transitions are initiated by software or by humans • Implication • Clean URLs (e.g. Flickr) Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

87. Big Data - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

88. Cloud Computing: Industry solution to the Grid • Virtualization has taken off in the past 5 years • VMWare, Xen, VirtualPC, VirtualBox, QEMU, etc. • Builds on ideas from VMS (i.e. old) • (Good) System administrators are hard to come by • And operating a large data center is costly • Internet boom means there are companies that have Figured out  how to do this really well • Google, Amazon, Yahoo, Microsoft, etc. • Outsource IT infrastructure!  Outsource software hosting! • Amazon EC2, Microsoft Azure, RightScale, Force.com, Google Apps • Over simpliFied: • You can’t install a cloud • You can’t buy a grid Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

89. Is “Cloud” the new “Grid”? • Grid is about mechanisms for federated,  distributed, heterogeneous shared compute and  storage resources • standards and software • Cloud is about on‐demand provisioning of  compute and storage resources • services No one buys a grid.  No one installs a cloud. Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

90. The interesting thing about Cloud Computing is that  we’ve redeTined 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, Oracle CEO, quoted in the Wall Street Journal, September 26, 2008*  *http://blogs.wsj.com/biztech/2008/09/25/larry‐ellisons‐brilliant‐anti‐cloud‐computing‐rant/ Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

91. When is cloud computing  interesting? • My deFinition of “cloud computing” • Dynamic compute and storage infrastructure provisioning in a scalable manner providing  uniform interfaces to virtualized resources • The underlying resources could be •  “in‐house” using licensed/purchased software/hardware • “external” hosted by a service/infrastructure provider • Consider using cloud computing if • You have operational problems/constraints in your current data center • You need to dynamically scale (up or down) access to services and data • You want fast provisioning, lots of bandwidth, and low latency • Organizationally you can live with outsourcing responsibility for (some of) your data and  applications • Consider providing cloud computing services if • You have an ace team efFiciently running your existing data center • You have lots of experience with virtualization • You have a speciFic application/domain that could beneFit from being tied to a large compute  farm or disk array with great Internet connectivity Grid Overview - Ian Stokes-Rees ijstokes@hkl.hms.harvard.edu

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