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© 2007 Open Grid Forum Cloud Computing BOF OGF22 Birds of a Feather Session Hyatt Regency Cambridge February 27 2008 Geoffrey Fox Indiana University email@example.com
© 2007 Open Grid Forum 2 Cloud Agenda Geoffrey Fox (Indiana U.) Remarks on Cloud Computing Martin Swany (Internet2) Clouds and Dynamic Networking Steven Newhouse (Microsoft) Personal View on Clouds Kate Keahey (Argonne, Chicago) First Steps in the Clouds Next Steps
© 2007 Open Grid Forum 3 What are Clouds? Clouds are Virtual Clusters (Virtual Grids) of possibly Virtual Machines They may cross administrative domains or may just be a single cluster; the user cannot and does not want to know Clouds support access (lease of) computer instances Instances accept data and job descriptions (code) and return results that are data and status flags Each Cloud is a Narrow (perhaps internally proprietary) Grid When does Cloud concept work Parameter searches, LHC style data analysis.. Common case (most likely success case for clouds) versus corner case? Clouds can be built from Grids Grids can be built from Clouds
© 2007 Open Grid Forum 4 Cloud References http://en.wikipedia.org/wiki/Cloud_computing Includes references to Amazon, Apple, Dell, Enomalism, Globus, Google, IBM, KnowledgeTreeLive, Nature, New York Times, Zimdesk Others like Microsoft Windows Live Skydrive important http://en.wikipedia.org/wiki/Amazon_Elastic_Compute_Cloud http://uc.princeton.edu/main/index.php?option=com_content&ta sk=view&id=2589&Itemid=1 Policy Issues http://uc.princeton.edu/main/index.php?option=com_content&ta sk=view&id=2589&Itemid=1 http://www.cra.org/ccc/home.article.bigdata.html Hadoop (MapReduce) and Data Intensive Computing See Data intensive computing minitrack at HICSS-42 January 2009 http://ianfoster.typepad.com/blog/2008/01/theres-grid-in.html OGF Thought Leadership blog OGF22 talks by Charlie Catlett and Irving Wladawsky-Berger
© 2007 Open Grid Forum 5 Big-Data Computing Study Group CCC Role Versus OGF? Hadoop and MapReduce are just workflow?
© 2007 Open Grid Forum 6 Google MapReduce Simplified Data Processing on Clusters/Clouds http://labs.google.com/papers/mapreduce.html This is a dataflow model between services where services can do useful document oriented data parallel applications including reductions The decomposition of services onto cluster engines (clouds) is automated The large I/O requirements of datasets changes efficiency analysis in favor of dataflow Services (count words in example) can obviously be extended to general parallel applications There are many alternatives to language expressing either dataflow and/or parallel operations and/or workflow
© 2007 Open Grid Forum 7 Technical Questions about Clouds I What is performance overhead? On individual CPU On system including data and program transfer What is cost gain From size efficiency; green location (rumor that Google has purchased the Niagara Falls including Canada!) Is Cloud Security adequate: can clouds be trusted? Can one can do parallel computing on clouds? Looking at capacity not capability i.e. lots of modest sized jobs Marine corps will use Petaflop machines – they just need ssh and a.out
© 2007 Open Grid Forum 8 Technical Questions about Clouds II How is data compute affinity tackled in clouds? Co-locate data and compute clouds? Lots of optical fiber i.e. just move the data? What happens in clouds when demand for resources exceeds capacity – is there a multi-day job input queue? Are there novel cloud scheduling issues? Do we want to link clouds (or ensembles as atomic clouds); if so how and with what protocols Is there an intranet cloud e.g. cloud in a box software to manage personal (cores on my future 128 core laptop) department or enterprise cloud?
© 2007 Open Grid Forum 9 Standards for Compute and Storage Clouds We no longer need interoperability of services and messages (SOAP) but rather interoperability of clouds Maybe each cloud so big that interoperability between clouds not so critical Interoperability certainly for application specific data and perhaps also for job specifications WFS, GML for Geo-data; IVOA standards; DST LHC experiment formats JSDL, BES etc. Each Cloud will be proprietary but they might want raw infrastructure standards so they can easily swap in and out different vendors disk drives Clouds very very loosely coupled; services loosely coupled
© 2007 Open Grid Forum 10 MSI Challenge Problem There are > 330 MSIs – Minority Serving Institutions 2 examples ECSU is a small state university in North Carolina HBCU with 4000 students Working on PolarGrid (Sensors in Arctic/Antarctic linked to TeraGrid) Navajo Tech in Crown Point NM is community college with technology leadership for Navajo Nation Internet to the Hogan and Dine Grid links Navajo communities by wireless Wish to integrate TeraGrid science into Navajo Nation education curriculum Current Grid technology too complicated if you are not an R1 institution Hard to deploy campus grids broadly into MSIs Clouds provide virtual campus resources?
© 2007 Open Grid Forum 11 Next Steps at OGF Clouds are just starting and build on/are related to Grids Clear need for best practice in use and technology Likely to be need for new standards and novel use of existing/projected standards New Cloud Community Group? Chairs, participants? Workshop? OGF23 activity? Identify key players not currently involved with OGF?
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© 2006 Open Grid Forum OGF-22 Opening Keynote Craig A. Lee, President, OGF February 25, 2008 OGF-22, February 25-29, 2008 Hyatt Regency Cambridge.
© 2007 Open Grid Forum Data Management Challenge - The View from OGF OGF22 – February 28, 2008 Cambridge, MA, USA Erwin Laure David E. Martin Data Area.
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Hello i am so and so, title/role and a little background on myself (i.e. former microsoft employee or anything interesting) set context for what going.
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Ken Birman. Massive data centers We’ve discussed the emergence of massive data centers associated with web applications and cloud computing Generally.
Amazon Web Services announced the launch of Cluster Compute Instances for Amazon EC2. Which aims to provide high-bandwidth, low- latency instances.
Authors: Thilina Gunarathne, Tak-Lon Wu, Judy Qiu, Geoffrey Fox Publish: HPDC'10, June 20–25, 2010, Chicago, Illinois, USA ACM Speaker: Jia Bao Lin.
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