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Smarter Planet Dynamic Infrastructure Cloud Computing (Brief Client Briefing Research & Univ Oct 2009 En Uk) (Reduced)

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    10/15/09 11:40 Shearer SLM.ppt

    The proliferation of sensors, digital communications and other forms of digital data collection, along with advances in the storage and management of such data has led to a projected tenfold growth in digital data between 2007 and 2011. All of this data has the potential to provide enterprise with valuable insights for running their businesses more effectively and efficiently. Now, businesses analysts need to adapt from an environment in which the challenge was in gaining insights from limited data to one in which the challenge is in managing and extracting useful information from massive data sets. As one can imagine, finding the relevant data, and quickly, amid the 'mountain' of available data can be like finding a needle in a haystack. Moreover, of the growth in digital data, approximately 80% of it is expected to comprise semi-structured and unstructured data (i.e., email, blogs, medical images, videos, audio files, pictures). With unstructured data, considerable effort is required to 'understand' the data, even before any further analysis can be performed to intelligently influence decision making. Semantics The number of semantically tagged documents and data sets is growing, as a result of five developments: “ Linked Data” guidelines, published in 2006, make it easier to share data on the web. The graph in the upper left represents the output of the Linking Open Data community project, which has the goal of making large numbers of open data sets more available by complying with the Linked Data guidelines. RDF (Resource Description Framework) triples are a key component of the Linked Data guidelines. Technologies to convert many legacy sources, especially of relational data, into RDF triples, became available as open source in 2006 (and IBM Research has improved versions of these) Technologies are appearing that can automatically find associations between subjects and objects from one “data graph” with subjects and objects in other “data graphs” Several important reference information suppliers, most notably Thomson Reuters, entered into this space (through their OpenCalais effort). Several efforts have developed technology to mine the essential information about people, places, materials, governments, businesses, works of literature and so on from Wikipedia, into a semantically tagged form (notably DBPedia and Freebase), so that as Wikipedia extends to cover more of the worlds knowledge, more of that becomes part of the Web of semantic data. Net: Both the number of facts, and the rich interconnecting of different classes of facts, have been growing at an accelerating rate. Example Use: In BlueJ! grand challenge, linked data (DBpedia, IMDb, Freebase etc) is used as important structured information source to improve the accuracy of question answering in additional to unstructured information. Acronyms: RDF – Resource Description Framework (W3C Standard) FOAF – Friend of a Friend – the RDF application for describing people and other people they know DBLP – Digital Bibliography and Library Project – bibliographic information on more than 1 million computer science research publications SIOC – Semantically Interlinked Online Communities DOAP – Description of a Project; DOAPSpace – a repository of open source projects RIESE - R DFizing and I nterlinking the E uro S tat Data Set E ffort OpenGuides – Information about leading cities of the world, the kind of information that would appear in a guidebook, produced by the community through a public Wiki. Jamendo – an online music community, including a large quantity of music licensed under various Creative Commons license terms. www.garlik.com – an online identity monitoring service Sindice.com – web service providing a directory/index of all Linked Open Data and Microformat data on the Web

    The proliferation of sensors, digital communications and other forms of digital data collection, along with advances in the storage and management of such data has led to a projected tenfold growth in digital data between 2007 and 2011. All of this data has the potential to provide enterprise with valuable insights for running their businesses more effectively and efficiently. Now, businesses analysts need to adapt from an environment in which the challenge was in gaining insights from limited data to one in which the challenge is in managing and extracting useful information from massive data sets. As one can imagine, finding the relevant data, and quickly, amid the 'mountain' of available data can be like finding a needle in a haystack. Moreover, of the growth in digital data, approximately 80% of it is expected to comprise semi-structured and unstructured data (i.e., email, blogs, medical images, videos, audio files, pictures). With unstructured data, considerable effort is required to 'understand' the data, even before any further analysis can be performed to intelligently influence decision making. Semantics The number of semantically tagged documents and data sets is growing, as a result of five developments: “ Linked Data” guidelines, published in 2006, make it easier to share data on the web. The graph in the upper left represents the output of the Linking Open Data community project, which has the goal of making large numbers of open data sets more available by complying with the Linked Data guidelines. RDF (Resource Description Framework) triples are a key component of the Linked Data guidelines. Technologies to convert many legacy sources, especially of relational data, into RDF triples, became available as open source in 2006 (and IBM Research has improved versions of these) Technologies are appearing that can automatically find associations between subjects and objects from one “data graph” with subjects and objects in other “data graphs” Several important reference information suppliers, most notably Thomson Reuters, entered into this space (through their OpenCalais effort). Several efforts have developed technology to mine the essential information about people, places, materials, governments, businesses, works of literature and so on from Wikipedia, into a semantically tagged form (notably DBPedia and Freebase), so that as Wikipedia extends to cover more of the worlds knowledge, more of that becomes part of the Web of semantic data. Net: Both the number of facts, and the rich interconnecting of different classes of facts, have been growing at an accelerating rate. Example Use: In BlueJ! grand challenge, linked data (DBpedia, IMDb, Freebase etc) is used as important structured information source to improve the accuracy of question answering in additional to unstructured information. Acronyms: RDF – Resource Description Framework (W3C Standard) FOAF – Friend of a Friend – the RDF application for describing people and other people they know DBLP – Digital Bibliography and Library Project – bibliographic information on more than 1 million computer science research publications SIOC – Semantically Interlinked Online Communities DOAP – Description of a Project; DOAPSpace – a repository of open source projects RIESE - R DFizing and I nterlinking the E uro S tat Data Set E ffort OpenGuides – Information about leading cities of the world, the kind of information that would appear in a guidebook, produced by the community through a public Wiki. Jamendo – an online music community, including a large quantity of music licensed under various Creative Commons license terms. www.garlik.com – an online identity monitoring service Sindice.com – web service providing a directory/index of all Linked Open Data and Microformat data on the Web

    10/15/09 11:40 Shearer SLM.ppt

    10/15/09 11:40 Shearer SLM.ppt

    Cloud Computing delivers the significant economic benefits of reduced capex and opex and services level discipline. The Cloud idea says we can apply a new model to business process and IT delivery, management and security to transform the economics not only to e-mail, Google maps and search and other light weight applications, but to the trillion dollar infrastructure investment that underpin the way the world functions.

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    Smarter Planet Dynamic Infrastructure Cloud Computing (Brief Client Briefing Research & Univ Oct 2009 En Uk) (Reduced) - Presentation Transcript

    1. Moisés Navarro Marín (m.navarro@es.ibm.com) Member, IBM Technical Experts Council Services provisioning evolution Smarter Planet – Dynamic Infrastructure – cloud computing http://www.ibm.com/cloud
    2. http://www.ibm.com/smartplanet GREEN AND BEYOND SMART WORK NEW INTELLIGENCE DYNAMIC INFRASTRUCTURE
    3. As the world gets smarter, demands on IT will grow Smart traffic systems Smart water management Smart energy grids Smart healthcare Smart food systems Intelligent oil field technologies Smart regions Smart weather Smart countries Smart supply chains Smart cities Smart retail
    4. By 2011, the world will be 10 times more instrumented then it was in 2006. Internet connected devices will leap from 500M to 1 Trillion 2005 2006 2007 2008 2009 2010 2011 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 Exabytes RFID, Digital TV, MP3 players, Digital cameras, Camera phones, VoIP, Medical imaging, Laptops, smart meters, multi-player games, Satellite images, GPS, ATMs, Scanners, Sensors, Digital radio, DLP theaters, Telematics , Peer - to - peer, Email, Instant messaging, Videoconferencing, CAD/CAM, Toys, Industrial machines, Security systems, Appliances 10x growth in five years Approximately 70% of the digital universe is created by individuals, but enterprises are responsible for 85% of the security, privacy, reliability, and compliance.
    5. It’s time to start thinking differently about infrastructure. Infrastructure needs to become more dynamic .
    6. IBM RESEARCH GLOBLAL TECHNOLOGY OUTLOOK 2009 5. Security 6. Transformational Hybrid Systems Fine-grained, Risk Adjusted Security Transformative Enterprise Computing Foundations 3. Services Quality 4. Cloud Leadership in Service Excellence Opportunities beyond Infrastructure Business Decisions Services 2. Data to Smart Decisions 1. Digital Economy New Value Vehicles Consumability of Analytics
    7. what is cloud computing?
      • Nowadays, when providing services, users demand:
        • An improved end user experience (higher quality, faster response, transparency, robustness,…)
      • Nowadays, when providing services, service providers demand:
        • Dynamic capabilities for services provisioning (addressing aggressive changes in demand)
        • Efficiency (operational, economical, ecological).
      • So this is what the new services provisioning model is addressing:
        • Improve the end user experience (higher quality, faster response, transparency)
        • Strengthen the services delivery automatically addressing aggressive changes in services demand
        • Be focused on efficiency.
      cloud computing
    8. which is the evolution until cloud computing? 1990 2008 Software as a Service Utility Computing Grid Computing Cloud Computing
    9. cloud computing: features & models Measurement & Billing Fast provisioning Flexible pricing Elastic scaling Advanced virtualization Standardized offerings Demand based on Services Catalogue Energy efficiency Automation
      • Public Cloud “external sourcing”
        • Managed and owned by a provider
        • Access based on subscription
      Standardization Capital preservation Flexibility Time to deploy
      • Private Cloud “internal sourcing”
        • Managed and owned by the company
        • Access defined by the company
      Customization Efficiency Security & Privacy Availability
    10. how does cloud computing work? Elastic scalability Demand based on a standardized services catalogue users Services catalogue Portal IT Infrastructure
    11. what does it take to own a cloud? provisioning orchestration internal & external standardization virtualization management business resiliency security
    12. what is not cloud computing?
      • I have virtualized my IT infrastructure, am I a cloud user?
      • I can forsee demand
      • I have outsourced my IT, am I a cloud user?
      • I have these app’s in the net… are they cloud based?
      • I want to use the idle cycles in my corporate desktop fleet
      • …
      Measurement & Billing Fast provisioning Flexible pricing Elastic scaling Advanced virtualization Standardized offerings Demand based on Services Catalogue Energy efficiency Automation
    13. Up to 80% in cost savings Up to 60% in energy savings why would anyone move workloads to a cloud computing environment? Private Cloud Public Cloud 85% idle In distributed computing environments, up to 85% of computing capacity sits idle. Explosion of information driving 54% growth in storage shipments every year. 1.5x 70cents./1€ 70% on average is spent on maintaining current IT infrastructures versus adding new capabilities. CAPEX oriented Virtualization OPEX oriented Standardization & Automation 35% Energy consumption due to servers will increase by 35% in the next 4 years.
    14. storage computing on demand Web2.0 portals fraud, risk, analytics cloud, what for? e-training desktops Desktops & Devices Application development & testing IT Infrastructure Business Services Collaboration Analytics
    15. IBM ACADEMIC INITIATIVE http://www.ibm.com/jct01005c/university/scholars/academicinitiative/
      • IBM – Google Academic Alliance (since October 2007)
        • Promotes open standards & Hadoop massive parallel computing model
        • Jointly provide compute platform of the future using IBM Blue Cloud
        • Supported by the National Science Foundation: $4.95M in grants issued to 14 schools
      • Benefits
        • Trains students with next generation computing skills
        • Promotes advanced research & learning activities
        • Allows users to tap into massive computing resources not previously available
        • Allows workload optimization (search engines, video, audio, 3D Internet, …)
      IBM ACADEMIC ALLIANCE
      • NSF (National Science Foundation) created the CluE (Cluster Exploratory) programme for the academic research community
      • It’s one of the biggest cloud computing environments (+1100 servers – 800TBytes)
      • ~40 participant universities by the end of 2009
      • +1000 users (students – researchers) by the end of 2009
      • 9 new awards by NSF in 2009
      • Projects:
        • Inverted Index
        • PageRank on Wikipedia
        • Clustering NetFlix Movie Data
        • Language Modelling in the Clouds
        • Large-data Statistical Machine Translation
        • Collective Resolution of Identity in Email Archives
        • Parallel Automatic Text-Background Separation in Picture Books
        • Large-Scale Network Analysis to Improve Retrieval in the Biomedical Domain
      IBM ACADEMIC ALLIANCE
      • Hachiouji, Tokyo
      • We are working with Computer Science Major http://www.teu.ac.jp/english/departments/010616.html
      http://www.teu.ac.jp/english/ Learning Cloud for Tokyo University of Technology
    19. Learning Cloud for Tokyo University of Technology
      • Programming classes BEFORE CLOUD:
        • Long way to begin to study
          • download eclipse
          • setting up environment
          • create project
          • import sample code, etc.
        • Exercise code is on PC
          • Teacher can’t review until students’ submit.
          • No collaboration on exercise.
      • WITH CLOUD IN 2010:
        • Education environment for programming
          • WebSphere sMash Developer Edition - AppBuilder
        • Automated provisioning & load balancing
      • Education as a Service (EaaS)
        • Education is done only on the cloud
        • Student can begin study in 10 minutes.
        • Programming environment for exercise on the cloud.
        • Test environment for exercise on the cloud.
    20. More case studies IBM Universities cloud computing
      • Carnegie Mellon University at Qatar, Qatar University, Texas A&M University in Qatar
        • Cloud Programming Model Education
        • Arabic Language Search Engine
        • Migration of scientific application to cloud paradigm
        • Seismic modeling and Exploration for Oil & Gas
        • Oil & Gas Integrated Production Operation
      • University of Pretoria
        • Shared by students of the Computational Intelligence Research Group (CIRG)
        • Leverages Apache Hadoop and MapReduce
        • Will run advanced Computational Intelligence algorithms
      • Kyushu University
        • Research platform for distributed massive data processing
        • Dynamic provisioning of Hadoop parallel computing environments
    21. So...
    22. Now...

    + mnavarro_ibmmnavarro_ibm, 1 month ago

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