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Cloud Computing ...changes everything

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Presented at SDForum. October 2009

Presented at SDForum. October 2009

Published in: Technology

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    • 1.
      • Lew Tucker
      • Vice President and CTO
      • Cloud Computing
          • Sun Microsystems, Inc.
      Cloud Computing ... changes everything
    • 2. Cloud Computing.... Virtualization Grid computing Application hosting Utility computing Platform as a service Infrastructure as a service Software as a service
    • 3. Some definitions
      • Utility computing – general term, ‘pay only for what you use”, HW &/or SW
      • SaaS – Software as a service – an application offered on-demand via multi-tenancy (Salesforce.com, GoogleApps)
      • PaaS – Platform as a service – for developers to build apps in the cloud (Google App Engine, Force.com, Facebook)
      • IaaS – Infrastructure as a service – basic compute/storage and network resources (Amazon AWS, Mosso)
    • 4. Analogy with the evolution of electrical power From each company generating their own power to a utility
    • 5. Power generation technology
    • 6. Distribution is key
    • 7. When and will this happen? 2000 2005 2010? 2015? 2020? 2025? Utility / Cloud Computing Private DataCenters
    • 8. Clouds reach the tipping point – AWS Bandwidth exceeds Amazon.com
    • 9. Major driver - Web API's
    • 10. Growth of massive amounts of data
      • The Information Factories 
      • petascale data centers
      • George Gilder
      • Wired 14.10 2006
      • The desktop is dead. Welcome to the Internet cloud, where massive facilities across the globe will store all the data you'll ever use.
      • The End of Science
      • petascale data
      • Chris Anderson
      • Wired 16.07 2008 
      • The quest for knowledge used to begin with grand theories. Now it begins with massive amounts of data.
    • 11. History lesson: 1987 Connection Machine 65,536 processors and lots of blinking lights
    • 12. Truly Massive Scale TACC Sun Magnum 3456 Switch TACC Supercomputer center
    • 13. Putting it all together – TACC Specifications
      • Compute: 529 TFLOPs
      • 62,976 cores – 94 Racks
      • Sun Constellation C48 Cluster
        • 48x 4-Socket Blades per rack
      • 1.7 PB of Storage (x4500 “Thumper)‏
        • Lustre Parallel FS
        • ~57 GB/s (72 GB/s Theoretical Peak)‏
      • Sun Magnum InfiniBand Switch
        • High bandwidth and low latency
    • 14. Data Center Level Computing Google’s new data center on the Columbia river, Oregon Thousands and thousands of commodity parts built into a system to essentially serve a single application Power and Cooling major drivers of cost
    • 15. Data + compute -> new opportunities 'Semi-structured' data emerges Mogile, Bigtable, Hypertable ... New 'Analytics' emerge MapReduce, Hadoop ... New 'Compute' New 'Data'
    • 16. More data each and every day
    • 17. How do the new guys compete? Web server app server database server Storage system Web server Web server app server database server Storage system Load balancer distributed memory cache
    • 18. Economics Driving Utility Computing
      • Shared data centers allow efficiencies of large scale
      • Pay-as-you go, pay only for what you need
      • Automation and programatic API control
      • Scale up, scale down
      “ Let me be very clear here: I really don’t want to operate datacenters anymore... We’d rather spend our time giving our customers great service and writing great software rather than managing physical hardware,” Don MacAskill, CEO, Smugmug
    • 19. What changes for developers?
      • Access to large scale computing at reasonable cost
      • Component failure is an everyday reality
      • Applications span multiple services
      • Building “systems” not just apps
      • New design patterns for massive scale
      • New abstractions to simplify things
    • 20. Hardware Virtualization
      • Concept from mainframe era - increases server utilization
      • Most important: programmatically assemble virtual compute, storage and network components
      Compute Node Compute Node Compute Node Storage Node Storage Node Network
    • 21. Services oriented architecture
      • Independently-scaled, loosely-coupled systems
      • Higher-level components for systems architecture
      HotSite.com Profile: Member since 2004 Computer Scientist Authentication svc Forum svc Cache svc Ad Server Thumbnails Flickr Maps Aggregation
    • 22. Patterns emerge in architecture - learn from others
    • 23. Master-worker, queue-based design pattern - simple, dynamic scaling
      • Dynamically grow number of workers according to number of requests
      App Server Master Worker N Worker 3 Worker 1 Worker 2 Message Queue .... Internet
    • 24. Automation – scaling from 50 to 3500 servers in 3 days 4/11 4/12 4/13 4/13 4/14 4/15 4/15 4/16 4/17 4/17 4/18 4000 3000 2000 1500 1000 500 50 Animoto case study Servers
    • 25. Map Reduce design pattern Documents Map <word, cnt> Map <word, cnt> Map <word, cnt> hash(word, mod R)‏ Reduce <word, cnt> Reduce <word, cnt> Reduce <word, cnt> Result <apple, 2,045,455> <barn, 254,345> Example: compute word frequency over a large set of documents Map <word, cnt> Result <house, 111,341> <kitchen, 4,678> Result <cat, 1,033,746> <horse, 25,387> Reduce workers: reads and sorts Map output, invoking Reduce() task which sums counts for each word Map workers: reads input, invoking Map() function which finds words and outputs records for reduce workers P 1 P 1 P 1 P 1
    • 26. We are all part of the co-generation of information and information processing
      • The Internet becomes both our compute utility power plant and our distribution fabric
      • There will lots of clouds (public, private, HPC, video)
    • 27. Questions to ask yourself
      • Are you prepared?
        • How well do you scale?
        • Where are you taking your system architecture?
        • What is core – what can you leverage?
      • If you had unlimited computer resources...
        • What is your dream?
    • 28. The Network is YOUR Computer
    • 29. Thank you Lew Tucker Sun Microsystems, Inc

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