Cloud Computing ...changes everything


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Presented at SDForum. October 2009

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  • Cloud Computing ...changes everything

    1. 1. <ul><li>Lew Tucker </li></ul><ul><li>Vice President and CTO </li></ul><ul><li>Cloud Computing </li></ul><ul><ul><ul><li>Sun Microsystems, Inc. </li></ul></ul></ul>Cloud Computing ... changes everything
    2. 2. Cloud Computing.... Virtualization Grid computing Application hosting Utility computing Platform as a service Infrastructure as a service Software as a service
    3. 3. Some definitions <ul><li>Utility computing – general term, ‘pay only for what you use”, HW &/or SW </li></ul><ul><li>SaaS – Software as a service – an application offered on-demand via multi-tenancy (, GoogleApps) </li></ul><ul><li>PaaS – Platform as a service – for developers to build apps in the cloud (Google App Engine,, Facebook) </li></ul><ul><li>IaaS – Infrastructure as a service – basic compute/storage and network resources (Amazon AWS, Mosso) </li></ul>
    4. 4. Analogy with the evolution of electrical power From each company generating their own power to a utility
    5. 5. Power generation technology
    6. 6. Distribution is key
    7. 7. When and will this happen? 2000 2005 2010? 2015? 2020? 2025? Utility / Cloud Computing Private DataCenters
    8. 8. Clouds reach the tipping point – AWS Bandwidth exceeds
    9. 9. Major driver - Web API's
    10. 10. Growth of massive amounts of data <ul><li>The Information Factories  </li></ul><ul><li>petascale data centers </li></ul><ul><li>George Gilder </li></ul><ul><li>Wired 14.10 2006 </li></ul><ul><li>The desktop is dead. Welcome to the Internet cloud, where massive facilities across the globe will store all the data you'll ever use. </li></ul><ul><li>The End of Science </li></ul><ul><li>petascale data </li></ul><ul><li>Chris Anderson </li></ul><ul><li>Wired 16.07 2008  </li></ul><ul><li>The quest for knowledge used to begin with grand theories. Now it begins with massive amounts of data. </li></ul>
    11. 11. History lesson: 1987 Connection Machine 65,536 processors and lots of blinking lights
    12. 12. Truly Massive Scale TACC Sun Magnum 3456 Switch TACC Supercomputer center
    13. 13. Putting it all together – TACC Specifications <ul><li>Compute: 529 TFLOPs </li></ul><ul><li>62,976 cores – 94 Racks </li></ul><ul><li>Sun Constellation C48 Cluster </li></ul><ul><ul><li>48x 4-Socket Blades per rack </li></ul></ul><ul><li>1.7 PB of Storage (x4500 “Thumper)‏ </li></ul><ul><ul><li>Lustre Parallel FS </li></ul></ul><ul><ul><li>~57 GB/s (72 GB/s Theoretical Peak)‏ </li></ul></ul><ul><li>Sun Magnum InfiniBand Switch </li></ul><ul><ul><li>High bandwidth and low latency </li></ul></ul>
    14. 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. 15. Data + compute -> new opportunities 'Semi-structured' data emerges Mogile, Bigtable, Hypertable ... New 'Analytics' emerge MapReduce, Hadoop ... New 'Compute' New 'Data'
    16. 16. More data each and every day
    17. 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. 18. Economics Driving Utility Computing <ul><li>Shared data centers allow efficiencies of large scale </li></ul><ul><li>Pay-as-you go, pay only for what you need </li></ul><ul><li>Automation and programatic API control </li></ul><ul><li>Scale up, scale down </li></ul>“ 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. 19. What changes for developers? <ul><li>Access to large scale computing at reasonable cost </li></ul><ul><li>Component failure is an everyday reality </li></ul><ul><li>Applications span multiple services </li></ul><ul><li>Building “systems” not just apps </li></ul><ul><li>New design patterns for massive scale </li></ul><ul><li>New abstractions to simplify things </li></ul>
    20. 20. Hardware Virtualization <ul><li>Concept from mainframe era - increases server utilization </li></ul><ul><li>Most important: programmatically assemble virtual compute, storage and network components </li></ul>Compute Node Compute Node Compute Node Storage Node Storage Node Network
    21. 21. Services oriented architecture <ul><li>Independently-scaled, loosely-coupled systems </li></ul><ul><li>Higher-level components for systems architecture </li></ul> Profile: Member since 2004 Computer Scientist Authentication svc Forum svc Cache svc Ad Server Thumbnails Flickr Maps Aggregation
    22. 22. Patterns emerge in architecture - learn from others
    23. 23. Master-worker, queue-based design pattern - simple, dynamic scaling <ul><li>Dynamically grow number of workers according to number of requests </li></ul>App Server Master Worker N Worker 3 Worker 1 Worker 2 Message Queue .... Internet
    24. 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. 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. 26. We are all part of the co-generation of information and information processing <ul><li>The Internet becomes both our compute utility power plant and our distribution fabric </li></ul><ul><li>There will lots of clouds (public, private, HPC, video) </li></ul>
    27. 27. Questions to ask yourself <ul><li>Are you prepared? </li></ul><ul><ul><li>How well do you scale? </li></ul></ul><ul><ul><li>Where are you taking your system architecture? </li></ul></ul><ul><ul><li>What is core – what can you leverage? </li></ul></ul><ul><li>If you had unlimited computer resources... </li></ul><ul><ul><li>What is your dream? </li></ul></ul>
    28. 28. The Network is YOUR Computer
    29. 29. Thank you Lew Tucker Sun Microsystems, Inc