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Cloud Computing ...changes everything

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Presented at SDForum. October 2009

Presented at SDForum. October 2009

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  • intro

Cloud Computing ...changes everything Cloud Computing ...changes everything Presentation Transcript

    • Lew Tucker
    • Vice President and CTO
    • Cloud Computing
        • Sun Microsystems, Inc.
    Cloud Computing ... changes everything
  • Cloud Computing.... Virtualization Grid computing Application hosting Utility computing Platform as a service Infrastructure as a service Software as a service
  • 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)
  • Analogy with the evolution of electrical power From each company generating their own power to a utility
  • Power generation technology
  • Distribution is key
  • When and will this happen? 2000 2005 2010? 2015? 2020? 2025? Utility / Cloud Computing Private DataCenters
  • Clouds reach the tipping point – AWS Bandwidth exceeds Amazon.com
  • Major driver - Web API's
  • 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.
  • History lesson: 1987 Connection Machine 65,536 processors and lots of blinking lights
  • Truly Massive Scale TACC Sun Magnum 3456 Switch TACC Supercomputer center
  • 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
  • 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
  • Data + compute -> new opportunities 'Semi-structured' data emerges Mogile, Bigtable, Hypertable ... New 'Analytics' emerge MapReduce, Hadoop ... New 'Compute' New 'Data'
  • More data each and every day
  • 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
  • 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
  • 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
  • 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
  • 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
  • Patterns emerge in architecture - learn from others
  • 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
  • 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
  • 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
  • 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)
  • 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?
  • The Network is YOUR Computer
  • Thank you Lew Tucker Sun Microsystems, Inc