Scale as a Competitive Advantage
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Scale as a Competitive Advantage

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Deck presented at the 2010 SOA & Cloud Symposium

Deck presented at the 2010 SOA & Cloud Symposium

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  • Microsoft's Windows Azure platform is a virtualized and abstracted application platform that can be used to build highly scalable and reliable applications, with Java. The environment consists of a set of services such as NoSQL table storage, blob storage, queues, relational database service, internet service bus, access control, and more. Java applications can be built using these services via Web services APIs, and your own Java Virtual Machine, without worrying about the underlying server OS and infrastructure. Highlights of this session will include: • An overview of the Windows Azure environment • How to develop and deploy Java applications in Windows Azure • How to architect horizontally scalable applications in Windows Azure
  • http://highscalability.com/blog/2010/2/8/how-farmville-scales-to-harvest-75-million-players-a-month.htmlhttp://techcrunch.com/2010/09/22/zynga-moves-1-petabyte-of-data-daily-adds-1000-servers-a-week/
  • To build for big scale – use more of the same pieces, not bigger pieces; though a different approach may be neededPictures source:http://lego.wikia.com/wiki/10179_Ultimate_Collector%27s_Millennium_Falconhttp://lego.wikia.com/wiki/7778_Midi-scale_Millennium_Falcon
  • Source: http://danga.com/words/2007_06_usenix/usenix.pdf
  • Source: http://highscalability.com/blog/2007/11/13/flickr-architecture.html
  • Source: http://www.slideshare.net/jboutelle/scalable-web-architectures-w-ruby-and-amazon-s3
  • Source: http://www.slideshare.net/netik/billions-of-hits-scaling-twitterSource: http://highscalability.com/blog/2009/6/27/scaling-twitter-making-twitter-10000-percent-faster.html
  • Source: http://highscalability.com/blog/2009/10/12/high-performance-at-massive-scale-lessons-learned-at-faceboo-1.html

Scale as a Competitive Advantage Presentation Transcript

  • 1. Scale as a Competitive Advantage
    David Chou
    david.chou@microsoft.com
    blogs.msdn.com/dachou
  • 2. The age of “big data”
    2009: 600K photos served /sec
    2010: ~1PB / 60 minutes
    (projected)
    2008: ~1B views / day
    Source: Wired Magazine: Issue 16.07, 2008.06.23; illustration by Marian Bantjes
    http://www.wired.com/science/discoveries/magazine/16-07/pb_intro
  • 3. “More is different”
    Infinite storage. Clouds of processors. Our ability to capture, warehouse, and understand massive amounts of data is changing science, medicine, business, and technology. As our collection of facts and figures grows, so will the opportunity to find answers to fundamental questions. Because in the era of big data, more isn't just more. More is different.
    Source: Wired Magazine: Issue 16.07, 2008.06.23
    http://www.wired.com/science/discoveries/magazine/16-07/pb_intro
  • 4. “The future belongs to the companies and people that turn data into products”
    Source: “What is data science?”, An O’Reilly Radar Report, 2010.06.02, Mike Loukides
    http://radar.oreilly.com/2010/06/what-is-data-science.html
  • 5. Working with data at scale
    45M tweets pattern visualization in minutes
    #justinbieber cluster
    #teaparty cluster
    …. “political world has more connective tissue than of-the-moment entertainment”
    Source: “Data science democratize”, 2010.07.01, Mac Slocum
    http://radar.oreilly.com/2010/07/data-science-democratized.html
  • 6. Big data needs big processing
    Facebook (2009)
    +200B pageviews /month
    >3.9T feed actions /day
    +300M active users
    >1B chat mesgs /day
    100M search queries /day
    >6B minutes spent /day (ranked #2 on Internet)
    +20B photos, +2B/month growth
    600,000 photos served /sec
    25TB log data /day processed thru Scribe
    120M queries /sec on memcache
    Twitter (2009)
    600 requests /sec
    avg 200-300 connections /sec; peak at 800
    MySQL handles 2,400 requests /sec
    30+ processes for handling odd jobs
    process a request in 200 milliseconds in Rails
    average time spent in the database is 50-100 milliseconds
    +16 GB of memcached
    Google (2007)
    +20 petabytes of data processed /day by +100K MapReduce jobs
    1 petabyte sort took ~6 hours on ~4K servers replicated onto ~48K disks
    +200 GFS clusters, each at 1-5K nodes, handling +5 petabytes of storage
    ~40 GB /sec aggregate read/write throughput across the cluster
    +500 servers for each search query < 500ms
    >1B views / day on Youtube (2009)
    Myspace(2007)
    115B pageviews /month
    5M concurrent users @ peak
    +3B images, mp3, videos
    +10M new images/day
    160 Gbit/sec peak bandwidth
    Flickr (2007)
    +4B queries /day
    +2B photos served
    ~35M photos in squid cache
    ~2M photos in squid’s RAM
    38k req/sec to memcached (12M objects)
    2 PB raw storage
    +400K photos added /day
    Source: multiple articles, High Scalability
    http://highscalability.com/
  • 7. Bing Maps
    Big data collection and processing
    flying planes over nearly every inch of the United States
    on road photos
    45-degree low-altitude aerial photos
    high altitude plane photos
    satellite photos
    10% done (August 2010)
    previous “all USA” flight image gathering exercise took 10 years
    5PB storage and thousands of servers in one container
    Source: “Map Wars (visiting Bing’s imaging center)”, 2010.08.10, Robert Scoble
    http://scobleizer.com/2010/08/10/map-wars-visiting-bings-imaging-center/
  • 8. Cloud computing
    Characteristics
    On-demand self-service
    Broad network access
    Resource pooling
    Rapid elasticity
    Measured service
    Service models
    Software as a service
    Platform as a service
    Infrastructure as a service
    Deployment models
    Private cloud
    Community cloud
    Public cloud
    Hybrid cloud
    “Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.”
    Source: The NIST Definition of Cloud Computing, Version 15, 2009.10.07, Peter Mell and Tim Grance
    http://csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.doc
  • 9. Cloud levels the playing field
    2007
    founded by 6 people
    2008
    $29M funding from VC
    2009
    revenue - $270M
    $180M funding from Digital Sky Technologies
    2010
    1,200+ employees
    $300M funding from Google and Softbank
    Active unique players
    215M monthly; 10% of world internet population (updated 2010.10); 60M daily
    1M daily 4 days after launch; 10M after 60 days
    3B neighborhood connections
    Cloud infrastructure
    12,000 Amazon EC2 nodes
    Adding 1,000 servers per week (updated 2010.10)
    Moving 1PB data per day (updated 2010.10)
    3 Gigabits/sec of traffic between FarmVille and Facebook (at peak)
    caching cluster serves another 1.5 Gigabits/sec to the application
    Source(s): “How FarmVille Scales to Harvest 75 Million Players a Month”, HighScalability.com, 2010.02.08, Tedd Hoff
    “Zynga Moves 1 Petabyte Of Data Daily; Adds 1,000 Servers A Week”, TechCrunch.com, 2010.09.22, LeenaRao
  • 10. Cloud as a platform
    Utility computing
    on-demand infrastructure
    self-provisioning and servicing
    rapid elasticity
    economy of scale
    operational expenditures
    Infrastructure-as-a-Service
    Service delivery model
    … but cloud computing != cloud hosting
  • 11. Cloud as a platform
    Native cloud applications
    horizontal scaling (scale-out)
    parallelization
    shared-nothing architecture
    partitioned data (sharding)
    multi-tenancy
    failure resilient (or fail-in-place)
    service-oriented
    federated composition
    Platform-as-a-Service
    Application development model
  • 12. Service delivery models
    (On-Premise)
    Infrastructure
    (as a Service)
    Platform
    (as a Service)
    Software
    (as a Service)
    You manage
    Applications
    Applications
    Applications
    Applications
    You manage
    Data
    Data
    Data
    Data
    Runtime
    Runtime
    Runtime
    Runtime
    Managed by vendor
    Middleware
    Middleware
    Middleware
    Middleware
    You manage
    Managed by vendor
    O/S
    O/S
    O/S
    O/S
    Managed by vendor
    Virtualization
    Virtualization
    Virtualization
    Virtualization
    Servers
    Servers
    Servers
    Servers
    Storage
    Storage
    Storage
    Storage
    Networking
    Networking
    Networking
    Networking
  • 13. Use more pieces, not bigger pieces
    LEGO 7778 Midi-scale Millennium Falcon
    • 9.3 x 6.7 x 3.2 inches (L/W/H)
    • 14. 356 pieces
    LEGO 10179 Ultimate Collector's Millennium Falcon
    • 33 x 22 x 8.3 inches (L/W/H)
    • 15. 5,195 pieces
  • Live Journal (from Brad Fitzpatrick, then Founder at Live Journal, 2007)
    Web Frontend
    Apps & Services
    Partitioned Data
    Distributed
    Cache
    Distributed Storage
  • 16. Flickr (from Cal Henderson, then Director of Engineering at Yahoo, 2007)
    Web Frontend
    Apps & Services
    Distributed Storage
    Distributed
    Cache
    Partitioned Data
  • 17. SlideShare(from John Boutelle, CTO at Slideshare, 2008)
    Web
    Frontend
    Apps &
    Services
    Distributed Cache
    Partitioned Data
    Distributed Storage
  • 18. Twitter (from John Adams, Ops Engineer at Twitter, 2010)
    Web
    Frontend
    Apps &
    Services
    Partitioned
    Data
    Queues
    Async
    Processes
    Distributed
    Cache
    Distributed
    Storage
  • 19. Distributed
    Storage
    Facebook
    (from Jeff Rothschild, VP Technology at Facebook, 2009)
    2010 stats (Source: http://www.facebook.com/press/info.php?statistics)
    People
    +500M active users
    50% of active users log on in any given day
    people spend +700B minutes /month
    Activity on Facebook
    +900M objects that people interact with
    +30B pieces of content shared /month
    Global Reach
    +70 translations available on the site
    ~70% of users outside the US
    +300K users helped translate the site through the translations application
    Platform
    +1M developers from +180 countries
    +70% of users engage with applications /month
    +550K active applications
    +1M websites have integrated with Facebook Platform
    +150M people engage with Facebook on external websites /month
    Web
    Frontend
    Apps &
    Services
    Distributed
    Cache
    Parallel
    Processes
    Partitioned
    Data
    Async
    Processes
  • 20. Cloud computing as a new paradigm
    Scale-out architecture + distributed computing
    small logical units of work
    loosely-coupled processes
    stateless
    event-driven design
    optimistic concurrency
    partitioned data
    redundancy fault-tolerance
    re-try-based recoverability
    parallel tasks
    app server
    web
    data store
    app server
    web
    data store
    web
    app server
    data store
    app server
    web
    data store
    app server
    web
    data store
    app server
    web
    data store
    async tasks
  • 21. Strategic advantages of cloud computing
    cost reduction
    cost reduction
    time to market
    pay by use
    ability to scale
  • 22. What’s next?
    Data
    data federation
    data purification
    data democratization
    derived intelligence
    Process
    Web as a platform
    federated applications
    adaptive agents
  • 23. Thank you!
    David Chou
    david.chou@microsoft.com
    blogs.msdn.com/dachou
    © 2010 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
    The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.