Big Kahuna

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Presentation given at Google Product Prodigy 2009.

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Big Kahuna

  1. 1. What is the Big Kahuna • A Framework for performing massively distributed computation using grid of stateless transient nodes made up of mix of web browsers, handhelds and thick clients which make it a truly ubiquitous distributed computing platform with no install or setup overheads.
  2. 2. Some History Conceived - Summer of 2007 Originally a candidate for Product Prodigy 08 Paper submitted to WWW 09 Student initiated project at IIIT- Bangalore
  3. 3. Inspiration A very common sight amongst Internet users. So, why not get some useful work done whilst the browsers are idle.
  4. 4. Problems with current technology Thick App • Clients have to be downloaded and installed Network • Ports have to be opened up on firewalls Granularity • Not meant for small data sets
  5. 5. The Big Kahuna Solution • Make the clients thin – remove the download and install overhead • Use browser based resources like JavaScript (which is usually untapped). • Make the process seamless
  6. 6. Advantages • Zero installation /setup procedures • Built on Open Source technologies • Scales rapidly as more clients are added • Extremely low startup overheads • Problems modeled using the Map Reduce paradigm • Platform and Browser independent implementation • Works on devices and phones. Truly ubiquitous distributed computing platform.
  7. 7. Technology
  8. 8. The Big Kahuna is perfect … • To Process large volumes of highly granular data < 100Kb (ex: blogs ) • Solve a large subset of problems in IR and data mining • Leverage idle time on the local network or the web • Deploy a distributed computing solution on shared hosting servers • If data is present on the client itself, eg offline data on browsers.
  9. 9. Trendsetter? HTML 5 proposes features for Offline storage. Kahuna is perfect to be the framework for client side processing.
  10. 10. Monetization Market as a Market as a Product Service A marketplace can The Kahuna Server be setup, with will be available as a scriptwriters and customized and fully publishers as actors supported product
  11. 11. Demo Problems Non Gears Gears Workflow Workflow: • Auto Tagging • Searching for small blog 3x3 patterns in articles using Chess Logs (log Gears store and analysis) worker pool.
  12. 12. What Big Kahuna isn’t .. • It doesn’t guarantee speed up of operations, primarily due to the network latency. It only guarantees distribution. • Not targeted at datasets with large data unit size (ex : multimedia data )
  13. 13. Barriers and Challenges • The compute stubs are bound by the power of Javascript/ other client side technology • Bandwidth limitations hinder true performance benefits of using the framework. • Implement client side using other technologies like flex/AIR, Silverlight etc

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