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Some Interesting Examples of Distributing Computing

Some Interesting Examples of Distributing Computing. Michael Kilian October 21, 2011. Themes. Autonomous agents that loosely collaborate Many “processors” that come into “proximity” with other processors Incomplete information No single agent has a global view of the “world”

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Some Interesting Examples of Distributing Computing

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  1. Some Interesting Examples of Distributing Computing Michael Kilian October 21, 2011

  2. Themes • Autonomous agents that loosely collaborate • Many “processors” that come into “proximity” with other processors • Incomplete information • No single agent has a global view of the “world” • Unreliable communication • Agents talk to one another but may lose information, have information corrupted, etc. • Mobile assets • Processors move around and their proximity to other agents also changes • May create a global “answer”

  3. Use Cases to Explore • The ants go marching one by one • The Battlefield Example • Caching content for mobile users • Nanobots • CAVEAT: None of these examples are “real” in the sense that I have documented requirements from anyone for these

  4. Distributing Computing in Nature Local changes create global effect. Errors can be accommodated. Lots of examples in nature. Explicit communication between individuals is also exhibited. Local communication is propagated quickly to mobilize an entire hive without the benefit of any broadcasting.

  5. “The Battlefield” Multi-tiered information provides global view Local information can be sent over “noisy” lines to mobile receivers Information can be distributed on the ground Local groups gather information Enemies may try to disrupt communication

  6. How do we Approach this System • Network coding for distributing information in a very unreliable and noisy environment • Potential for easy encryption by encrypting coefficients rather than all the data • How do we manage metadata? • Network coding for “moving data” across distributed groups • If we view this as a network of pieces of information, how do we preserve the entirety of the content?

  7. Content View Is this realistic? Wouldn’t we simply disseminate the information globally to start with (with network coding)

  8. Metadata • How do we describe the information we are collecting and disseminating? • How are logical collections created? • Especially in the face of incomplete information? • What kinds of questions are we asking? • Is Google (Web Crawling, content correlation, search) a computational model?

  9. “The Battlefield” Distribute computing to the data vs. collecting data to the compute units

  10. What Questions can we Answer? • The condition of an object • Is someone hurt? Where are you located? • Relationships • Is this group near that group? • Tougher questions – Quantitative • Does this group have enough MREs to last through the week? • Tougher questions – Temporal • When did the state of an object change?

  11. Moving Collective Information As the receiver moves through the geography, information can track the receiver. Not all information needs to move – move only what is required to reconstruct it (See Exact Regeneration Codes for Distributed Storage Repair Using Interference Alignment, Changho Suh and Kannan Ramchandran)

  12. Is this a Real Example? • Sounds good when we consider a single person making the transition • What if we consider 1000’s of receivers transiting this geography over a period of time • Now “hot” content needs to be over the entire geography • So…use coding for the long-tail content freeing resources for hot content • That’s counterintuitive

  13. Nanobots

  14. A “bot” Connection point for communicating with a neighbor. Not all connection points need be used. Sensor for detecting A, G, T, C Botcan store a limited amount of state (it is not really important how much state this is). It is also capable of rudimentary processing. A G T C

  15. Bot “Manifolds” Can we design a “system” such that we determine if the sequence T C G T T exists? A T G C A A G C G A A T T G C T G A C T T T A C A C C C G T

  16. How do we Approach This? • Discover neighbors • Establish roles • Bot next to the A,T,C,or G (terminal bot), Bot with connection to n terminal bots within m “hops” • Network coding to allow very transient networks, etc.? Is there enough compute power? • How is consensus derived? How is consensus communicated? • Computation of the very limited!

  17. What about “Cloud?” Processors come and go, layout is not necessarily uniform or known beforehand, connectivity is not uniform or reliable

  18. Thank-you

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