slides.0329.2011 - Epidemics Michael Ford Simon Krueger 1...

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Epidemics Michael Ford Simon Krueger 1
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It’s just like Telephone! 2
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Epidemic Convergence If there are n nodes and each node gossips to log(n)+k other nodes on average, then the probability that everyone gets the message converges to e^(-e^(-k)). A. Ganesh, A.-M. Kermarrec and L. Massoulie, Peer-to-peer membership management for gossip-based protocols, IEEE Transactions on Computers 52 (2003) (2), pp. 139–149. 3
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Bimodal Multicast (pbcast) Kenneth P. Birman, Mark Hayden, Oznur Ozkasap, Zhen Xiao, Mihai Budiu, and Yaron 4
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Motivation Best-Effort Protocols Increased scalability No end-to-end delivery guarantee Hard to reason about system state during failures Reliable Protocols Strong atomic guarantees – “all or nothing” Throughput is not resilient to slow nodes One bad apple spoils the bunch 5
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Throughput Stability 6
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Bimodal Multicast (pbcast) Atomicity – almost all or almost none Throughput Stability – low variance Ordering – per sender FIFO Multicast Stability – minimal message buffer Lost Message Detection Scalability – “Costs are constant or grow slowly as a function of the network size” 7
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Pbcast details Best-effort broadcast IP Multicast or Multicast Tree Anti-entropy Gossip a message list summary Detect message loss Pull messages if needed 8
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Pbcast Example 9 Note: Broadcast and Anti-entropy stages occur concurrently.
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  • Spring '08
  • Gupta
  • MAC address, Wireless sensor network, Network topology, Percolation threshold, Percolation theory, ATIM window

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slides.0329.2011 - Epidemics Michael Ford Simon Krueger 1...

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