epidemics michael ford simon krueger 1. it’s just like telephone! 2
TRANSCRIPT
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Epidemics
Michael FordSimon Krueger
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IT’S JUST LIKE TELEPHONE!
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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.
• P. Erdös and A. Renyi, “On the Evolution of Random Graphs,” Mat Kutato Int. Közl, vol. 5, no. 17, pp. 17-60, 1960.
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Bimodal Multicast (pbcast)
Kenneth P. Birman, Mark Hayden, Oznur Ozkasap, Zhen Xiao, Mihai
Budiu, and Yaron Minsky
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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
– Background overhead reaches “meltdown” levels
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Throughput Stability
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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”
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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• Why not push?
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Pbcast Example
Note: Broadcast and Anti-entropy stages occur concurrently.
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Assumptions
• Faults– Network errors are independent and identically
distributed– Known, bounded, link delays– No Byzantine failures
• System– Each node knows every other node
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Computational Results
• Bcast unsuccessful • 5% message loss• 0.1% crash rate for run
• What is the ideal shape?
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Rounds to Delivery
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Issues
• Are slow processes always going to fall behind and slow down other processes?
• What if a processes receives multiple message queries?
• How do you determine when to stop buffering a message? (Scalability)
• Random gossip through a router can be a bottleneck.
• How does membership management affect scalability?
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Optimizations1) Soft-Failure Detection – Retransmit only in the round
that request was received2) Round retransmission limit – Cap data per round3) Cyclic Retransmissions – Avoid repeat message
retransmissions from previous rounds4) Most-Recent-First Retransmission – Stops processes
from permanently lagging5) Independent Numbering of Rounds – No
synchronization needed among processes6) Random Graphs for Scalability – Gossip only to a
subset of the processes7) Multicast for Some Retransmissions – Multicast upon
receiving multiple requests for the same message
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Comparison to a Strong Protocol
The effects of Soft faults on Throughput
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Effects of Network Congestion
The effects of Link faults on Throughput
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Comparison to SRM
Why compare pbcast to SRM (a reliable protocol) and not a best effort protocol?
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QUESTIONS?
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Exploring the Energy-Latency Trade-off for Broadcasts in Energy-Saving Sensor Networks
M. Miller, C. Sengul, I. Gupta, ICDCS 2005Presented By
Simon Krueger
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Outline1. Motivation and Background
– The Problem
– Existing Solutions
2. Core Ideas
– Probability-Based Broadcast Forwarding
3. Experimental Results
– Reliability
– Energy
– Latency
– Energy-Latency Trade-off
4. Discussion
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The Problem
• Wireless Sensor Networks (WSNs) use Motes that have a battery lifetime of a few weeks
• Message broadcast is useful for applications in WSNs
– Disseminating software updates (e.g., Trickle)
– Forwarding sensor observations
• Increasing reliability and performance causes greater depletion of battery
• Designers need flexibility between reliability and performance
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Existing Solution(s): Energy Efficient Medium Access Control (MAC) protocols• Active-sleep cycle
– Active Time
– Sleep Time
1. IEEE 802.11 Power Safe Mode (PSM)
– Synchronized active sleep schedule
2. S-MAC
– Virtual clusters of synchronized active sleep schedules
3. T-MAC
– Dynamic active sleep schedule
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Broadcast in IEEE 802.11 PSM
Node 1
Node 2
Node 3
B
A D1 B
A
A D1
D1
B
ATIM window ATIM window ATIM window
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1
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Probability-Based Broadcast Forwarding (PBBF)
• Design a broadcast protocol on top of existing energy efficient MAC layer protocols that allows a designer to tune energy and latency at different levels of reliability
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PBBF Adds Two New Parameters
1.p is the probability that a node rebroadcasts a packet immediately
2.q is the probability that a given node stays awake instead of sleeping
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PBBF Demonstration
Node 1
Node 2
Node 3
B
B
A D
ID B
ATIM window
ID
ATIM window ATIM window
p
q
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1
23
p
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PBBF (cont.)• p presents a tradeoff in latency and reliability
– As p ⬆, latency ⬇
– As p ⬆, fraction of nodes receiving a broadcast ⬇(unless q = 1)
• q represents a tradeoff in energy and reliability
– As q ⬆, energy consumption ⬆
– As q ⬆, fraction of nodes receiving a broadcast ⬆ (unless p = 0)
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Experimental Data
• Assumptions:
– Ideal MAC layer
– Ideal physical layer with no collisions or interference
• IEEE 802.11 PSM
• Grid network topology (i.e., a square lattice)
• Broadcast source is near the center of the grid
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• N is the number of nodes• λ is the source’s broadcast
rate• PTX is power to transmit• PI is power to idle/receive• PS is power to sleep• L1 is the latency
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Open Road
Closed Road
D Destination
D
S Source
S
Bond Percolation Theory
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Reliability
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Reliability
≥80% Reliability≥90% Reliability≥99% Reliability≈100% Reliability
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Average Energy Consumption
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Latency
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S
D20
Hop
s
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Latency
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Energy-Latency Tradeoff
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Energy-Latency Trade-off
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Code Distribution Application
• Study Trade Off Between Energy, Latency, and Reliability
• ns-2 network simulator• Collisions and interference present
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Discussion
• Why use IEEE 802.11 PSM for simulation results?
• How well would this work for other protocols like S-MAC and T-MAC?
• When studying reliability, why use Bond percolation theory over Site percolation theory?
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