This document describes simulations of peer-to-peer (P2P) networks using different protocols, configurations, and parameters. It discusses running many simulations to analyze the results using Tableau. Key aspects studied include average, maximum, and minimum aggregation functions, network size and topology, value distributions, and static versus dynamic network configurations. Real-world P2P networks tend to be either structured, unstructured, or hybrid depending on their topology and search capabilities.

1. P2P: Simulations and
Real World Networks
Matilda Rhode and Matt Courtney

2. (Partial) Average function
public void nextCycle(Node node, int protocolID) {
int linkableID = FastConfig.getLinkable(protocolID);
Linkable linkable = (Linkable) node.getProtocol(linkableID);
if (linkable.degree() > 0) {
Node peer = linkable.getNeighbor(CommonState.r.nextInt(linkable
.degree()));
// Failure handling
if (!peer.isUp())
return;
AverageFunction neighbor = (AverageFunction) peer
.getProtocol(protocolID);
double mean = (this.value + neighbor.value) / 2;
this.value = mean;
neighbor.value = mean;
}
}

3. (Partial) Maximum function
public void nextCycle(Node node, int protocolID) {
int linkableID = FastConfig.getLinkable(protocolID);
Linkable linkable = (Linkable) node.getProtocol(linkableID);
if (linkable.degree() > 0) {
Node peer = linkable.getNeighbor(CommonState.r.nextInt(linkable
.degree()));
// Failure handling
if (!peer.isUp())
return;
MaximumFunction neighbor = (MaximumFunction) peer
.getProtocol(protocolID);
double maximum = this.value
if (neighbor.value > this.value) {
maximum = neighbor.value;
}
this.value = maximum;
neighbor.value = maximum;
}
}

4. (Partial) Minimum function
public void nextCycle(Node node, int protocolID) {
int linkableID = FastConfig.getLinkable(protocolID);
Linkable linkable = (Linkable) node.getProtocol(linkableID);
if (linkable.degree() > 0) {
Node peer = linkable.getNeighbor(CommonState.r.nextInt(linkable
.degree()));
// Failure handling
if (!peer.isUp())
return;
MaximumFunction neighbor = (MaximumFunction) peer
.getProtocol(protocolID);
double maximum = this.value
if (neighbor.value < this.value) {
maximum = neighbor.value;
}
this.value = maximum;
neighbor.value = maximum;
}
}

5. Gossip-based protocols
• Gossip or epidemic protocol
• Each node has only a partial view of the network

6. When all nodes hold the
same value
Using Incremental Stats printed in average observer to see when
network is ‘settled’
control.avgo: 0 1.0 99.99999999999999 50 50.5 867.4354435651799 1 1
CDSimulator: cycle 0 done
control.avgo: 1 1.0 69.6938775510204 50 23.386122448979595 414.34298362076936 7 1
CDSimulator: cycle 1 done
control.avgo: 2 1.0 31.30612244897959 50 5.162040816326529 49.386130098853386 26 2
CDSimulator: cycle 2 done
control.avgo: 3 1.0 7.061224489795918 50 1.4040816326530612 1.6661425086486068 44 2
CDSimulator: cycle 3 done
control.avgo: 4 1.0 1.0 50 1.0 0.0 50 50
CDSimulator: cycle 4 done
Largest value
held by
any node
Cycle
Smallest value
held by
any node
Network
size
Mean
value
Variance
in values
Total nodes
holding
minimum
value
Total nodes
holding
maximum
value
Minimum function, linear distribution of values (1,100) , network size = 50

7. When all nodes hold the
same value
Using Incremental Stats printed in average observer to see when
network is ‘settled’
control.avgo: 0 1.0 99.99999999999999 50 50.5 867.4354435651799 1 1
CDSimulator: cycle 0 done
control.avgo: 1 1.0 69.6938775510204 50 23.386122448979595 414.34298362076936 7 1
CDSimulator: cycle 1 done
control.avgo: 2 1.0 31.30612244897959 50 5.162040816326529 49.386130098853386 26 2
CDSimulator: cycle 2 done
control.avgo: 3 1.0 7.061224489795918 50 1.4040816326530612 1.6661425086486068 44 2
CDSimulator: cycle 3 done
control.avgo: 4 50 1.0 0.0 50 50
CDSimulator: cycle 4 done
Largest value
held by
any node
Cycle
Smallest value
held by
any node
Network
size
Mean
value
Variance
in values
Total nodes
holding
minimum
value
Total nodes
holding
maximum
value
Minimum function, linear distribution of values (1,100) , network size = 50
1.0 1.0

8. When all nodes hold the
same value
Using Incremental Stats printed in average observer to see when
network is ‘settled’
control.avgo: 0 1.0 99.99999999999999 50 50.5 867.4354435651799 1 1
CDSimulator: cycle 0 done
control.avgo: 1 1.0 69.6938775510204 50 23.386122448979595 414.34298362076936 7 1
CDSimulator: cycle 1 done
control.avgo: 2 1.0 31.30612244897959 50 5.162040816326529 49.386130098853386 26 2
CDSimulator: cycle 2 done
control.avgo: 3 1.0 7.061224489795918 50 1.4040816326530612 1.6661425086486068 44 2
CDSimulator: cycle 3 done
control.avgo: 4 1.0 1.0 1.0 0.0
CDSimulator: cycle 4 done
Largest value
held by
any node
Cycle
Smallest value
held by
any node
Network
size
Mean
value
Variance
in values
Total nodes
holding
minimum
value
Total nodes
holding
maximum
value
Minimum function, linear distribution of values (1,100) , network size = 50
505050

9. Varying parameters
• Protocol {AverageFunction, MaximumFunction, MinumumFunction}
• Network size [2,50000]
• Distribution of values {Linear, Peak}, distribution parameters
• k, directed edges out of each node [1,8]
• Network Topography {WireKOut, WireINet, WireRegRootedTree, WireRingLattice,
WireStar, WireWS}
• Network dynamism {Static, Dynamic, Oscillating}, dynamic and oscillating parameters

10. Distribution
Static config file
random.seed 1234567890
control.shf Shuffle
simulation.cycles 10
network.size 100
protocol.lnk IdleProtocol
protocol.0 example.aggregation.
protocol.0.linkable lnk
init.rnd WireKOut
init.rnd.protocol lnk
init.rnd.k 5
init.lin LinearDistribution
init.lin.protocol 0
init.lin.max 100
init.lin.min 1
init.peak example.aggregation.PeakDistributionInitializer
init.peak.value 10000
init.peak.protocol 0
include.init rnd lin
control.avgo example.aggregation.AverageObserver
control.avgo.protocol 0
k
MaximumFunction
Distribution Parameters
Protocol
Network size

11. Distribution
Static config file
random.seed 1234567890
control.shf Shuffle
simulation.cycles 10
network.size
protocol.lnk IdleProtocol
protocol.0 example.aggregation.
protocol.0.linkable lnk
init.rnd WireKOut
init.rnd.protocol lnk
init.rnd.k 5
init.lin LinearDistribution
init.lin.protocol 0
init.lin.max 100
init.lin.min 1
init.peak example.aggregation.PeakDistributionInitializer
init.peak.value 10000
init.peak.protocol 0
include.init rnd lin
control.avgo example.aggregation.AverageObserver
control.avgo.protocol 0
Protocol
Network size
MaximumFunction
100
k
Distribution Parameters

12. Static config file
Protocol
Network size
DistributionMaximumFunction
100
100
1
10000
lin
k
Distribution Parameters
random.seed 1234567890
control.shf Shuffle
simulation.cycles 10
network.size
protocol.lnk IdleProtocol
protocol.0 example.aggregation.
protocol.0.linkable lnk
init.rnd WireKOut
init.rnd.protocol lnk
init.rnd.k 5
init.lin LinearDistribution
init.lin.protocol 0
init.lin.max
init.lin.min
init.peak example.aggregation.PeakDistributionInitializer
init.peak.value
init.peak.protocol 0
include.init rnd
control.avgo example.aggregation.AverageObserver
control.avgo.protocol 0

13. random.seed 1234567890
control.shf Shuffle
simulation.cycles 10
network.size
protocol.lnk IdleProtocol
protocol.0 example.aggregation.
protocol.0.linkable lnk
init.rnd WireKOut
init.rnd.protocol lnk
init.rnd.k 5
init.lin LinearDistribution
init.lin.protocol 0
init.lin.max
init.lin.min
init.peak example.aggregation.PeakDistributionInitializer
init.peak.value
init.peak.protocol 0
include.init rnd
control.avgo example.aggregation.AverageObserver
control.avgo.protocol 0
Static config file
Protocol
Network size
DistributionMaximumFunction
100
100
1
10000
lin
k
Distribution Parameters

14. random.seed 1234567890
control.shf Shuffle
simulation.cycles 10
network.size
protocol.lnk IdleProtocol
protocol.0 example.aggregation.
protocol.0.linkable lnk
init.rnd WireKOut
init.rnd.protocol lnk
init.rnd.k
init.lin LinearDistribution
init.lin.protocol 0
init.lin.max
init.lin.min
init.peak example.aggregation.PeakDistributionInitializer
init.peak.value
init.peak.protocol 0
include.init rnd
control.avgo example.aggregation.AverageObserver
control.avgo.protocol 0
Static config file
Protocol
Network size
DistributionMaximumFunction
100
100
1
10000
lin
k
Distribution Parameters
5

15. Static config file
MaximumFunction
100
100
1
10000
lin
1
random.seed 1234567890
control.shf Shuffle
simulation.cycles 10
network.size
protocol.lnk IdleProtocol
protocol.0 example.aggregation.
protocol.0.linkable lnk
init.rnd WireKOut
init.rnd.protocol lnk
init.rnd.k
init.lin LinearDistribution
init.lin.protocol 0
init.lin.max
init.lin.min
init.peak example.aggregation.PeakDistributionInitializer
init.peak.value
init.peak.protocol 0
include.init rnd
control.avgo example.aggregation.AverageObserver
control.avgo.protocol 0

16. Observations
• Ran many simulations and recorded results
• Used Tableau to analyse

17. Protocol

18. Network size
Network ~ Linear(0,100); k=5; static network

19. Distribution

20. k

21. Benefits of Simulation
Allows for a ‘sterile’ environment where approaches can be
designed to improve the components of a P2P network’s
functionality.

22. Benefits of Simulation
Test various approaches and topologies for their suitability in
the given scenario. This may imply a suitability for a real life
application.

23. Other Projects
“http://doc.tm.uka.de/tr/TM-2011-4.pdf” , “https://dspace.stir.ac.uk/bitstream/1893/3688/1/ccnc-2011-jamie.pdf”

24. Dynamic config file
Removing nodes/
Oscillating
Dynamic
parameters
simulation.cycles 5
network.size 100
protocol.link IdleProtocol
protocol.coord example.hot.InetCoordinates
protocol.1 example.aggregation.MaximumFunction
protocol.1.linkable link
init.0 example.hot.InetInitializer
init.0.protocol coord
init.1 peersim.dynamics.WireRingLattice
init.1.protocol link
init.1.coord_protocol coord
init.1.k 5
init.ld LinearDistribution
init.ld.protocol 1
init.ld.max 100
init.ld.min 1
control.dnet DynamicNetwork
control.dnet.add -10
control.dnet.from 1
control.dnet.until 5
k/alpha/
k, beta
Topography

25. simulation.cycles 5
network.size 100
protocol.link IdleProtocol
protocol.coord example.hot.InetCoordinates
protocol.1 example.aggregation.MaximumFunction
protocol.1.linkable link
init.0 example.hot.InetInitializer
init.0.protocol coord
init.1 peersim.dynamics.WireRingLattice
init.1.protocol link
init.1.coord_protocol coord
init.1.k 5
init.ld LinearDistribution
init.ld.protocol 1
init.ld.max 100
init.ld.min 1
control.dnet DynamicNetwork
control.dnet.add
control.dnet.from
control.dnet.minsize
Dynamic config file
Removing nodes/
Oscillating
Dynamic
parameters
-10
1
2
100
20
3
k/alpha/
k, beta
Topography

26. simulation.cycles 5
network.size 100
protocol.link IdleProtocol
protocol.coord example.hot.InetCoordinates
protocol.1 example.aggregation.MaximumFunction
protocol.1.linkable link
init.0 example.hot.InetInitializer
init.0.protocol coord
init.1
init.1.protocol link
init.1.coord_protocol coord
init.1.k 5
init.ld LinearDistribution
init.ld.protocol 1
init.ld.max 100
init.ld.min 1
control.dnet DynamicNetwork
control.dnet.add
control.dnet.from
control.dnet.minsize
Dynamic config file
Removing nodes/
Oscillating
Dynamic
parameters
Topography
k/alpha/
k, beta
-10
1
2
100
20
3
peersim.dynamics.WireRingLattice

27. simulation.cycles 5
network.size 100
protocol.link IdleProtocol
protocol.coord example.hot.InetCoordinates
protocol.1 example.aggregation.MaximumFunction
protocol.1.linkable link
init.0 example.hot.InetInitializer
init.0.protocol coord
init.1
init.1.protocol link
init.1.coord_protocol coord
init.1.
init.ld LinearDistribution
init.ld.protocol 1
init.ld.max 100
init.ld.min 1
control.dnet DynamicNetwork
control.dnet.add
control.dnet.from
control.dnet.minsize
Dynamic config file
Removing nodes/
Oscillating
Dynamic
parameters
Topography
k/alpha/
k, beta
-10
1
2
100
20
3
peersim.dynamics.WireRingLattice
k 5

28. Dynamic config file
Removing nodes/
Oscillating
Dynamic
parameters
Topography
k/alpha/
k, beta
-10
1
2
100
20
3
peersim.dynamics.WireRingLattice
k 5
simulation.cycles 5
network.size 100
protocol.link IdleProtocol
protocol.coord example.hot.InetCoordinates
protocol.1 example.aggregation.MaximumFunction
protocol.1.linkable link
init.0 example.hot.InetInitializer
init.0.protocol coord
init.1
init.1.protocol link
init.1.coord_protocol coord
init.1.
init.ld LinearDistribution
init.ld.protocol 1
init.ld.max 100
init.ld.min 1
control.dnet DynamicNetwork
control.dnet.add
control.dnet.from
control.dnet.minsize

29. Dynamic Network (killing nodes)

30. Oscillating Network

31. Oscillating Network

32. Oscillating Network

33. WireKOut

34. WireInetTopology

35. WireRegRootedTree

36. WireRingLattice

37. WireStar

38. WireWS

39. Topographies
2
2

40. Topographies
2
2
2
2

41. Real World P2P
• Structured - Overlay has fixed topology, protocol
aims to ensure an efficient search of network for a
given resource.
ImageSRC:- “https://personalpages.manchester.ac.uk/staff/m.dodge/cybergeography/atlas/gnucleus_graph_large.gif”

42. Real World P2P
• Unstructured - No formal topology, easy to build.
Allow for localised optimisations and are highly
resilient against problems caused with high ‘churn’.
ImageSRC:- “http://courses.cse.tamu.edu/caverlee/csce438/hw/rand_graph.png"

43. Real World P2P
• Hybrid - Combination of P2P and client-server
models. Currently tend to have better all round
performance versus pure P2P and client-server.
ImageSRC:- “http://www.di.unipi.it/~hkholidy/projects/cids/CIDS%20in%20Pure%20P2P%20model.gif”

44. Real World P2P
• Spotify(2011) - Hybrid model, utilises centralised
servers, P2P network, and a local cache. Only non
web-based on-demand music streaming service.
ImageSRC:- “http://pansentient.com/2011/04/spotify-technology-some-stats-and-how-spotify-works/“
~8
~36~56

45. Real World P2P
• Spotify - Simple use case
ImageSRC:- “http://pansentient.com/2011/04/spotify-technology-some-stats-and-how-spotify-works/“
• User selects track
• If cached, play from there
• Else
• Request first 15secs from servers.
• Search P2P network for remainder of song.

46. Real World P2P
• Spotify’s P2P Network - Works like BitTorrent,
using server-side trackers and network queries to
locate peers. Returned peers are those that can
support playback.
Predictable listening habits - Most users listen to the
same music multiple times or listen to albums. Makes
predictive caching possible, reducing network traffic.