P2P: Simulations and Real world Networks

P2P: Simulations and Real World Networks

Matilda Rhode and Matt Courtney

(Partial) Average functionpublic 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; } }

(Partial) Maximum functionpublic 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()));


MaximumFunction neighbor = (MaximumFunction) peer .getProtocol(protocolID); double maximum = this.value

if (neighbor.value > this.value) { maximum = neighbor.value; }

this.value = maximum; neighbor.value = maximum; } }

(Partial) Minimum functionpublic 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()));


MaximumFunction neighbor = (MaximumFunction) peer .getProtocol(protocolID); double maximum = this.value

if (neighbor.value < this.value) { maximum = neighbor.value; }

this.value = maximum; neighbor.value = maximum; } }

Gossip-based protocols

• Gossip or epidemic protocol

• Each node has only a partial view of the network

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 1CDSimulator: cycle 0 donecontrol.avgo: 1 1.0 69.6938775510204 50 23.386122448979595 414.34298362076936 7 1CDSimulator: cycle 1 donecontrol.avgo: 2 1.0 31.30612244897959 50 5.162040816326529 49.386130098853386 26 2CDSimulator: cycle 2 donecontrol.avgo: 3 1.0 7.061224489795918 50 1.4040816326530612 1.6661425086486068 44 2CDSimulator: cycle 3 donecontrol.avgo: 4 1.0 1.0 50 1.0 0.0 50 50CDSimulator: cycle 4 done

Largest value held by

any nodeCycle

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



control.avgo: 0 1.0 99.99999999999999 50 50.5 867.4354435651799 1 1CDSimulator: cycle 0 donecontrol.avgo: 1 1.0 69.6938775510204 50 23.386122448979595 414.34298362076936 7 1CDSimulator: cycle 1 donecontrol.avgo: 2 1.0 31.30612244897959 50 5.162040816326529 49.386130098853386 26 2CDSimulator: cycle 2 donecontrol.avgo: 3 1.0 7.061224489795918 50 1.4040816326530612 1.6661425086486068 44 2CDSimulator: cycle 3 donecontrol.avgo: 4 50 1.0 0.0 50 50CDSimulator: cycle 4 done


any nodeCycle


any node

Network size

Mean value

Variance in values

Total nodes holding

minimum value

Total nodes holding

maximum value


1.0 1.0



control.avgo: 0 1.0 99.99999999999999 50 50.5 867.4354435651799 1 1CDSimulator: cycle 0 donecontrol.avgo: 1 1.0 69.6938775510204 50 23.386122448979595 414.34298362076936 7 1CDSimulator: cycle 1 donecontrol.avgo: 2 1.0 31.30612244897959 50 5.162040816326529 49.386130098853386 26 2CDSimulator: cycle 2 donecontrol.avgo: 3 1.0 7.061224489795918 50 1.4040816326530612 1.6661425086486068 44 2CDSimulator: cycle 3 donecontrol.avgo: 4 1.0 1.0 1.0 0.0 CDSimulator: cycle 4 done


any nodeCycle


any node

Network size

Mean value

Variance in values

Total nodes holding

minimum value

Total nodes holding

maximum value


50 50 50

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

Distribution

Static config filerandom.seed 1234567890control.shf Shuffle

simulation.cycles 10network.size 100 protocol.lnk IdleProtocol

protocol.0 example.aggregation.protocol.0.linkable lnk init.rnd WireKOutinit.rnd.protocol lnkinit.rnd.k 5

init.lin LinearDistributioninit.lin.protocol 0init.lin.max 100init.lin.min 1

init.peak example.aggregation.PeakDistributionInitializerinit.peak.value 10000init.peak.protocol 0

include.init rnd lin

control.avgo example.aggregation.AverageObservercontrol.avgo.protocol 0

k

MaximumFunction

Distribution Parameters

ProtocolNetwork size

Distribution

Static config filerandom.seed 1234567890control.shf Shuffle

simulation.cycles 10network.size protocol.lnk IdleProtocol


init.lin LinearDistributioninit.lin.protocol 0init.lin.max 100init.lin.min 1

init.peak example.aggregation.PeakDistributionInitializerinit.peak.value 10000init.peak.protocol 0

include.init rnd lin


ProtocolNetwork size

MaximumFunction

100

kDistribution Parameters

Static config fileProtocol

Network sizeDistribution

MaximumFunction

100

1001

10000

lin


random.seed 1234567890control.shf Shuffle



init.lin LinearDistributioninit.lin.protocol 0init.lin.max init.lin.min

init.peak example.aggregation.PeakDistributionInitializerinit.peak.valueinit.peak.protocol 0

include.init rnd







include.init rnd




MaximumFunction

100

1001

10000

lin




protocol.0 example.aggregation.protocol.0.linkable lnk init.rnd WireKOutinit.rnd.protocol lnkinit.rnd.k



include.init rnd




MaximumFunction

100

1001

10000

lin


5

Static config file

MaximumFunction

100

1001

10000

lin

1



protocol.0 example.aggregation.protocol.0.linkable lnk init.rnd WireKOutinit.rnd.protocol lnkinit.rnd.k



include.init rnd


Observations

• Ran many simulations and recorded results

• Used Tableau to analyse

Protocol

Network size

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

Distribution

k

Benefits of Simulation

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

functionality.

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.

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”

http://doc.tm.uka.de/tr/TM-2011-4.pdf

https://dspace.stir.ac.uk/bitstream/1893/3688/1/ccnc-2011-jamie.pdf

Dynamic config fileRemoving nodes/

OscillatingDynamic

parameterssimulation.cycles 5

network.size 100

protocol.link IdleProtocol

protocol.coord example.hot.InetCoordinates

protocol.1 example.aggregation.MaximumFunctionprotocol.1.linkable link

init.0 example.hot.InetInitializer init.0.protocol coordinit.1 peersim.dynamics.WireRingLatticeinit.1.protocol linkinit.1.coord_protocol coordinit.1.k 5

init.ld LinearDistributioninit.ld.protocol 1init.ld.max 100init.ld.min 1

control.dnet DynamicNetworkcontrol.dnet.add -10control.dnet.from 1control.dnet.until 5

control.onet OscillatingNetwork

k/alpha/ k, betaTopography

simulation.cycles 5

network.size 100




init.0 example.hot.InetInitializer init.0.protocol coordinit.1 peersim.dynamics.WireRingLatticeinit.1.protocol linkinit.1.coord_protocol coordinit.1.k 5


control.dnet DynamicNetworkcontrol.dnet.add control.dnet.from control.dnet.minsize



OscillatingDynamic

parameters

-101

2

100203

k/alpha/ k, betaTopography

simulation.cycles 5

network.size 100




init.0 example.hot.InetInitializer init.0.protocol coordinit.1init.1.protocol linkinit.1.coord_protocol coordinit.1.k 5





OscillatingDynamic

parameters Topography k/alpha/ k, beta

-101

2

100203

peersim.dynamics.WireRingLattice

simulation.cycles 5

network.size 100




init.0 example.hot.InetInitializer init.0.protocol coordinit.1init.1.protocol linkinit.1.coord_protocol coordinit.1.





OscillatingDynamic


-101

2

100203


k 5


OscillatingDynamic


-101

2

100203


k 5

simulation.cycles 5

network.size 100




init.0 example.hot.InetInitializer init.0.protocol coordinit.1init.1.protocol linkinit.1.coord_protocol coordinit.1.




Dynamic Network (killing nodes)

Oscillating Network

Oscillating Network

Oscillating Network

WireKOut

WireInetTopology

WireRegRootedTree

WireRingLattice

WireStar

WireWS

Topographies

2

2

Topographies

2

2

2

2

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”

https://personalpages.manchester.ac.uk/staff/m.dodge/cybergeography/atlas/gnucleus_graph_large.gif

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"

http://courses.cse.tamu.edu/caverlee/csce438/hw/rand_graph.png

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”

http://www.di.unipi.it/~hkholidy/projects/cids/CIDS%20in%20Pure%20P2P%20model.gif

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

http://pansentient.com/2011/04/spotify-technology-some-stats-and-how-spotify-works/

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.

http://pansentient.com/2011/04/spotify-technology-some-stats-and-how-spotify-works/

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.

Technology

P2P: Simulations and Real world Networks