Efficient Simulation of
Large-Scale P2P Networks:
Packet-level vs. Flow-level Simulations
Kolja Eger
Hamburg University of Technology
Tobias Hoßfeld, Andreas Binzenhöfer
University of Würzburg
Gerald Kunzmann
Technical University of Munich
Communication Networks
Kolja Eger, Prof. Dr. U. Killat
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Overview
• P2P Content Distribution
• Motivation
• BitTorrent
• Simulation scenarios
• Simulation results
• Related work
• Conclusion
Communication Networks
Kolja Eger, Prof. Dr. U. Killat
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P2P Content Distribution
• Load is distributed among the peers, whereas servers carry all the load in
client/server architecture
 Scalability
• Aims at avoiding centralized services
 good resilience
• Multi-source download
(swarming, multipoint-to-point)
– File is fragmented into chunks
– Peer uploads when it finishes one chunk
– Load balancing
Communication Networks
Kolja Eger, Prof. Dr. U. Killat
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Motivation
• Analyze BitTorrent by simulation
• Packet-level simulation:
– Detailed simulation (e.g.TCP), cross-layer interactions visible
• Flow-level simulation:
– Simulation of large P2P networks possible
• Differences between packet and flow-level simulations?
• Strengths and weaknesses of BitTorrent?
• Which functionalities are useful for CDNs?
Communication Networks
Kolja Eger, Prof. Dr. U. Killat
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BitTorrent
Most popular P2P application
– Accounts for 30% of all traffic on the Internet in 2005
– Integrated in Opera browser, extension for Firefox available
• One overlay network for each file, NO search
• Tracker
– Centralized component which stores information about all peers
– Peers contact tracker to get a random subset of other peers in the network
– Newer versions support DHT  trackerless BitTorrent
• Peers exchange chunks with each other
Communication Networks
Kolja Eger, Prof. Dr. U. Killat
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Incentive mechanism in BitTorrent
• Unchoking: Peer uploads to a fixed number of other peers (default =5)
When downloading (leecher):
• Peer chooses peers from which it has highest download rates for upload
(every 10s)  tit-for-tat
• Optimistic unchoke:
– Find better connections than the currently used ones
– unchoke one peer independently from its rate (every 30s)
When download completed (seed):
• Choose peers based on upload rates
Communication Networks
Kolja Eger, Prof. Dr. U. Killat
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Chunk Selection
• When a peer is unchoked it requests a chunk according
– Strict priority:
• Request started but incomplete chunks
• Only complete chunks (where data integrity is verified) are
forwarded
– Rarest first:
• Estimate rarity of chunks by chunk information of neighbors
– Random
• Without any chunk request a random chunk
• Ensures faster completion of the first chunk
• Super-seeding: Original seed uploads whole file once to the network before
uploading duplicate chunks
Communication Networks
Kolja Eger, Prof. Dr. U. Killat
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Simulator
• Ns-2 for packet-level simulations
– Extend implementation to transfer application data
– Different topologies
• Star
• Overlay topology (access link + overlay link)
• German network
• Flow-level simulations:
– Upload bandwidth of peers is the bottleneck
– Ignore overhead of BitTorrent protocol and TCP
– Distribute bandwidth evenly over data connections
Communication Networks
Kolja Eger, Prof. Dr. U. Killat
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Simulation Scenarios
• Flash crowd
– One seed, P-1 leechers, U parallel uploads
– File size SF, chunk size SC, peers’ capacity C
– Download time until all peers have the file
• Constant peer population
– L leechers, S seeds (const.)
– Download rate of leechers:
Communication Networks
Kolja Eger, Prof. Dr. U. Killat
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Flash Crowd
• File size 100 MB
• Cuplink =1024 kbps
• Cdownlink = 8·Cuplink
< 27%
• Star topology with random
delay between 1-50ms
• Results with super-seeding 
• w/o super-seeding up to 70%
higher download times
< 9%
• For other Cuplink differences
< 20% and < 30% , resp.
Communication Networks
Kolja Eger, Prof. Dr. U. Killat
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Constant Peer Population
• 1 seed, 99 leechers
• Cuplink=1024kbps
• Topologies have same RTT
• Optimal: 811s
• Flow sim. only 3% higher
(838s on average)
• Packet sim. < 21% higher
(960s, 973s, 961s on ave.)
• Mean of 15s for first chunk
Communication Networks
Kolja Eger, Prof. Dr. U. Killat
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Delay
• Star topology
• Links with 10ms and 100ms
propagation delay
• Mean download time:
857s on 10ms links
1237s on 100ms links
 44% larger
 RTT bias
Communication Networks
Kolja Eger, Prof. Dr. U. Killat
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Related Work
• BitTorrent
– tit-for-tat does not avoid unfairness [3], especially for the allocation of
altruistically resources and with heterogeneous capacities
• Resource pricing [1,2,3]
– Controls the upload rate based on price information
– Ensures fairness and provides a Nash equilibrium
– No RTT bias
– As transport protocol avoids congested links of IP network
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Kolja Eger, Prof. Dr. U. Killat
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Conclusion
• BitTorrent’s main advantages for content distribution
– Use of bandwidth of all peers
– Multi-source download
• Flow-level simulations are near optimal performance
• Packet-level deviate at most by 30%
 BitTorrent is very efficient
• Higher simulation complexity at packet-level but cross-layer interactions are
taken into account
 BitTorrent has RTT bias, because TCP is used
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Kolja Eger, Prof. Dr. U. Killat
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Thank you for your attention!
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Kolja Eger, Prof. Dr. U. Killat
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[1] K. Eger, U. Killat, "Resource Pricing in Peer-to-Peer Networks", IEEE Communications
Letters, vol. 11, no.1, pp. 82--84, 2007
[2] K. Eger, U. Killat, "TCPeer: Rate Control in P2P over IP Networks", 20th International
Teletraffic Congress (ITC20), Ottawa, Canada, June 2007
[3] K. Eger, U. Killat, "Bandwidth Trading in Unstructured P2P Content Distribution
Networks", Proc. Sixth IEEE International Conference on Peer-to-Peer Computing
(P2P2006), Cambridge, UK, pp. 39--46, Sept. 2006
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