1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 1 From Packet-level to Flow-level Simulations of P2P Networks Kolja Eger, Ulrich Killat Hamburg University of Technology ITG-Fachgruppentreffen, Aachen 4. Mai 2006

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 2 Overview P2P Content Distribution Packet-level Simulation Flow-level Simulation Simulation complexity & accuracy Conclusion

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 3 P2P Content Distribution Objective: Disseminate large files in minimal time to a large number of users Swarming principle: –A file is fragmented into small pieces which can be shared before download of the whole file is completed –E.g.: BitTorrent protocol Our research interests: –Efficiency: Peer has something of interest for at least one other peer at any point of time –Fairness: Peers which contribute much should also gain much ⇒ incentive to contribute

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 4 Complexity of P2P Simulation P2P networks are complex: –Large and varying peer populations –Peer behaviour is user-driven –Peers provide and consume different services, e.g. exchange different pieces of a file with each other –Services are offered with different quality, e.g. upload bandwidth –Each peer has only local information about the network Only simple cases can be studied analytically, e.g. flash crowd of homogeneous peers Simulations must be based on simplifications

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 5 Packet-level Simulations Assumptions: –Access line of the peers is the bottleneck in the network –No packet drops in the core network Simplified topology: –Access link plus overlay link –Different RTTs between access routers –No. of links: Z = (N P -1)N P /2 + N P = N P /2 (N P +1) –Memory increases quadratically with N P –No. of events is decreased, because of small no. of hops

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 6 Peer contacts tracker Connects to other peers Inform about pieces Check interest Peer selection (Unchoke) Request pieces Upload Event 1Event 2 Download Have Check interest Timer x Event x BitTorrent Messages Packet-level Flow-level

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 7 TCP Behaviour In BitTorrent each peer uploads to a number of other peers (default = 5) (called unchoking) Every 10s peers are chosen based on the download rates from them Exponential increase RTT C up / (No. of uploads) * RTT Upload Capacity: 1*10 kbit/s to 30*10 kbit/s RTT 1*10ms to 25*10ms If uplink of a peer is the bottleneck, TCP reduces to exponential increase at the beginning TCP throughput / max. throughput

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 8 Flow-level Simulation In peer selection algorithm download volume is computed beforehand If remote peer needs less, it is redistributed over the remaining connections Thus, peer allocates its upload bandwidth max-min fair Surplus / 3 Surplus / 2 Volume = (Upload Capacity * unchoking interval) / (No. of uploads) Demand

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 9 Simulation Setup Flash crowd scenario where a single peer holds the complete file at the beginning Time measured until all peers have finished their download No peer leaves the network beforehand File size: 10MB, piece size: 256KB Homogeneous peers with upload capacity of 10KB/s and download capacity 8 times higher (asymmetric access line) Packet-level simulation with ns-2 Flow-level simulation uses timer functionality of ns-2 Simulation are run on a Pentium 4: 3,2 GHz, 1 GB RAM

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 10 Simulation Time approx. 11h for 60 peers with packet-level compared to 2 sec. with flow-level simulation Calendar queue is used

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 11 Map List Calendar insert: O(log(n)) delete: O(log(n)) Heap insert: O(log(n)+) delete: O(log(n)+) insert: O(n) delete: O(1) insert: O(1) to O(n) delete: O(1) to O(n) Event Scheduler No. of peers Simulation Time [s]

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 12 Flow-level Simulation Memory consumption No. of PeersMB MB MB MB MB MB MB peers in less than 30min.

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 13 Standard-Upload-Kapazität B/s = 10 KiB/s = 80 Kib/s ADSL B/s = 75 KiB/s = 600 Kib/s ADSL B/s = 24 KiB/s = 192 Kib/s ADSL B/s = 16 KiB/s = 128 Kib/s 640 B/s = 0,625 KiB/s = 5 Kib/s Flow-level Simulation (cont.) Simulation time for a flash crowd of 4000 peers with different upload capacities Higher capacities result in less events for the same download volume

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 14 Simulation Accuracy Both curves have the same shape But results differ by around 10% Reasons: –Packet headers –TCP behaviour –Load for BitTorrent messages

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 15 Conclusion Packet-level simulation does not scale for P2P networks Flow-level simulation is inevitable to study networks of reasonable size Results with flow-level simulator are qualitatively comparable but underestimate the true values due to the simplifications made Flow-level simulation is a good compromise to study protocol design But inadequate to take cross-layer interactions into account, e.g. unchoking is based on TCP throughput which depends on RTT

1 Communication Networks Kolja Eger, Prof. Dr. U. Killat 16 Thank you for your attention!