1. School of Electrical Engineering &Telecommunications UNSW Cost-effective Broadcast for Fully Decentralized Peer-to-peer Networks Marius Portmann & Aruna Seneviratne

2. School of Electrical Engineering &Telecommunications UNSW Peer to Peer Systems Two types –Structured Guarantee location of content (if exists) Access within bounded number of hops  Control of data placements and topology –Unstructured Decentalized Looser guarantees  Placement of data and topology is ad-hoc

3. School of Electrical Engineering &Telecommunications UNSW Wireless Environments Characterized by –Highly transient node populations –Wide range of users form non cooperating organizations –Searches on partial information –Not typically looking for “rare” information – replicated at a number of places Not a good match for structured systems –Back to unstructured systems

4. School of Electrical Engineering &Telecommunications UNSW Unstructured Systems Most widely used application of p2p systems is file sharing –As the placement of data is ad-hoc Only random searchers are possible Hard to find desired files without wide distribution of queries Unscalable unless can improve the efficiency of searches

5. School of Electrical Engineering &Telecommunications UNSW Example - Gnutella Gnutella can be considered as “pure” peer- to-peer system –Fully decentralized and distributed searching Operation of Gnutella –Two types of services Searching for files Peer discovery –Implemented with application level broadcasts –Broadcast is implemented with TTL flooding

6. School of Electrical Engineering &Telecommunications UNSW File Location A query message is forwarded to all its neighbors, except for the one, where it was received from Each message has a Time To Live (TTL) –Decremented by one at each visited node –Message is dropped when TTL=0 Each message has an unique ID Node keeps a record of IDs of messages that it has seen in the recent past –Message with the same ID and type as ones that that have been received are dropped

7. School of Electrical Engineering &Telecommunications UNSW Cost Metric 1 Define a cost metric for comparison of methods –number of messages that are generated and forwarded – based solely on the network size and the average node degree, Estimate the average bandwidth consumption per node

8. School of Electrical Engineering &Telecommunications UNSW Cost Metric 2

9. School of Electrical Engineering &Telecommunications UNSW Flooding - Unscalable Resource consumption per node of flooding based broadcast can be prohibitively high, even for networks of moderate size

10. School of Electrical Engineering &Telecommunications UNSW Rumor Mongering or Gossip Protocols A class of probabilistic protocols for message routing Messages are spread in a network much like a disease in a susceptible population. (epidemiological protocol) The neighbors to which messages are forwarded to by each node are chosen randomly. Trades off reliability and speed for a reduction in cost

11. School of Electrical Engineering &Telecommunications UNSW Blind Counter Rumor Mongering A node n initiates a broadcast –Send the message m to B neighbors, chosen at random –When a node (p) receives a message m from anther node (q) If (p has received m no more than F times) p sends m to B uniformly randomly chosen neighbors that p knows have not yet seen m –p knows if its neighbor q has already seen the message m only if p has sent it to q previously, or if p received the message from q

12. School of Electrical Engineering &Telecommunications UNSW Cost of BCRM Difficult to obtain analytical expressions to describe the behavior of a Gossip protocol, even for relatively simple topologies Can give an upper limit – bounded by BF- an upper limit for the cost c

13. School of Electrical Engineering &Telecommunications UNSW Simulation Results Barabási Topology: –Model for generating topology is based on how typical p2p networks evolve –Power-law characteristics 1000 nodes with an average node degree of 6 –F and B for the BCRM was set to be 2

14. School of Electrical Engineering &Telecommunications UNSW Some More Results Trade-off of cost, reliability and time by choosing F and B appropriately Level of cost reduction depends on the average node degree –The higher the node degree is, the bigger the potential for cost reduction

15. School of Electrical Engineering &Telecommunications UNSW P2P Network Topologies Typical characteristic of peer-to-peer networks is a power-law distribution of the node degrees – most nodes have few links while a small number of nodes have a large number of links From Matei Ripeanu & Ian Foster

16. School of Electrical Engineering &Telecommunications UNSW Deterministic Rumor Mongering Make intelligent decisions as to which of its neighbors to forward messages to Based it on the node degree of the corresponding nodes –The nodes with the lowest degree are chosen first

17. School of Electrical Engineering &Telecommunications UNSW Deterministic Rumor Mongering cont. When a node p receives a message m from node q –If (p has received m no more than F times) 1)send m to all of its neighbors of degree one, and 2)B of the rest of its neighbors with the lowest node degree, that p knows have not yet seen m

18. School of Electrical Engineering &Telecommunications UNSW Rationale for (1) Pendant neighbors, have no other chance to receive the message These pendant neighbors cannot contribute to the further propagation of the message – not considered for the limit of B messages to be forwarded

19. School of Electrical Engineering &Telecommunications UNSW Rationale for (2) Nodes of high degree receive a large number of copies of the same message –This overhead grows approximately linearly with the node degree –Also with higher parameters B and F.

20. School of Electrical Engineering &Telecommunications UNSW Viability The only requirement is that each node knows the node degree of its immediate neighbors Not in conflict with the decentralized nature of the networks –Can easily be integrated –Gnutella a one byte field in the Gnutella message header Increasing the minimal message size by less than 5%.

21. School of Electrical Engineering &Telecommunications UNSW Some Results Performance of Deterministic Rumor Mongering compared to Blind Counter Rumor Mongering For a given B and F, DRM achieves a significant higher reach than the BCRM, within a shorter time For a given reach, DRM has a significantly lower cost

22. School of Electrical Engineering &Telecommunications UNSW Some More Results  BCRM  DRM (B,F)

23. School of Electrical Engineering &Telecommunications UNSW Conclusions Unstructured peer-to-peer systems are more suitable for wireless environments For unstructured systems to be viable, scalable methods of searching need to developed The obvious way of is to look at alternatives to broadcast One such scheme that have been used in the past in other application is Rumor Mongering (Gossiping) We show that, Rumor Mongering, can be used as a basis for providing an alternative flooding for distributing queries in unstructured peer to peer systems

24. School of Electrical Engineering &Telecommunications UNSW More Information Available form mobqos.ee.unsw.edu.au M. Portmann, Pipat Sookavatna, Sebstien Ardon and Aruna Seneviratne,”The Cost of Peer Discovery and Searching in the Gnutella Peer-to-peer File Sharing Protocol”, IEEE ICON 2001, Bangkok, September 2001 M. Portmann, and Aruna Seneviratne, “The Cost of Application-level Broadcast in a fully Decentralized Peer-to-peer Networks”, ISCC, Italy, July 2002 M. Portmann, and Aruna Seneviratne, “Cost-effective Broadcast for Fully Decentralized Peer-to-peer Networks”, accepted for publication, Computer Communication, Special Issue on Ubiquitous Computing Also related work Qin Lv, Sylvia Ratnasamy and Scott Shenker,”Can Heterogeneity Make Gnutella Scalable?”, 1st International Workshop on Peer-to-Peer Systems (IPTPS '02), Cambridge, MA, USA, March 2002 Berverly Yang, and Hector Garcia-Molina,”Efficient Search in Peer-to-Peer Networks”, 1st International Workshop on Peer-to-Peer Systems (IPTPS '02), Cambridge, MA, USA, March 2002

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