1. UNIVERSITY OF JYVÄSKYLÄ New Topology Management Algorithms for Unstructured P2P Networks Presentation for The Second International Workshop on P2P Systems and Applications 14.5.2007 Annemari Auvinen, research student Department of Mathematical Information Technology University of Jyväskylä, Finland http://www.mit.jyu.fi/cheesefactory With co-authors Mikko Vapa, Matthieu Weber, Niko Kotilainen and Jarkko Vuori

2. UNIVERSITY OF JYVÄSKYLÄ Contents Topology Management Algorithms: –Node Selection –Node Removal –Overload Estimation –Overtaking Results

3. UNIVERSITY OF JYVÄSKYLÄ Topology Management 1/2 Logical (i.e. overlay) topology on top of the physical network In an unstructured network a node's place in the network is not pre-defined like it is in a structured network A node may join the network by establishing a connection to another node on the P2P network

4. UNIVERSITY OF JYVÄSKYLÄ Topology Management 2/2 Topology management algorithms affect the topology by making network more scalable and effective for resource discovery Nodes are placed so that they stay connected and find resources efficiently without using too much of their capacity for being in the network Network can be kept connected

5. UNIVERSITY OF JYVÄSKYLÄ Topology Management Algorithms Use local information the nodes are collecting about their neighbors Active neighbors and history –IP address and port number –Request time and success –The amount of the resource replies the node has got from the neighbor and (Hit value) –The amount of the resource replies the neighbors neighbor has relayed to the node (Relayed hit value)

6. UNIVERSITY OF JYVÄSKYLÄ Goodness Algorithms are based on the goodness of the node A good neighbor node provides resources to the node Goodness = hit value + relayed hit values

7. UNIVERSITY OF JYVÄSKYLÄ Node Selection Node tries to establish connections to the neighbors which it had before leaving the network Node searches a node to which to establish a new connection from the history based on hit values and request information 1. Nodes with hit values and old request time 2. Nodes with old request time or unrequested nodes 3. Nodes without hit values and request time 4. Nodes with hit values

8. UNIVERSITY OF JYVÄSKYLÄ Node Removal Selects the worst neighbor Worst neighbor is a neighbor which has the smallest goodness value

9. UNIVERSITY OF JYVÄSKYLÄ Overload Estimation Connections are established and dropped based on the amount of traffic going through the node Algorithm compares the calculated traffic amount to predefined traffic limit values (upper and lower limits) If the traffic amount is more than the upper traffic limit one neighbor is dropped by using Node Removal If the traffic amount is less than the lower traffic limit, node tries to add a new neighbor by using Node Selection

10. UNIVERSITY OF JYVÄSKYLÄ Overtaking A node moves closer to the good nodes by overtaking the current neighbor If neighbor has a neighbor whose proportion of neighbors goodness is more than the defined overtaking percent, a new connection to that node is established and current neighbor is dropped 1 2 3 4 Hits:2 Relayed hits:19 (68%) Relayed hits:7 (25%) 1 2 3 4

11. UNIVERSITY OF JYVÄSKYLÄ Results

12. UNIVERSITY OF JYVÄSKYLÄ Testing Peer-to-Peer Realm (P2PRealm) simulator 500 nodes, normally distributed network Three set of queries BFS-algorithm Upper traffic limits 100, 125, 150,.., 600 (messages/50 sent resource queries) Lower traffic limits 20%, 40% and 60% Overtaking 80% and without it

13. UNIVERSITY OF JYVÄSKYLÄ Neighbor Distribution Lower traffic limit 20%40%60% OvertakingNo80%No80%No80% DistributionNormal ly Power law Normal ly Power law Normal ly

14. UNIVERSITY OF JYVÄSKYLÄ Hops 1/2

15. UNIVERSITY OF JYVÄSKYLÄ Hops 2/2

16. UNIVERSITY OF JYVÄSKYLÄ Balance 1/3 The amount of changes with overtaking

17. UNIVERSITY OF JYVÄSKYLÄ Balance 2/3 The amount of changes without overtaking

18. UNIVERSITY OF JYVÄSKYLÄ Balance 3/3 without overtaking: lower traffic limits 20%, 40% and 60% the networks attained the balance With overtaking: –20% balanced –40% upper traffic limit >350 –60% upper traffic limit >325

19. UNIVERSITY OF JYVÄSKYLÄ Conclusion Best combination of parameters: lower traffic limit 40%, 80% overtaking, upper traffic limit over 350 messages/50 sent messages Amount of changes in the network was small, topology got balance, neighbor distribution was power law and number of hops small

20. UNIVERSITY OF JYVÄSKYLÄ References Auvinen A., Vapa M., Weber M., Kotilainen N., Vuori J., "Chedar: Peer-to-Peer Middleware", Proceedings of the 19th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2006), Rhodes Island, Greece, 2006. Kotilainen N., Vapa M., Keltanen T., Auvinen A., Vuori J., "P2PRealm - Peer-to-Peer Network Simulator", 11th International Workshop on Computer-Aided Modeling, Analysis and Design of Communication Links and Networks (CAMAD'06), IEEE Communications Society, pp. 93-99, Trento, Italy, 2006.