UNIVERSITY OF JYVÄSKYLÄ Topology Management in Unstructured P2P Networks Distributed Systems Research Seminar on Annemari Auvinen, Research Student Department of Mathematical Information Technology

UNIVERSITY OF JYVÄSKYLÄ 2007 Content Topology Management Topology Management Algorithms Chedar and P2PRealm simulator NeuroTopology Future

UNIVERSITY OF JYVÄSKYLÄ 2007 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

UNIVERSITY OF JYVÄSKYLÄ 2007 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 Self-organizing using local information

UNIVERSITY OF JYVÄSKYLÄ 2007 Topology Management Algorithms Are based on the goodness of the node A good neighbor node provides resources to the node Goodness is sum of –The amount of the resource replies the node has got from the neighbor and –The amount of the resource replies the neighbor’s neighbor has relayed to the node

UNIVERSITY OF JYVÄSKYLÄ 2007 Node Selection and Node Removal Node searches a node to which to establish a new connection from the history based on hit values and request information Removed node is the ”worst” neighbor Worst neighbor is a node which has the lowest goodness value

UNIVERSITY OF JYVÄSKYLÄ 2007 Overload Estimation Connections are established and dropped based on the traffic amount flowing through the node If the traffic meter value is more than the given traffic limit one node is dropped by using Node Removal If the traffic meter value is less than the given lower traffic limit, algorithm tries to establish a connection to new node by using Node Selection

UNIVERSITY OF JYVÄSKYLÄ 2007 Overtaking Node moves closer to the ”good” nodes If neighbor has neighbor whose relayed hits proportion of all neighbor’s neighbors’ relayed hits and neighbor’s hits is more than the given percent node establishes a new connection to that node and current connection to the neighbor is dropped Hits:2 Relayed hits:6 (60%) Relayed hits:2 (20%)

UNIVERSITY OF JYVÄSKYLÄ 2007 Results Best combination of parameters: lower traffic limit 40%, 80% overtaking, 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

UNIVERSITY OF JYVÄSKYLÄ 2007 Chedar Decentralized P2P middleware implemented using Java Basis for P2P applications: distributed computing (P2PDisCo), data fusion, extension for mobile devices Includes the topology management algorithms, but because of errors in connections and machines affect results -> algorithms were implemented and tested in P2PRealm simulator

UNIVERSITY OF JYVÄSKYLÄ 2007 NeuroTopology Topology construction using neural networks The idea is that every peer has a neural network to make decisions about establishing new connections in a P2P network NeuroTopology algorithm is executed in every peer after a predefined amount of resource queries The algorithm goes through all neighbor candidates The information that the neural network needs, is gathered during resource queries

UNIVERSITY OF JYVÄSKYLÄ 2007 Inputs Bias = 1. CurrentNeighborsAmount is the number of node's neighbors ToNeighborsAmounts is the number of node's candidate neighbor’s neighbors RepliesFromCandidates is the number of the resource replies received from a candidate neighbor RelayedRepliesFromCandidates is the number of the resource replies which the candidate neighbor has relayed to the node TrafficMeter is a counter, which calculates the amount of the resource reply messages going through a node TrafficLimit simulates the bandwidth of a candidate node. If TrafficMeter value is bigger than Trafficlimit, the node will not reply to resource requests

UNIVERSITY OF JYVÄSKYLÄ 2007 Training The weights of the neural network have to be optimized Evolutionary computing and Gaussian random variation were used Define the P2P network conditions Define the fitness requirements for the algorithm Create candidate algorithms randomly Select the best ones for next generation Breed a new population Finally select the best algorithm for these conditions Iterate thousands of generations

UNIVERSITY OF JYVÄSKYLÄ 2007 Fitness of the neural network The fitness function takes into account –The number of resource queries –The number of resource replies –The number of the packets the topology query used –The number of the new connections, i.e. topology changes

UNIVERSITY OF JYVÄSKYLÄ 2007 Results

UNIVERSITY OF JYVÄSKYLÄ 2007 Future NeuroTopology: comparing the results with other algorithms Bayesian Networks and Petri Nets in Topology Management

UNIVERSITY OF JYVÄSKYLÄ 2007 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, 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 , Trento, Italy, Auvinen A., Vapa M., Weber M., Kotilainen N., Vuori J., ”New Topology Management Algorithms for Unstructured P2P Networks”, to be published in the Second International Conference on Internet and Web Applications and Services, May Auvinen A., Keltanen T., Vapa M., ”Topology Management in Unstructured P2P Networks Using Neural Networks”, submitted to IEEE Congress on Evolutionary Computation, March 2007.