_{1}

^{*}

Due to the lack of QoS (quality of service) guarantee in current Peer-to-Peer services routing network, it is difficult to apply Peer-to-Peer network to business successfully. Therefore, a service guarantee routing model is proposed in this paper, and an ant colony algorithm is designed for this routing model. Finally, the experimental analysis of the Peer-to-Peer services routing algorithm is presented. The experimental result shows the effectiveness of the service routing algorithm.

Peer-to-Peer (P2P) service routing is an important area of the P2P network. Currently, the P2P routing is divided into the categories of unstructured and structured routing algorithms. Unstructured P2P network is not fixed logical topology of the P2P network. Joining and leaving Nodes affect only the direct neighbors. The message overhead is relatively small. Typical unstructured P2P network routing includes Gnutella, Freenet, APPN, NeuroGrid [

For the P2P service routing in this paper, factors affecting the service routing can be considered from the following aspects: the connection bandwidth between network nodes; the maximum bandwidth of node; The session time between nodes. P2P service network routing goal is to establish a service path that meet the above requirements from source node to the destination node, and get the maximum throughput of P2P services along the selected path.

P2P services routing problem can be defined as:_{i}b_{j} is available bandwidth between i and j, then, _{ij} show the minimum available bandwidth between i and j, a service quality assurance of the P2P services routing goal is to find a path to make P2P services network throughput maximization, and satisfy the following requirements. Select the Maximum bandwidth utilization rate; the effective bandwidth between the nodes is between connection bandwidth and acceptable bandwidth.

In a undirected graph_{ij} is bandwidth between the node I and the node j, F_{ij} show traffic between node I and node j, then defined as follows:

Assume source s and destination d, _{i} is one of path in_{ij} is bandwidth between the node i and the node j, x_{ij} is binary variables. x_{ij} = 1, _{ij} is available bandwidth between node I and node j, c_{ij} is the minimum accepted bandwidth between node i and j.

In summary, the problem can be described as the following:

In this model, the objective function is: the set of multiple paths between a given source and destination, find one of these paths which has maximum utilization of the bandwidth of the path of service.

Nodes in the P2P service network play an important role in routing. The bandwidth of the node is related to the performance of the entire network, so attributes of high capacity nodes are considered. In this paper, the available bandwidth of nodes is considered and guarantees the available bandwidth of nodes so as to ensure that each service can be achieved availability. First, the P2P service routing model is built, then ant colony algorithm to solve the service routing problems is design.

Ant colony or swarm intelligence algorithm [

The selection probability of ants on each route (i, j) can be described as follows:

As

In Equation (3):

_{ij} show traffic through path (i, j); B_{ij} is the bandwidth through path (i, j).

Now

In this Equation (4),

In this equation,

(6)

In Equation (6), Q is a constant. It is the pheromone after ant finish one complete path search. L_{k} is the total path bandwidth utilization of the k ant. It is equal to the sum of the k-th segment of the passed path of the ants bandwidth utilization. If the total bandwidth utilization of the path of the ants is higher, concentration of pheromone is higher by released per unit path. Obviously, the ants will not release pheromone on the path which has never experienced.

Dynamic Ant Colony Algorithm for solving problem as follows:

Global search ability and search speed of the application of ant colony algorithm used to solve the P2P service network routing problem [

Begin

Initialization, the m ants on the starting node s,

Loop

For k = 1 to m do

Ant k to determine if the current node i connecting the node has gone through.

Calculate the probability of all nodes are not traversed, and selecting randomly the next node j according to the probability applying for Equation (2), (3); Update the pheromone concentration in the path between nodes m i and j applying for Equation (4), (5) and (6),

If node j is object node t then

Determine the path bandwidth utilization is the largest, if it is the largest path then record the point

The starting node s and destination node t are interchangeable and empty node traversed records

End

If approximate bandwidth utilization is greater than the sum given the current bandwidth utilization

then exit Loop

Else go to Loop

End.

Environment of the experiment to build 16 units of the Red Hat Linux operating system on 16 PCs, using Java to design and realization [

will generate the network topology. In order to verify the effectiveness of service-based P2P service routing algorithm, randomly selected 16 as an ordinary node. The data communication between nodes used the Java RMI mechanism, synchronization cycle simulation. In each round a loop, each node is read from its input queue, and then processed according to the specified routing rules.

P2P service routing algorithm, assume

From

The convergence rate in different network scale under the conditions of two kinds of algorithms, it can be seen from

is

This work was financially supported by the Hebei province science and technology research project (No. Z2014038). The work was supported by the Fundamental Research Funds for the Central Universities (3142014125, 3142015022, 3142013098, and 3142013070).

Gaoxiao Yan, associate professor, Dr., majors in communication and information system. Research directions include the computer network and the network security. She was a visiting scholar at Queen’s University in Canada.

Xiaoyan Gao, (2015) Design and Implementation of Peer-to-Peer Service Routing Algorithm. Journal of Software Engineering and Applications,08,575-580. doi: 10.4236/jsea.2015.811054