P2P Network Node Aggregation Algorithm Research Based on Community

P2P Network Node Aggregation Algorithm Research Based on Community Xiaoyan Gao* North China Institute of Science and Technology Department of Computer Science, Beijing‐east, China *

yanxiaogao@163.com

Abstract The network nodes in a Peer‐to‐peer (P2P) system are aggregated at the application layer of the TCP/IP suite. because the application layer do not match the physical network, it reduced the performance of the application layer.

To address the problem, this paper presents a community‐ based node aggregation algorithm in P2P networks. This algorithm takes into account the existing infrastructure of the community, the node to the existing network delay coding technology roadmap was used, Network nodes are divided into different communities so that the application‐ layer topology can be mapped to the physical‐layer structure. This algorithm is able to optimize the network utilization and provide insights for the theoretical research in P2P networks.

Keywords Community; P2P; Overlay Aggregation Algorithm

Network;

Discrete

Degree;

Introduction Compared with the traditional distributed systems, the P2P network has many advantages and broad application prospects. All kinds of P2P applications emerge on the Internet, and the number of users has increased dramatically. It is reported that some 50% of the foreign network traffic is occupied by the P2P network trafficand forecast in the next few years, peer‐ to‐peer (P2P) network traffic will account for more than 70% of the network traffic (Ou Zhonghong et al., 2008) P2P network technology is increasingly applied in the field of military, commercial, government information, communications and other fields.

Application of P2P network mainly includes the following several aspects: (Ou Zhonghong et al., 2008) (K.Aberer, M.Hauswirth, 2002 )files and content sharing, such as Napster, Gnutella, CAN, eDonkey, BT(BitTorrent) is the application examples in this regard also; peer‐to‐peer computing ability and storage sharing ability, For instance, SETI@home, Avaki, Popular applications such as Power;

collaborative sharing platform and services, such as JXTA, Magi, Groove, NET My Service; instant communication, including ICQ, OICQ, Yahoo, Messenger, MSN etc; communication and information sharing, such as CliqueNet, Crowds, Onion, Routing etc.

In many problems of P2P network, it is a key problem how to construct the overlay network. Structure coverage network directly determines the P2P network system scalability, robustness, security and performance, therefore, it is the primary problem how to construct suitable overlay network in P2P network, only it is solved, research on other issues can be go on.

Currently, the overlay P2P network mainly focus on distributed unstructured overlay network design, distributed structured overlay network design and semi distributed covering three aspects of network (Zhang guoyin, Lijun., 2013). (1) for mobile network distributed unstructured overlay network design mainly includes: (Niu XZ et al., 2010) propose a model of collaborative resource sharing among mobile nodes, the model of queuing theory and reliability theory to realize the prediction algorithm and the corresponding resource scheduling strategy based on the Gnutella protocol using; Li et al. Discuss the cross‐layer design problem in MANET (Li T et al., 2009) in file sharing, the network layer routing protocol AODV is extended to EAODV, and the Gnutella agreement in the flooding request replacement as a cross layer by layer and network layer, common to complete a file search operations; Shah proposed a cross layer design of unstructured overlay networks based on P2P applications (Shah N, Depei Q.A., 2010); literature (Mawji, A, Hassanein H., 2010) created network game model of Fabrikant et; Literature (Fabrikant A et al., 2003, Afzal M et al., 2011) presented a MANET in the creation of P2P network model. The model is a non‐ cooperative game model of complete information between nodes, then decide whether to establish a connection between nodes through the game, so as to

International Journal of Engineering Practical Research, Vol. 4 No. 1‐April 2015 33 2326‐5914/15/01 033‐05 © 2015 DEStech Publications, Inc. doi: 10.12783/ijepr.2015.0401.07

34 Xiaoyan Gao

realize the topology of the overlay network control; (2) for distributed mobile network structured overlay network, Hoang et al (Giang NH et al., 2009) discussed high disturbance wireless environment of Chord, by modifying the Chord protocol, adopted an atomic ring maintenance mechanism to improve the anti‐ interference capability of Chord on high disturbance; The literature (Shiguo W, Hong J., 2009) improve the performance of in wireless networks by modifying the Chord finger table and ID allocation strategy; (3) for mobile network semi distributed (hybrid) overlay network, Cheer (Sonia GF, Habib Y., 2009 Wang SG, Ji H., 2010) is a semi‐distributed wireless in‐network construction based on the theory of small world networks; Wang Shiguo of Beijing University of Posts and Telecommunications proposed a dynamic topological perception wireless peer‐to‐peer overlay (Tsao SL, Cheng CM., 2011).

Currently, for the structure of P2P network, the researchers carried out the beneficial attempt and exploration from different theoretical basis and different application fields, cross layer method involves the underlying protocol specific, this method has some limitations; using GPS and other auxiliary equipment based on geographic location, this method will also be accuracy and delay constraints.

In this paper, We propose a community‐based node aggregation algorithm to address the problem. The proposed algorithm is able to use the existing infrastructure community location, Furthermore, network provides the foundment for P2P network structure.

The Idea of P2P Network Node Aggregation Algorithm Based on Community Although the current P2P application is becoming more and more widely, it still lacks the effective mechanism of P2P topological structure to ensure the prosperous development of the network. Through the P2P network node aggregation algorithm, realizing the goal is to ensure more network nodes through our clustering algorithm to form multiple communities, then P2P application topology structure is composed of several community structure , this structure as far as possible keeps relative consistency between the P2P network structure and the physical network topology, at the same time it has good ability of extension.

In this paper, the community of the P2P network node clustering method is proposed. Main ideas are as follows: the algorithm will be close to the physical

distance of node togetherthat is say the geographically adjacent nodes were assigned to the same gather in order to make the logical topology of P2P network reflect the actual physical topology. P2P network node aggregation is the process of collection of the network node, the P2P network node is divided into a number of community, the distance within a community is as small as possible, and the distance between community is as large as possible.

P2P Network Community-based Node Aggregation Algorithm Problem about P2P Network Node Aggregation In P2P networks, A network graph can be defined as an ordered pair, denoted as G=(V,E) sets, among them V for the network nodes in the set, E for a set of logical link between link node, D is V a metric of V, here the D is defined as transfer delay between nodes in a network. The P2P network node aggregation problem is that the network node in the delay metric space is divided into different subgraph G 1 V 1 , E 1 , D 1  G 2 V 2 , E 2 , D 2  G k V k , E k , D k  , K for aggregation number, Vi , Ei , Di , respectively the subgraph nodes, link and delay between nodes link ( I = 1,... , K), and satisfy the following conditions:

Vi  V j   i  j  V1  V2  ...  V K  V (1) The Main Advantages of Node Aggregation Algorithm Easy to distinguish. P2P network node aggregation is the process of collection of the network node is divided into a number of community, the requirements in same the community, the distance between nodes may be small, and the distance between the community may be large. The distance between nodes within the same community and the distance between different community difference is bigger, so that easy to distinguish.

Real time. The aggregation algorithm is applied in P2P network system, and according to the nodes of the P2P network system in the end, and the number of large, strong dynamic, so this algorithm must have a real time.

Scalability. With node number increasing, The aggregation algorithm has good scalability.

The Description of the Node Aggregation Algorithm The calculation of distributed coding. P2P network

P2P Network Node Aggregation Algorithm Research Based on Community 35

nodes gathered the initialization process of the node aggregation algorithm based on the sign, the calculation of new nodes and road signs are adopted distributed coding delay during (Distributed Binning) method (Zhang guoqiang et al., 2012). Before the description aggregation algorithm, first of all, the calculation methods of distributed coding are presented. Distributed node coding goal is divided into different communities. This method only if k machines on the network is a road sign, IP address of the machine can be found in a DNS application, by measuring RTT the node and the sign of computer, and the return of sorting, thereby gaining the collating sequence.

Characteristics of effectiveness evaluation standards J e is defined as the ratio of s b and s w , s b is discrete degree between the community and s w is he discrete degree of community, the concrete expressions are given later.

TABLE 1 N NODE CODE IN K NODE

sample

Community

y1

Communiy …

v1

v11

…

v2

v 21

…

 vn

 vn1

Community

yk

…

v1k v2k  v nk

vector type in the class i .

Definition 2.3: Concentrate all the total average vector c   of all kinds of communities m . m   pk mk pk is the k 1

prior probability of class k , pk  N K N ,The first N is the total number of samples, N K is the k sample, the number of C for the community Definition 2.4: Average divergence communities is defined as follows: sb 

v  (vij ) nm

In order to give a good community evaluation index, we define several basic concepts first.

Definition 2.1: A group of community sample mean vector. If Ni is the number of node clustering i in the  i community, mi is the mean vector these nodes,  1  mi   y N i y

, i  1, 2, , c c is the number of

j

categories (Richard O. Duda et al., 2012).

Definition 2.2: Discrete degree of node in the same community. Each of the community and the mean square error between the square (distance) and the mean value is defined as the characteristic vector sw of the

class of discrete degree i  1 N  i   T  i    i . sw  yk  mi yk is the sample  yk  mi N i k 1 i







1 c   T     mi  m   mi  m  c i 1

(3)

J

Definition 2.5: Classification of evaluation index e is defined as: the ratio of average divergence between communities and all kinds of classes in of the average of the sum of the discrete degree.

J e  sb

c

p s j 1

j wj

s wj is the first j class inside the

class of discrete degree; p j is the prior probability of class j ; C is the number of categories According to above analysis for the P2P network node aggregation model is:

MAX J e J e  sb

 v11 , v12 , v13  v1k    v , v , v  v2 k    21 22 23 (2)      vn1 , vn 2 , vn3  vnk 

between

c

p s j 1

j wj

(4)

With larger J e , sb will become larger (community between discrete degree sb ), at the same time s w will become smaller the denominator (community of discrete degree s w ). The smaller the denominator show that communities in discrete degree is smaller, the internal nodes, the more densely populated communities, each community within the class node, the higher the similarity degree. Molecular shows that the greater the community, the greater the discrete between the major community, the more dispersed (the greater the difference), the smaller the similarity degree of different communities.

Conclusions Using the existing network nodes and road signs can gather n nodes for k community, the community k gathered in the node to the roadmap node delay metrics as a parameter to calculate, by considering the node delay parameters, it is with relatively consistent between the community and the physical network

36 Xiaoyan Gao

structure. When P2P network nodes want to join the community system, using our node aggregation algorithm to calculate the delay metrics encoding again, and then choose the appropriate community to join in, until find belongs to the existing set or create new communities, so aggregation algorithm is always effective.

Niu XZ, Zhou MT, She K. A cooperative sharing scheme for resources in mobile P2P networks. Chinese Journal of Electronics,2010,38(1):18‐24 .

Ou zhong hong, Song meina, Zhan xiaoshu. Key technologies of mobile peer‐to‐peer networks. Journal of software2008,19(2):404‐418. http://www.jos.org.cn/ Richard O. Duda Peter E. Hart David G.Stork. Li

Future Work

hongdong

Essay node aggregation algorithm can quickly gather for different communities in P2P network, so as to provide good topological structure for the application of P2P, but due to the change of the state of the network nodes as time changes, this can affect the stability of the network structure. Therefore, how to handle the effective management of nodes in communities is the next step research work.

Yao

tiaoxiang.

pattern

classification

[M]Publishing House of Mechanical Industry CITIC publishing house, 2012.10.

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Gao Xiaoyan, associate professor, Dr, major in Communication and information system . she was born in 1970, research direction include the computer network, the network security.

She has been a visiting scholar at Queenʹs University in canada. yanxiaogao@163.com. The Qinghai Province Natural Science Fund Project “complex system fuzzy cognitive map mining

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key technology research”(No. 2012‐Z‐932Q) grant. Hebei province science and technology research project “Research

on intelligent method system”(No.Z2014038) support.

of

the

interactive