Selective Flooding Based on Relevant Nearest-Neighbor using Query Feedback and Similarity across Unstructured Peer-to-Peer Networks

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1 Journal of Computer Scence 5 (3): , 009 ISSN Scence Publcatons Selectve Floodng Based on Relevant Nearest-Neghbor usng Query Feedback and Smlarty across Unstructured Peer-to-Peer Networks 1 Iskandar Ishak and Naome Salm 1 Department of Computer Scence, Faculty of Computer Scence and Informaton Technology, Unversty Putra Malaysa, UPM, Selangor, Malaysa Faculty of Computer Scence and Informaton System, Unversty Technology Malaysa, Skuda, Johor, Malaysa Abstract: Problem statement: Effcent searchng s a fundamental problem for unstructured peer to peer networks. Floodng requres a lot of resources n the network and thus wll ncrease the search cost. Searchng approach that utlzes mnmum network resources s requred to produce effcent searchng n the robust and dynamc peer-to-peer network. Approach: Ths study addressed the need for effcent flood-based searchng n unstructured peer-to-peer network by consderng the content of query and only selectng peers that were most related to the query gven. We used mnmum nformaton to perform effcent peer selecton by utlzng the past queres data and the query message. We exploted the nearest-neghbor concept on our query smlarty and query hts space metrcs for selectng the most relevant peers for effcent searchng. Results: As demonstrated by extensve smulatons, our searchng scheme acheved better retreval and low messages consumpton. Concluson: Ths study suggested that, n an unstructured peer-to-peer network, floodng that was based on the selecton of relevant peers, can mprove searchng effcency. Key words: Unstructured peer-to-peer searchng, nformaton retreval, nearest neghbor INTRODUCTION Peer-to-peer system has phenomenally become popular n recent years for ts ablty to search and communcate across the globe. The prncpal operatons n any peer-to-peer networks s to effcently search and locate data or fle, whch s ultmately challengng due to ts dynamc and robust nature. The challenges are to develop searchng effcently able o locate the fle ntended. The dynamc nature of the peer-to-peer network where peers can jon and leave at anytme make the searchng to become effcent s even dffcult. The demand for advance searchng technque s always present as the peer-to-peer networks becomng larger and more complex and the network become faster and storage become cheaper. In a peer-to-peer network, a peer acts as clent and a server of the system. Peer-to-peer presents attractve soluton through ts scalablty, fault-tolerance and autonomy. Many real-world large scale peer-to-peer networks are unstructured. However, n ther basc structure; peer-to-peer suffers hgh cost when dealng wth locatng content effcently due to use of prmtve searchng and routng technque that uses large overhead and long query tme. Therefore t s crucal to select relevant peers to route query message to reduce the search cost and better retreval n unstructured peerto-peer network wthout the loss of the unstructured peer-to-peer dentty and characterstcs. Unstructured peer-to-peer networks such as Gnutella [1,] rely on a random process, that peers are nterconnected n a random way. Typcally, unstructured peer-to-peer search s based on floodng. Basc unstructured networks whch apply floodng technque for propagatng user queres s very expensve both n processng tme and resources. Several studes have addressed the completeness and scalablty ssues of floodng [3,4]. Although flood-based search s generally consdered neffcent, a number of efforts have been done to mprove the searchng n unstructured peer to peer to become more effcent Structured peer-to-peer networks have been developed to provde strct data locaton management [5,6]. It uses dstrbuted hash tables that assst the routng mechansm to mantan desrable propertes for quck lookup. Structured approach offers better search performance n terms of response tme and communcaton overhead when compared to the Correspondng Author: Iskandar Ishak, Department of Computer Scence, Faculty of Computer Scence and Informaton Technology, Unversty Putra Malaysa, Malaysa 184

2 unstructured system. However, despte ts hghly effectve approach, the system lacks partal match lookup capablty. The system also ncur larger overhead than unstructured peer-to-peer systems especally when the overlay s re-arrange whenever there s falng peers or leavng peers. Ths study proposes relevant nearest neghbor based search technque that explots mnmal nformaton n each peer. The algorthm s formulated to acheve effcent search and hgh retreval rate n unstructured peer-to-peer networks. Related work: The earlest technque for peer-to-peer routng s based on the Naïve Breadth-Frst Search (BFS) algorthm or Floodng. Ths technque s used n fle-sharng peer-to-peer applcaton Gnutella [1]. In ths approach, each query from a peer wll be broadcasted to all the peers n the network but restrcted by the TTL (tme to lve) value. Floodng may generate O(N) message where N s the number of node. As a result, the query consumes a great deal of processng resources and excessve network. In a worst case stuaton such as low bandwdth network, floodng could make the network become a bottleneck. Although, t s a robust and smple technque for query routng but t nvolves a great deal of communcaton overhead, that s, hgh n number of messages. Hop count s also ncreased exponentally. Some of the messages mght vst the same node that has been searched prevously. Therefore, communcaton overhead and scalablty are the man problems n ths approach. These problems have been proven n a number of papers [,7,8]. In the random Breadth-Frst-Search (BFS) approach [9,10], each peer forwards a search message to only a fracton of ts peers. Each node randomly selects a subset of peers connected to t and then propagates the search message to those peers. The advantage of ths technque s t does not need any global knowledge. Each peer s able to make local decson n a quck manner snce t needs only small porton of connected peers to route the query. Ths approach may generate only a fracton of messages compared to floodng approach. In random walks [4] approach, the requestng peer sends out a number of query messages to an equal number of random neghborng peers. Each of the query messages wll follow ther own path n whch ntermedate peers wll forward the messages to randomly chosen neghbor. These query messages are known as walkers. The walkers wll be termnated on both success and falure occasons and determned through the use of TTL of the messages or through the use of checkng method n whch the walker perodcally J. Computer Sc., 5 (3): , contact the source peer whether the termnaton condton s met. It acheves sgnfcant message reducton when compared to the floodng approach. However, the success rate and the number of hts depend largely on the network topology and the random choces t made. Another unstructured peer-to-peer searchng approach s the Drected BFS combned wth the Most Result n the Past n [11]. Each peer forwards a search message to a number of peers whch returned the most results for the last most current queres. The nature of ths approach s t allows peers explore larger network segments and fnd most stable neghbors. A content-based searchng for peer-to-peer based system s proposed n [1]. In ths approach, each peer wll have a specal ndex called flters to facltate query routng only to those that may contan relevant nformaton. Each peer mantans one flter that summarzes all documents that exst locally n the peer, called local flters. A merged flters s the flter that summarzng the document of a set of ts neghbors. When a query reaches a peer, the peer wll check ts local flter and uses the merged flter to route the query to the peers whose flters match the query. Intellgent Search Mechansm [9] s proposed as a searchng technque based on the smlarty of the query. In ths approach, each peer has ts own profle table that stores the nformaton they get from peers that answered ther queres. The nformaton stored n Table 1 s the query ID, peer ID and the query keywords that have been answered and also the query ht. Only the latest peer that answered the query wll be kept nto a table. Routng s based on the smlarty values of the query word wth the keyword from the past queres stored n the profle. Peers that have hgh smlarty wth the query wll be selected for routng. Ant Colony optmzaton s also used n unstructured peer-to-peer search n [13]. The approach s called SemAnt where t emulates the nature of ants cooperatng between themselves to fnd food based on the pheromone. The peers are the ones who act lke an ant and cooperated between them n creatng pheromone trals. The pheromone trals s the probablstc overlay networks and also ndcates the most promsng path for a gven query. As a result, the more popular a query becomes, the better the tral. The experments shown that the search algorthm s stable, robust and converges fast whole ts performance s pretty much acceptable. Table 1: Profle table Query ID Connecton and hts Tmestamps Amazon ran forest 1311 P(34), P34(), P5(56) 1011 Gulf ol rgs Null 10 Waste dsposal P4(34), P8(4) 1033

3 J. Computer Sc., 5 (3): , 009 MATERIALS AND METHODS Relevance-Nearest Neghbor based search (RNN) usng query feedback and smlarty: Our search approach consst of 3 components; profle table, relevance peers estmaton and nearest neghbor selecton. Profle table s used to store past query message and query hts from other peers who have answered prevous queres. The Relevance Nearest Neghbor based Search or RNN s based on the concept of gvng the same weght both on the query hts and query content smlarty wth the ncomng query and selectng only the nearest relevant peers. We also nclude the nearest neghbor approach to mnmze the search cost but at the same tme able to retreve hgh query hts or recall. The search method s bascally a floodng based search but s based on selectve floodng. The search algorthm s shown n Fg. 1. The objectve of ths searchng approach s to have an effcent search and hgh recall. For a peer p P, we use T(p) to denote set of past query mantaned by p. Each tem n T(p) has two attrbutes; query term, q and number of hts n, so we denote each data tem n T(p) as a par of q(p), n(p). By usng each q and n on T(p), we determne the relevance of a peer by usng the smlarty of all q(p) n T(p) to calculate the smlarty between them. On the other hand, we use n(p) to calculate the stablty of each peer n T(p). We defne a reference pont, whch s the hghest or optmum value of query smlarty and also the hghest query hts denoted as d pont. Relevance peer estmaton: The relevance-based component s based on the work n [14]. Ths component uses the peer smlarty-hts graph model. Our peer-smlarty graph model captures both the peer query hts and peer smlarty wth correspondng ncomng query (Fg. ). We used both nformaton gathered n the profle table whch s based on the work done n [9]. We ncorporated both, query content and connecton stablty nformaton to determne relevant peer to route query. Each peer stores nformaton about past queres and the query hts n a table. There wll be no global knowledge shared between all the peers but each peer wll also have a lst of data collected from the answered query and store t n neghbor profle table (Table 1). The profle table contans the ID of the answerng peer, connecton ID, the query words that have been answered by other peers and a tmestamp of the returned query. These query words are the words that matched the query sent by ths peer and the words are contaned n the peer are only answered query words. The lst wll keep the last M queres and a Least Recently Used (LRU) polcy wll keep the most recent queres n the table. The relevance value wll be based on two parameters, query hts and the smlarty value between the query to be routed and the stored past queres. Query hts determne peer connecton stablty wth the processng peers. The more query hts, the more stable the peer s connected and thus gvng the mpresson of the partcular peers connecton relablty. Smlarty value s based on cosne smlarty (1). q s the ncomng query whle q s the past queres stored n the profle table n each peer. As an example, let peer A has a lst of past queres; d. Query q s an ncomng query and s watng to be routed q wll be compared wth all the queres, q, n d. The smlarty between them wll determne the relaton between the content that the partcular peer has n ts storage wth the query terms. The most relevance of peers s peers that have among the most query hts and t has a content related to the ncomng query. Fg. 1: Relevance Nearest Neghbor Search algorthm that uses the Selectve floodng concept Fg. : Smlarty and Query hts metrc space 186

4 J. Computer Sc., 5 (3): , 009 Fg. 4: Doublng the normal search radus Fg. 3: Pont of reference estmaton We calculate the relevance value usng formula descrbed n (3). In ths formula, we are actually calculatng the dstance of relevance value of the peers nsde the profle table wth the most optmum relevance pont n the smlarty-query hts graph. M s the maxmum cosne value, n whch for the purpose of easy calculaton, we decded to defne M = 1. h s the returned hts values for a partcular query, whle H p s the maxmum hts retreved from all h that have been recorded. The formula to defne the maxmum hts (), nvolved the use of nearest-neghbor concept, whch wll be explaned later. N p s the total number of query hts of all peers stored n the neghbor profle table: Fg. 5 Nearest-neghbor search space RESULTS sm(q,q ) = p ( q * q ) (q) * (q ) (1) H = (h ) () H h p R(q,q ) = + M sm(q,q ) N p ( ) (3) Nearest neghbor selecton: We determne our group of peers wthn the relevance value by usng the nearest neghbor prncpal. It s based on the fast algorthm for nearest neghbor search proposed n [15]. The purpose of the applcaton of nearest neghbor method s to avod comparng all the peers nsde the table because table wth sze of N wll requre N tmes comparson and relevance calculaton process. As we can see n Fg. 3, nearest neghbors n our context are the peers that resdes wthn the area of radus r. However, nstead of selectng a statc value [14], we decded to make the value r dynamc. We determned the dynamc r value by explotng the nequalty of trangle (Fg. 4). The nequaltes wll determne a bound for peer selectons and therefore, less relevance values wll be compared to the dstance r (Fg. 5). 187 We evaluate the performance of our searchng approach by extendng a peer-to-peer smulator called Peerware. We generate 100 peers wth a total of documents. Each node holds random number of documents between documents. The document collecton used s the Reuters-1578 document collecton whch appeared on the Reuters newswre n Three dfferent number of query set are used; q100 that contan 100 random query terms; q75 contans 75 queres and q50 wth 50 query terms. Each query terms contan between two to fve words. Each peer s country-based and each peer holds news about that partcular country. One country could have more than one peer representatves n the network. We compare our approach wth the Most-Query Hts (MQH) and Intellgent Search Mechansm (ISM) approaches. We compare the search approaches based on query recalls, number of messages used and search effcency. s are the number of query matches wth the content of each peer, whle number of messages used s the number of messages used to answer a query. Search effcency s the performance evaluaton parameter that s calculated by dvdng recalls wth number of messages used: Search effcency = (4)

5 J. Computer Sc., 5 (3): , 009 DISCUSSION Our experments showed that our approach (RNN) s effcent n terms of network usage compared to the other two searchng technques. The experments that employed q50 showed that our searchng approach recorded the hghest recall when compared to the ISM and MQH technques. The recall s 4.87% hgher compared to the MQH approach and slghtly over than ISM approach wth 1.67% more than ISM recall (Fg. 6). Fgure 7 shows the message usage for all three approaches for query set q50 n whch our approach recorded the hghest number of message usage than MQH and ISM (4.79 and.1% respectvely). However, we recorded hghest effcency wth 38.1% hgher than MQH and 8.8% effcency hgher than ISM (Fg. 8). RNN also recorded hghest recall when employng the q75 query set n the experment, our approach stll managed to record hghest recall; as shown n Fg. 9. The RNN search recorded 4.31% hgher recall than MQH and 0.85% hgher recall than ISM. The RNN recorded 9.4% messages hgher than MQH but t yeld better search effcency wth 16.5% Fg. 6: recorded for 50 queres searchng RNN also recorded hgher number of messages used than the ISM wth 5.1% hgher but the RNN approach regstered 0.6% hgher search effcency than ISM. and search effcency graph for experment usng the query set q75 are shown n Fg. 10 and 11 respectvely. Our experment usng q100 query set also showed that RNN approach recorded hgh recall than other search technque. As shown n Fg. 1, RNN recorded 1.67% hgher recall than MQH approach and 0.% hgher recall than ISM. In terms of message usage, Search effcency Fg. 8: Search Effcency for 50 queres searchng Fg. 9: recorded for 75 queres searchng MQH ISM Relevant (NN) Fg. 7: for 50 queres searchng Fg. 10: for 75 queres searchng 188

6 J. Computer Sc., 5 (3): , 009 Search effcency Search effcency MQH ISM Relevant (NN) 0.03 Fg. 11: Search effcency for 75 queres searchng MQH ISM R elevant (NN) Fg. 1: recorded for 100 queres searchng Fg. 13: for 100 queres searchng we can show n Fg. 13 that RNN approach recorded 3.01% less message than MQH and 8.5% messages less than ISM approach. In the same experment settng, we found out that RNN search s the most effcent wth 13.51% more effcent than MQH and 17.95% more effcent than ISM (Fg. 14). Fg. 14: Search Effcency for 100 queres searchng CONCLUSION Ths study explots the query content and query feedback data for developng flood-based search n unstructured peer-to-peer that s mnmal n cost but gvng hgh retreval. RNN explots very mnmal data and also nearest-neghbor concept to reduce the cost of searchng n unstructured peer-to-peer networks. Our smulaton tests showed that our searchng approach performs better than the other two flood-based searchng approaches that also use mnmal data and local ndces. We showed that by usng mnmal nformaton of query hts and smlarty, effcent search n unstructured peer-to-peer can be acheved ACKNOWLEDGEMENT The researchers would lke to express ther cordal thanks to Demetrs Zenalpour for gvng the permsson to use the full data set of Reuters-1578 document collecton and the Peerware smulator. REFERENCES 1. Grossel, Y. and R. Manfred, 009. Gtk-Gnutella. Rpeanu, M., 001. Peer-to-peer archtecture case study: Gnutella network. Proceedng of the 1st Internatonal Conference on Peer-to-Peer Computng, Aug. 7-9, IEEE Xplore Press, USA., pp: DOI: /PP Cohen, E. and S. Shenker, 00, Replcaton strateges n unstructured peer-to-peer networks. Proceedngs of the 00 Conference on Applcatons, Technologes, Archtectures and Protocols for Computer Communcatons, Aug. 19-3, ACM Press, New York, USA., pp:

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