A Fuzzy Intra-Clustering Approach for Load Balancing in Peer-to-Peer System

Transcription

1 ISSN , England, UK Journal of Informaton and Computng Scence Vol. 7, No. 1, 2012, pp A Fuzzy Intra-Clusterng Approach for Load Balancng n Peer-to-Peer System Rupal Bhardwa 1, V.S.Dxt 2, Anl Upadhyay 3 1 Department of Master of Computer Applcatons, Aay Kumar Garg Engneerng College, Ghazabad, UP, Inda 2 Department of Computer Scence, Atma Ram Sanatan Dharma College, Delh Unersty, New Delh, Inda 3 Department of Appled Mathematcs & Scence, Bpn Chnadra Trpath Kumaon Engneerng College, Dwarahat, Almora, Inda (Receed October 23, 2011, accepted December 2, 2011) Abstract. Load balancng algorthms goal s to eep all nodes normally loaded through mgraton of modules from heay loaded nodes to lghtly loaded nodes. In addton, load balancng must nole low communcaton oerhead and respond qucly to load mbalance n the system. In preous load balancng algorthms, classfcaton of node as heay, lght or normal loaded node s done by usng concept of threshold leel, whch s fxed and predefned. Now, today scenaro s to change the status of nodes dynamcally accordng to the state of system. So that, n ths paper we proposed an algorthm for load balancng usng fuzzy clusterng; whch mproes the performance of the system wthout pre-defnng the threshold alues. The proposed algorthm s compared wth other exstng algorthms and s found to be fast and effcent n reducng load mbalance n Peer to Peer system. Keywords: heay weghted, lght weghted, fuzzy system, clusterng, load balancng 1. Introducton A Peer to Peer (P2P) system n whch eery partcpatng node acts both as a clent and as a seer (serent) and share a part of ther own hardware resources such as processng power, storage capacty or networ bandwdth. A P2P system wll hae a number of peers (nodes) worng ndependently wth each other. Each node s classfed ether as heay or lght weghted node usng fxed threshold leel. The use of sngle-threshold alue may lead to a useless load transfers and mae the load balancng algorthm unstable because a node s status may be lght weghted when t decdes to accept a remote process, but t status may be heay weghted wheneer the remote process arres. Therefore, a lght weghted node becomes heay weghted node and wll agan noe load balancng algorthm to change ts status. (Alonso and Coa, 1988) proposed a double-threshold leels algorthm to reduce the nstablty of the sngle-threshold leel polcy ; howeer those two threshold leels are fxed and predefned. One desred feature of load balancng algorthm s change the status of nodes dynamcally. Some load balancng algorthms (A Rao & Stoca, 2003; G. Sharatr & Snghal,1992; Raee Gupta & Prabha Gopnath,1990; Sonesh Surana & I Stoca, 2004) use fxed threshold leels for load balancng. Load balancng usng fuzzy system s a natural extenson of double threshold leel approach and t mproes the performance wthout pre-defnng the threshold alues. The remander of the paper s organzed as follows: Secton 2 brefly reews fuzzy logc controller and clusterng. The proposed algorthm usng fuzzy clusterng s presented n secton 3. Secton 4 & 5 descrbes exstng load balancng algorthms such as Round Robn and BID algorthm and expermental study. Fnally, we conclude the paper n secton Bacground 2.1. Fuzzy Logc Controller Fuzzy logc s a powerful mathematcal tool n representng lngustc nformaton and s ery useful to sole problems that do not hae a precse soluton. The archtecture of the fuzzy logc controller s shown n fgure 1, t ncludes four components: Fuzzfer, Inference Engne, Fuzzy nowledge rule base and Publshed by World Academc Press, World Academc Unon

2 20 Rupal Bhardwa, et al: A Fuzzy Intra-Clusterng Approach for Load Balancng n Peer-to-Peer System Defuzzfer (A. Karm & Sarpan, 2009; Ally E. EI-Bad, 2002; M.C.Huang &Varaan, 2003 ; T. J. Ross, 1995) Fuzzfcaton It conerts the crsp nput alue to a lngustc arable usng the membershp functons stored n the fuzzy nowledge base. Fuzzy nowledge rule base It contans the nowledge on the applcaton doman and the goals of control. The nference engne It apples the nference mechansm to the set of rules n the fuzzy rule base to produce a fuzzy set output. Defuzzfcaton It conerts the fuzzy output of the nference engne to crsp alue usng membershp functons smlar to the ones used by the fuzzfer. Fuzzy Knowledge Base Input Fuzzfer Inference Engne Defuzzfer Output Fgure 1: Archtecture of fuzzy logc controller 2.2. Clusterng Clusterng s a tool for data analyss, whch soles classfcaton problems. Its mote s to dstrbute cases (people, obects, eents etc.) nto groups, so that the degree of assocaton to be strong between obects of the same cluster and wea between obects of dfferent clusters (Clar F. Olson,1996).Cluster analyss organzes data by abstractng underlyng structure ether as a groupng of ndduals or as a herarchy of groups. In bref, cluster analyss groups data obects nto clusters such that obects belongng to the same cluster are smlar, whle those belongng to dfferent clusters are dssmlar. A cluster s therefore a collecton of obects whch has smlarty between them and has dssmlarty to the obects belongng to other clusters. We can show ths wth a smple graphcal example (Osmar R. Zaïane,1999). Fgure 2: organzng obects nto groups whose members are smlar n some way Clusters are formed by two methods- dstance based clusterng, two or more obects belong to a gen dstance (n ths case geometrcal dstance). Another nd of method s conceptual clusterng, two or more obects belong to the same cluster f ths one defnes a concept common to all that obects. In other words, obects are grouped accordng to ther ft to descrpte concepts. 3. Fuzzy-Clusterng Load Balancng Approach Assumng the poll of dynamc cluster heads n herarchcal method, based on dynamc cluster node; dynamc cluster heads are constructed by usng the terms n node and cluster heads wll change when dfferent cluster nodes are merged. The degree of smlarty between node clusters s calculated based on these dynamc node cluster heads. Dynamc clusters are formed by bascally two thngs- (1) Smlarty between two data method. JIC emal for contrbuton: [email protected]

3 Journal of Informaton and Computng Scence, Vol. 7 (2012) No. 1, pp (2) Dstance from data to cluster head. Weght w of term t n node n s calculated by usng the followng formula gen by (Salton G., 1971). w = tf max tf N where INF = log10, N s total number of nodes, n denotes the number of nodes whch contan term t and INF (1) INF denotes the nerse node frequency of termt, n tf denotes the frequency of term t appearng n node n. Calculate fuzzy degree of node wth help of equaton 2, of term t wth respect to node n based on nerse node frequency factor INF, of termt, where [0, 1]. INF, ag denotes the mean alue of the terms n node n and INF, max denotes the maxmum INF alues of the terms n node n. = INF INF,max,max INF INF,mn, ag Calculate the relate tme to le wth respect to eery term t n node n, where where I I =, max,mn [0, 1], denotes the degree of effect of term t n node n and, max (2) (3) denotes the maxmum degree of effect of the terms n node n. Calculate the degree of smlarty between two node clusters c and c, s denotes the number of terms appearng n both node clusters c and c. where s ( c, c ) [0, 1] s ( c, c ) = N = 1,2,3,... s N = 1,2,3,..., s Mn( Max( Now, clusters are organzed as dynamc herarchcal clusters (Wtold,2005) and cluster loads are categorzed as ery lght, lght, heay and ery heay cluster. Number of node Table 1: Membershp Functon,, VLN LN HN VHN ) ) (4) JIC emal for subscrpton: [email protected]

4 22 Rupal Bhardwa, et al: A Fuzzy Intra-Clusterng Approach for Load Balancng n Peer-to-Peer System Apply the nference rules accordng to Table 1- ( receer) max = ( VLN ) ( LN ) ( sender) mn = ( HN ) ( VHN ) Load transfer table s presented n table2, assumng sender ntated load balancng algorthm, and proposed rule base s as follows- Rule 1: If (cluster_load s ery_lght) then (cluster s receer) Rule 2: If (cluster_load s lght) then (cluster s receer) Rule 3: If (cluster_load s moderate) then (cluster s normal) Rule 4: If (cluster_load s heay) then (cluster s sender) Rule 5: If (cluster_load s ery_heay) then (cluster s sender) Table 2: Load Transfer Table Operaton Notaton Expresson Moderate=NOT(ML) m (x) {1- m (x) } Sender=HN OR VHN (x) Max{ ( x), ( x)} HN VHN Receer=VLN AND LN (x) Mn{ ( x), ( x)} 4. Exstng Reference Algorthms VLN LN We hae used two algorthms, whch are releant to our context, as reference algorthms to compare the result of our algorthm Round Robn Algorthm In the round robn algorthm (Pradeep K. & Snha, 1996), processes are equally dded among all processors. Each new process s assgned to a new processor n a round robn fashon. Wheneer number of processes larger than number of processors, round robn algorthm wors well. No need of nter-process communcaton n case of round robn algorthm BID Algorthm In the bddng algorthm (Z. Xu & Haang, 2008), when a node s heay loaded, t multcasts a request for bds to the other nodes n the system. After collecton of all bds by heay loaded node, the best bd s chosen as lght weghted node. If none of the node s found n the group for load transfer, the bddng procedure starts oer agan. 5. Expermental Study We use MATLAB to ealuate our load balancng algorthm. We show Compare the performance of our proposed algorthm wth the other algorthms dscussed n secton 4. Compare the performance of sender node s. receer node usng fuzzy expert system. The effect of node falure, concludng that nodes transmsson s more stable wth fuzzy expert system as compare to non fuzzy system. The effect of throughput, concludng that throughput s better n proposed algorthm as compare to BID algorthm Performance Comparson Fgure 3 captures the tradeoff between number of nodes and total executon tme. Each pont on the lower lne corresponds to the effects of our proposed algorthm. Ths obseraton ndcates that performance of our algorthm s far better than exstng algorthms n secton 4. HN VLN VHN LN JIC emal for contrbuton: [email protected]

5 Journal of Informaton and Computng Scence, Vol. 7 (2012) No. 1, pp Proposed BID Round Robn Sender Receer Total Executon Tme Total Executon Tme Number of Nodes Fgure 3: Total executon tme s. number of nodes Utlzaton Fgure 4: Total executon tme s. utlzaton 5.2. Performance comparson of sender s. receer node In ths secton, we wll ealuate the performance of sender node wth receer node usng fuzzy expert system. Ths obseraton (fgure 4) ndcates that sender node executon tme s less as compare to receer node, means performance of sender ntated load balancng algorthm usng fuzzy expert system s better than receer ntated load balancng algorthm Effect of Node Falure As we hae already mentoned that n case of node falure, nodes transmsson s more stable usng fuzzy expert system as shown n fgure 5. The arous Parameter alues used for smulaton are shown n Table 3. Table 3 Parameter Values Parameter Value Number of nodes 50 Surface 20m 20m Transmsson range 15m Data transmsson rate Falure model Sze of node 15 node/sec Random 128 bytes Fuzzy Nonfuzzy Proposed BID Transmsson Rate Throughput Falure Node Fgure 5: Transmsson rate s. falure node Tme Fgure 6: Throughput 5.4. Throughput Ths obseraton (fgure 6) ndcates that throughput s more n case of proposed algorthm as compare to JIC emal for subscrpton: [email protected]

6 24 Rupal Bhardwa, et al: A Fuzzy Intra-Clusterng Approach for Load Balancng n Peer-to-Peer System BID algorthm, means throughput of load balancng algorthm usng fuzzy expert system s better than BID algorthm. 6. Concluson A noel approach, to sole the problem of dynamc load balancng n P2P system usng fuzzy clusterng, s deeloped. Nodes are dded nto clusters based on ther membershp functon alues. We hae compared three load balancng algorthms: BID, round-robn and the proposed one. The smulaton results show the proposed algorthm s more effcent and flexble than exstng algorthms n terms of executon tme and throughput for arous number of nodes. The future wor wll be mprong the performance of proposed algorthm by cluster parttonng. 7. References [1] A.. Karm, F.Zarafshan, A.b.Jantan, A.Raml, M.Iqbal b. Sarpan. A New Fuzzy Approach for Dynamc Load Balancng algorthm. Internatonal Journal of Computer Scence and Informaton Securty. 2009, 6(1). [2] Ally E. EI-Bad. Load Balancng n Dstrbuted Computng Systems Usng Fuzzy Expert Systems.TCSET L-Slaso, Urane, [3] Alonso, R. and Coa, L. L. Sharng Jobs Among Independently Owned Processors. In Proceedng of the 8 th Internatonal Conference on Dstrbuted Computng Systems. NewYor: IEEE. 1988, pp [4] Ananth Rao, Karth Lashmnarayanan, Sonesh Surana, Rchard Karp and Ion Stoca. Load Balancng n Structured P2P systems. In 2 nd Internatonal Worshop on Peer to Peer Systems (IPTS), [5] Clar F. Olson. Parallel algorthms for herarchcal clusterng. Techncal report. Unersty of Calforna at Bereley, [6] M.C.Huang, S.H.Hossen, K.Varaan. A Receer Intated Load Balancng Method In Computer Networs Usng Fuzzy Logc Control. GLOBECOM 2003, /03. (2003) [7] Iranan G. Sharatr, Phlp Krueger and Muesh Snghal. Load Dstrbutng for locally dstrbuted System. IEEE , pp [8] Nranan G. Sharatr, Phlp Krueger. Two adapte locaton polces for global schedulng algorthms. IEEE , pp [9] Osmar R. Zaïane. Prncples of Knowledge Dscoery n Database. [10] Pedrycz Wtold. Knowledge-Based Clusterng. ISBN John Wley & Sons, Inc [11] Pradeep K. and Snha. Dstrbuted Operatng Systems Concepts and Desgn. IEEE [12] Raee Gupta, Prabha Gopnath. A Herarchcal approach to Load Balancng n Dstrbuted Systems. IEEE , pp [13] Salton G. The Smart Retreal System-Experments n Automatc Document Processng. Englewood Clffs, NJ, USA: Prentce Hall, [14] Sonesh Surana, B. Godfrey. K. Lashmnaraynan, R. Karp, I Stoca. Load Balancng n dynamc structured Peer to Peer system. IEEE , pp [15] Tmothy J. Ross. Fuzzy Logc wth Engneerng Applcatons. McGraw Hll, [16] Z. Xu, R. Haang. Performance Study of Load Balancng Algorthms n Dstrbuted Web Serer System JIC emal for contrbuton: [email protected]