Study of Cloud Services Recommendation Model Based on Chord Ring

Transcription

1 Internatona Journa of New Technoogy and Research (IJNTR) ISSN: Voue-2 Issue-2 February 2016 Pages Study of Coud Servces Recoendaton Mode Based on Chord Rng Chen L Qng-Tao Wu Jng Chen Abstract The advent of coud coputng era the aount of appcaton data ncreasng sharpy To sove the probe that the cobnaton of personazed recoendaton technoogy and coud coputng whch facng proongng the recoended te deay and on the arge of network overhead a coud servces recoendaton ode based on chord rng s proposed. In ths ode coud-based dstrbuted storage ode nto Chord rng Sort recoendaton process a greater pact on servce etadata coecton syste to ensure quck retreva of canddate recoendaton servce sets; proposed recoendaton agorth based on weghted bpartte graph on ths bass predct the target user top-k set of recoendatons. Experenta resuts show that the echans can effectvey prove the recoendaton accuracy and recoend effcency. Index Ters coud servce Chord rng personazaton recoendaton weghted bpartte network. I. INTRODUCTION Hghght wth the rapd deveopent of Internet technoogy onne data ncreasng rapdy on the one hand peope coud exchange nforaton and share data reans wthn doors on the other hand wth the sharp ncreasng of network nforaton users are dffcut to qucky fnd vauabe nforaton fro these vast aounts of nforaton so the rate of nforaton utzaton decne whch brngs the probe of nforaton overoad [1]. Personazed recoendaton whch as an portant nforaton fterng easure actvey recoends to users wth ts potenta nterestng n the te by anayzng the nterests and hstorca behavors of users whch effectvey sove the probe of Internet nforaton overoadng [2]. The recoended resuts of personazed recoendaton syste are coser to the users ndvdua needs whch s dfferent fro the one to any server offered by search engnes (that s search engne presents the sae search resuts to a users whch cannot provde the correspondng servces by dfferent users' nterests [3]). Personazed recoendaton deveops rapdy n recent years. A copete recoend syste conssts of three coponents: recordng odue to coect user behavor nforaton anayss odue to anayss potenta nterests of Chen L coputer appcaton technoogy Coege of Inforaton Engneerng Henan Unversty of Scence and Technoogy Luoyang Chna Qng-Tao Wu nforaton safety Coege of Inforaton Engneerng Henan Unversty of Scence and Technoogy Luoyang Chna. Jng Chen coputer appcaton technoogy Coege of Inforaton Engneerng Henan Unversty of Scence and Technoogy Luoyang Chna. user and recoendaton agorth odue to rea-te fter the nforaton whch s user nterests fro target coecton. In whch the recoendaton agorth odue s the core of the recoendaton syste [4]. Personazed recoendaton syste s any dvded nto coaboratve fterng recoendaton syste recoendaton syste based on content xture recoendaton syste and recoendaton syste based on network structure of user-product accordng to dfferent recoendaton agorths. In whch network structure recoendaton agorth of the user- proect bpartte network receved extensve attenton of researchers [5]. Bpartte network as a knd of speca network the prncpe of personazed recoendaton for users s usng the process of copex network dynacs such as substance dffuson heat conducton on bpartte network etc. Bpartte network s the spe heterogeneous nforaton network whch contans ony two types of nodes edges ony exsts between heterogeneous nodes. Recoendaton agorth based on bpartte network does not consder the content features of user and proect the nforaton used n a agorths are hdden n the seecton reatonshp between the user and proect [6] so bpartte network recoendaton agorth n coud coputng envronent wde appcaton prospect. On the other hand coud servce provders n order to prove the parae coputatona effcency constructs the structure wthout a frae of server custer [7] each node n the server custer are ndependent of the oca storage data s dstrbuted storage to each node parae processng the sae user score accordng to aso be dspersed storage n dfferent nodes so n the process of recoendaton and need to retreve data fro each storage node whch w ead to the probe of hgh deay[8].coud servce provders need frequent updates the user of servce evauaton data set so t w ead to nter node data transsson rate of ncrease ncreasng the network overhead. In terature [9] Aggarwa was frst proposed recoendaton agorth based on network structure (bpartte network) n KDD 99. In terature [10] Huang etc. proposed a network dagra recoendaton agorth based on the aocaton of resources. Usng product bpartte network to bud product correaton and put forward recoendaton agorth of bpartte network and open up a new drecton of researchng recoendaton agorth. In terature [11] Zhou etc. proposed network recoendaton agorth (Network-Based Inference NBI) usng bpartte network to aocate resource whch better resuts than coaboratve fterng agorth CF but n ths paper 1

2 Study of Coud Servces Recoendaton Mode Based on Chord Rng bpartte network no weghted resource aocaton s eveny dstrbuted aong proects wthout consderng the probe of edge and weght. In terature [12] I.S.Dhon was frst apped spectra custerng to vocabuary-docuent bpartte network the agorth s a custerng agorth based on graph parttonng whch regard the probe of custerng as a utpe segentaton probe on undrected graph ts essence s usng egenvaue and egenvector of Lapacan atrx as a too of descrbng the connectvty of graph. Tvet etc. proposed foodng strategy recoendaton agorth the target user transts score vector to cacuate canddate servce set the agorth w have a huge network overhead [13]. Ths paper proposes coud servces recoendaton ode based on chord rng to prove the recoendaton accuracy and recoend effcency. The an work ncudes the foowng two aspects: adoptng chord rng ode to prove the speed of retrevng servce canddate sets optzng bpartte network recoendaton agorth to prove the recoendaton accuracy. II. CLOUD SERVICES RECOMMENDATION MODEL A. Recoendaton ode based on Chord rng Coud servce recoendaton ode based on chord rng s dvded nto 4 ayers whch are basc data ayer data processng ayer servce recoendaton ayer access ayer. As shown n the fgure 1 beow. n whch each chord rng a sae servce eeent attrbute. 3) Recoender syste ayer: Responsbe for the correspondng recoendaton request trggered by users cacuatng the sarty between servce unt wth the sae attrbutes predctng servce unt ratngs of users and generatng recoendaton sets. 4) User access ayer: Accordng to the recoendaton resut the end-users seect the servce resources and extract deand ode of each attrbute n servce resources. B. Coud servce organzaton structure Accordng to the functon and attrbute of the coud servce the servce resource s organzed by the Hash functon based on SHA-1 whch s shown n fgure 2. The dfferent attrbutes of the servce resource s generated the correspondng attrbute based on the h functon. Servce resources by SHA-1 operatons on ther own property and get an M bt fag nuber denoted by ID gven the foowng correaton defnton: Defnton 1(Sub Chord Rng) Accordng to the servce of each attrbute tag and the sze of the ID order to servce for a ogca rng that s the sub Chord rng. Defnton 2(Man Chord Rng) A rng wth the servces ncudng hghest reputaton on ths attrbute n a sub chord rng. header... H... Master servce unt ndex Retreve the acqured servce unt vrtua set H 1 search H 4 search H 2 H 3 atch the coud path densons 1 recoendaton syste Deand 1 Deand 2 densons 2... denson n servce unt ndex servce unt ode user deandng ode Deand ndex atchng... Deand... Deand n atch the coud path Fgure 1: The coud servce recoendaton ode based on chord rng 1) Data pre-processng ayer: To data pre-processng on the need of the recoended target set that s when user recoendaton servce resources server custer retreved each storage node then found out a seected hstory servce unt by users whch provde support to 2). 2) Data processng ayer: Searchng the sae attrbutes of each storage node n server custer accordng to the servce resource attrbutes generatng soe chord rngs Fgure 2: Servce Natura Vrtua Organzaton Structure Usng dynac the stronger custerng accordng to the dfferent attrbutes of servce resources whch reazng that apped dstrbuted servce resources nto correspondng propertes of the vrtua coecton. In whch the stronger servce resources refers to servce resources wth the hghest score n the sae cass. Then represent the generated correspondng vrtua coectons n the for of a "rng". Accordng to the dfferent attrbutes of servce resources to generate a seres of sub-rng and a the sub-rngs ake "stronger" as head node to bud a ore advanced an rng. When searched servce resource searchng fro the an rng to a the sub-rng generatng a vrtua servce unt set D that confor the requreents of a search servce. III. RECOMMENDATION ALGORITHM BASED ON WEIGHTED BIPARTITE NETWORK In ths paper Q represents user requreents U represents a users u represents ndvdua users. Data processng ayer retreves storage nodes on each based on the needs of users whch uses D to represent t. In coud coputng envronent whch contans assve servces descrbng 2

3 Internatona Journa of New Technoogy and Research (IJNTR) ISSN: Voue-2 Issue-2 February 2016 Pages G ( U Q D E) servces recoendaton as there are nks between user requreents and servce unt n soe way to abstract nto edge set E. Therefore the fundaenta probe of the nforaton retreva can be expressed as the process of user u sendng a query of Q to seek whch eet the coud servces of user deand. A. The Basc Thought of Bpartte Network Recoendaton Agorth If there are ore than one type of nodes or nks n nforaton network whch caed heterogeneous nforaton network. Further dvdng heterogeneous nforaton network nto spe heterogeneous nforaton networks and copex heterogeneous nforaton network. Spe heterogeneous nforaton network any contan certan network dagras such as bpartte network trpartte network doube-type nforaton network and star nforaton network and so on. In coud envronent vast and heterogeneous coud servce resources fro dfferent suppers s of varous QoS requreents can be regard as a heterogeneous nforaton network [14]. Bpartte network aso caed bnary network whch s the ost spe heterogeneous nforaton network. Edge exsts ony between heterogeneous nodes and there no edge n hoogeneous nodes. Many appcatons n nforaton retreva can be abstracted as a bpartte network such as cck reatonshp between query and docuent the reatonshp between author and pubsh artces arked reatons between ages and abes and so on. The tradtona bpartte network s no weghted network that s n the process of aocatng resources aong proects aocatng proect resource eveny to users whe users aocatng the aocated resources to proect. Ths agorth ony deterne whether the user chosen the proect or not whch no consderaton n browsng but no buyng n whch aso contans the tendency of the ntendng to buy of the user whch ay cause the oss of nforaton. Therefore n practce the weght of edges between user and proect pays an portant roe. Introducng weght to vaue weght v of the edge of user-proect f user y seected proect x v 1 f user y browsed but no bought v n whch the vaue of between 0 and 1 f user y nether browsed nor bought v 0. Any proect aocatng resource to proect s by a the edges connected to protect and to aocate the weght cacuaton forua s aa w (1) 1 k( y ) k( x ) In whch k ( y ) represents as the degree of user y that s whch s the nuber of proects connected to user y k ( x ) represents as the degree of proect x that swhch s the nuber of users connected to proect x a s the vaue of th row th coun n the adacency atrx A ( ) of n represents as the weght a n between proect x y n bpartte network. x to not rated Cacuatng grade v forua of target user proect 0 a 1 y s as foows: x y E x y E others no browsed browsed ths ths proect proect Cacuatng a the not rated proects for a target user n ths paper t s not feedback drecty the proect whch hgher a vaue r to user but save and as canddate servce set S. As shown n forua (6). 1 (2) f ( o ) w a (3) B. Effcent coud retreva based on Chord rng Ths paper uses peer-to-peer retreva ethod of Chord protoco desgns quck resource ocate ethod based on Chord Chord chooses SHA-1 as h functon to ensure repeatabty of the h. SHA-1 produces the space of 2160 each te s bg nteger wth 16 bytes (160bt). We can thnk these ntegers end-to-end to for a rng whch caed the Chord rng. Each node n the Chord rng antans a Chord routng tabe whch coud ocate data bock resources ore convenenty and then reduce the resource search te. Each routng tabe tes. The routng tabe structure of node n n the Chord as shown n tabe 1. Tabe 1 Chord routng tabe structures Expresson Defnton fnger[ ]. start 1 ( 2 )od2 1 n fnger [ ]. nterva [ fnger[ ]. start fnger[ 1]. start] fnger[ ]. node The frst node s greater than fnger[ ]. start successor predecessor Successor of node n Precursor of node n To seek the successor of node k whe there no key vaue of node k n the node n routng tabe then perforng the foowng actons the node n seects node whch nearest k then fnd the node ore coser k node by node. By repeatng the operaton we can ocate the needed resources fnay. The agorth 1 gves correspondng pseudo-code of storng nodes resource ocaton. Ths agorth udges whether node n s the precursor of k. It not fnd the requred resources n routng tabe then fnd node n' whch nearest the node wth target resources and repeat the agorth by node n' unt fnd the target resources. Fro the structure of Chord routng tabe and storage nodes resources ocate agorth we can fnd that every turn t fnd the nearest node the dstance between new node and resource obect usuay ess haf than the orgna. In genera t coud be successfu postonng ess than ogn tes thus t coud acceerate data bock postonng of the storage node. Agorth 1 resource ocaton agorth 1. n.fndsuccessor(k) 2. f (k (nn.successor ] ) 3. return n.successor; 4. end f 5. for (=;>1;--) 6. f (fnger[].node n (nk)) 3

4 Study of Coud Servces Recoendaton Mode Based on Chord Rng 7. return fnger[].node; 8. end f 9. end for 10. n ' =n; 11. return n '.fndsuccessor(k); When =4 n=2 k=14 fnd the exape dagra shown n fgure 3: 节 点 为 10 的 路 由 表 : start node Fgure 3: Map of resource ocaton C. Agorth descrpton 节 点 为 2 的 路 由 表 : start node The specfc steps of coud servce recoendaton agorth based on Chord rng are as foows: Step 1: Cacuatng the canddate set. Input: Score atrx R and target user u of users and servce unts. Output: The set of u a s the st of recoended for seectng proects. 1) Accordng to the score atrx R of the user and servce unt to construct the bpartte network G whch represents the reatonshp between the user and the servce unt. In whch the weght of the edge deterned by user whether or not to purce or browse the proect. If the user purced the proect so v=1 f the user browsed the proect v 0 1 f the user never purced nor browsed the proect v=0. 2) If the user ust browsed the proect reguatng the vaue of whch coud be adusted the pact on forua (1) and to get a ore accurate ratng atrx a w. 3) Accordng to the varous attrbutes of the servce unt to generate the correspondng attrbute expand nae by h agorth n whch expand nae contans the attrbute nforaton of the proect then uses "the stronger servce unt" custerng agorth to ap the dstrbuton servce unt to the vrtua set of the correspondng attrbutes to generate a seres of sub-chord rng. In whch the stronger servce unt refers to the hghest rate servce unt n the sae cass servce unt each sub-chord rng a sae attrbute servce unt then regard hghest rate servce unt as head node to consttute the an chord rng. 4) Accordng to the forua (3) to get correspondng attrbutes by the user hstory seected servce unt as forecast ratng set of the servce unt then cassfy nto each sub-chord rng by attrbutes seect Top-N forecast hghest ratng proects n sub-chord rng as canddate set O. Step 2: Recoendng correspondng attrbute servce unt set O accordng by the seected servce of the target user. Retrevng the servce unt whch the sae attrbutes fro an chord rng accordng by the seected servce of the target user unt to the a sub-chord rng returnng the fna recoendaton servce unt st to user. A. Data set IV. EXPERIMENTATION Usng standard data set MoveLens to test the feasbty of the agorth [15]. Ths data set contans 1682 oves 943 users a tota of ove ratngs wth users. The ratng vaue fro 1 score to 5 score the degree of kng the oves ncreents fro 1 score to 5 score the ratng vaue ore than 3 represents recoend ths ove seectng score records ore than 3 are as edges whch consttute as users-f bpartte network. The experent patfor confguraton s as foows: 2.0 GHz Inte CPU 2 GB of eory Wndows XP and uses the Java prograng anguage. In the experent seectng randoy 90% of the data n the data set as tranng set to desgn and construct agorth the other 10% as test set to test the feasbty of the recoended agorth. Seectng 10 average vaue of the evauaton resut as fna recoendaton resut to do coparson test. B. Agorth Evauaton Index Usng average rank score [11] to easure the recoended accuracy of the agorth n ths paper usng Hang dstance [11] to evauate the dversty and usng recoended te deay to evauate the executon effcency of the agorth. Average Rank Score For the target user recoendaton agorth coud be gven the user a sort of ength L recoended st f user seected servce o and o at the R n the recoended st argung that the reatve poston of proect o s as foows: R r (4) L Servce o n the test set s the seected of user the ore accurate agorth of reatve poston and the at rank the saer of r. Dversty For any two users u and u whch dstance between ts recoended st s Q H 1 (5) L In whch L represents the ength of the recoended st Q represents the sae proect nuber n ength L recoended st of users u and u. Cacuatng the Hang dstance between any two users then cacuatng the average vaue H usng H to easure the dversty of the agorth. Vaues H s between 0 and 1 H=1 represents a the user recoended sts are not the sae whch the best recoended dversty and H=0 represents a the user recoended sts are the sae. 4

5 K <H> <r> Internatona Journa of New Technoogy and Research (IJNTR) ISSN: Voue-2 Issue-2 February 2016 Pages Novety Because of the ess popuar products the ore nove users fe so the agorth shoud be abe to recoend the proect of dffcuty to fnd for users rather than fashon products. Therefore the degree of novety evauated by the average degree <K> of L proects n user recoend sts. It s ore practca sgnfcance to recoend ess popuar coodty than popuar products so the saer the average degree of <K> the better of the agorth nove degree that s not a popuar proect coud aso be recoended. C. Anayss of Experenta Resuts Ths experent any coparson accuracy dversty popuarty and so on set v =0.5 n ths experent L represents the ength of the recoendaton st. The experenta resuts are shown n fgure 4 and fgure 5 and fgure Recoendaton agorth s proposed n ths paper NBI cobnaton agorth SA-CF agorth network can prove the recoendaton accuracy and dversty and reduce popuarty of the recoended proect. Ths ndcates that the degree of personazed been enhanced and the ess popuar servce aso the chance of recoendaton. V. CONCLUSION Ths paper presents a bpartte network recoendaton ode based on chord rng n coud coputng envronent whch s used to sove the probes of the tradtona recoendaton agorth apped to the coud coputng envronent such as the oss of the score and the extenson of the recoendaton. Frsty ths paper ntroduces chord sortng to seect servce resource whch greaty nfuenced on the process of recoendaton and convenent recoendaton syste rapdy retreve the canddate sets then ntroduces weght n the bpartte network to prove the accuracy of recoendaton agorth. The experenta resuts show that the recoendaton agorth n ths paper have sgnfcanty proved than NBI agorth and SA-CF agorth n the recoendaton accuracy the degree of personazaton and recoendaton of unsought goods Fgure 4: The accuracy the agorth n ths paper NBI cobnaton agorth SA-CF agorth Fgure 5: The dversty the agorth n ths paper NBI cobnaton agorth SA-CF agorth 200 Fgure 6: The popuarty Fro fgure 4 and fgure 5 and fgure 6 we can see that recoendaton agorth based on the weghted bpartte REFERENCES [1] Tunkeang D. Recoendatons as a conversaton wth the user[c]//proceedngs of the ffth ACM conference on Recoender systes. ACM 2011: [2] XU Ha-Lng WU Xao LI Xao-Dong YAN Bao-Png. Coparson Study of Internet Recoendaton Syste [J]. JOURNAL OF SOFTWARE (2): [3] HUANG ZAN DANIEL ZENG HSINCHUN CHEN. A coparson of coaboratve-fterng recoendaton agorths for e-coerce[j]. IEEE Integent Systes 2007 (5): [4] DHILLON INDERJIT S. Co-custerng Docuents and Words usng Bpartte Spectra Graph Parttonng[J]. 2001: [5] JIAN-GUO LIU BING-HONG WANG QIANG GUO. Iproved coaboratve fterng agorth va nforaton transforaton[j]. Internatona Journa of Modern Physcs C (02): [6] YI-CHENG ZHANG MARCEL BLATTNER YI-KUO YU. Heat conducton process on county networks as a recoendaton ode[j]. Physca revew etters (15): [7] Me P Grance T. The NIST defnton of coud coputng[j] [8] Zhang Y C Battner M Yu Y K. Heat conducton process on county networks as a recoendaton ode[j]. Physca revew etters (15): [9] Wang J C Chu C C. Recoendng trusted onne aucton seers usng soca network anayss[j]. Expert Systes wth Appcatons (3): [10] ZAN HUANG HSINCHUN CHEN DANIEL ZENG. Appyng assocatve retreva technques to aevate the sparsty probe n coaboratve fterng[j]. ACM Transactons on Inforaton Systes (TOIS) (1): [11] TAO ZHOU JIE REN; MATÚŠ MEDO; YI-CHENG ZHANG. Bpartte network proecton and persona recoendaton[j]. Physca Revew E (4): [12] KOH SU MING LIANG-TIEN CHIA. Web age custerng wth reduced keywords and weghted bpartte spectra graph parttonng. Advances n Muteda Inforaton Processng. PCM Sprnger Bern He 2006: [13] TVEIT AMUND. Peer-to-peer based recoendatons for obe coerce[c]. Proceedngs of the 1st nternatona workshop on Mobe coerce. ACM 2001: [14] ZHANG Yun-yong LI Su-fen WU Jun FANG Bng-y. Research on the coud servces provder-orented servces seecton ethod [J]. Journa on Councatons (9): [15] CteULke Data Sets.Who -post -what data [OL].[ ]. 5