A Reputation Management Policy Based on the User Credibility for Cloud Services

Transcription

1 Iteratoal Joural of Egeerg ad Advaced Research Techology (IJEART) ISSN: , Volume-2, Issue-3, March 206 A Reputato Maagemet Polcy Based o the User Credblty for Cloud Servces Xao-gag Ca, Qg-tao Wu, Mg-chua Zhag Abstract I order to esure the hgh avalablty of the cloud servce credt system, ad for that there are some problems such as dsloyal evaluato exsted the system, the lack of ethusasm for evaluato ad so o, a cloud servce resource maagemet strategy based o the credblty s proposed. Ths paper uses the "credblty" of the user odes to modfy reputato value of cloud servces, whch based o the dstrbuted computg of cloud servce reputato, troduces the dyamc cetve mechasm to mprove the ethusasm of user evaluatg cloud servces smultaeously, ad combes calculato credblty, the modfcato reputato ad evaluato cetves to buld a dstrbuted reputato evaluato model of hgh credblty. The smulato results verfy ts feasblty ad effectveess, ad mprove the ethusasm of the user evaluatg cloud servces whle mprove the accuracy of the results s mproved. Idex Terms cloud servce; reputato ratg; reputato revso; ratg credblty; pealty cetve. I. INTRODUCTION Wth the rapd developmet of cloud computg, more ad more cloud servces have bee geerated.. Users wsh to get hgh-qualty servces from the cloud, whle provders are lookg to optmze the allocato of resources ad wsh that the users are hoest to use the cloud servce. Therefore, the reputato maagemet for cloud servce resources s partcularly mportat. Through the collecto ad aalyss of the hstorcal behavor of odes, reputato maagemet ca predct ther possble behavor the future teracto, whch wll provde a certa bass for the choce of teractve obects ad successfully avod the rsk. The reputato maagemet has bee wdely used the busess evaluato of ole trade, the upload ode selecto of p2p fle trasfer, grd computg ad other felds[,2]. For the ope, dyamc cloud servce evromet, the troducto of reputato system provdes the bass for the user to obta accurate ad relable cloud servces, whch wll mprove the accuracy ad relablty for cloud servces push. How to buld a effectve reputato evaluato model for cloud servces has become a hot research[3]. Paper [4] preseted a reputato measuremet approach of Cloud servces based o cloud model ad fuzzy logc for ustable feedback ratgs of Cloud servces ad compute the ustablty of feedback ratgs ad the employ fuzzy logc to calculate the reputato score of Cloud servce. Paper [5] proposed a ew model based o D-S evdece theory, whch uses the fuzzy theory to deal wth the customer's satsfacto wth the related servce. Paper [6] proposed a ew method of flterg out accurate ratgs based o departure degree, whch s proposed to revse the egatve ad postve error evaluato. Paper [7] desged a kd of servce selecto model based o reputato percepto, whch s based o a reputato revso method. Paper [8] preseted a ew method whch s used as a referece to detfy ad flter out the accurate ratgs by evaluatg the mea value of smlarty wth the pror kowledge. The above research provdes certa theoretcal bass for cloud servce resource reputato maagemet, but there are some defceces. Lterature [4, 5] more cocered reputato maagemet of cloud servce provder, whch gored trust evaluato of the cloud servce users, ths makes some users get ther ow persoal terests to make compatble wth ts reputato durg evaluatg cloud servces, such as excessve exaggerato or malcous slader. Lterature [6-8] proposed some amedmet credt strategy, but wthout cosderg some users oly usg cloud servces but ot partcpatg the evaluato actvely by reaso of ther selfshess, ert or other factors, whch affects the reputato ceter get suffcet evaluate resources. These weakesses make the dffcult of acqurg real credt of cloud servces, ad the affect the avalablty of the credblty system. Cosderg these problems, a reputato maagemet polcy has bee proposed. Based o the drect experece betwee the evaluato odes ad the cloud servce provders, the credblty of each evaluato ode has bee measured whch wll drectly affect ts weght the reputato computg. The the reputato result wll be more accurate ad more useful to respod to user chages demad. At the same tme, a dyamc cetve mechasm based o pushmet has bee troduced. The chage for the partcpato degree of user ode wll push or reward the credblty of the user ode, ad further affect the scope ad qualty of recommeded cloud servces the user ode wll get. Evetually, the user wll be prompted to carry out the hoest evaluato actvely. II. REPUTATION EVALUATION MODEL AND RELATED DEFINITIONS FOR CLOUD SERVICES The fgure s the reputato evaluato model for cloud servces. The tal republca s a tal value of the cloud servce regstered to the servce reputato maagemet ceter. The value of the global reputato s obtaed by the calculato the reputato ceter, to provde a referece for all users. The local reputato s stored as a evaluato set the user sde. As a hstorcal evaluato formato, the local reputato s saved ad used for referece of the dvdual demads. There are may subectve factors such as malcous 34

2 A Reputato Maagemet Polcy Based o the User Credblty for Cloud Servces evaluato user depedet evaluato carred out by the experece of the users. So the reputato revso s ecessary for the reputato evaluato. I order to ecourage users to partcpate the evaluato, the dyamc cetve mechasm based o pushmet s troduced. Recommed reputato Local reputato Reputato computg Servce selecto Global reputato Reputato correcto Partcpato Degree Servce request Servce regster Ital reputato User credblty pealtycetve mechasm Iformato feedback Cloud Reputato ceter User Fg Reputato evaluato model for cloud servces The model maages the reputato of cloud servces, servce provders ad users by the combato of reputato computg, reputato revso ad evaluato cetve, whch made the dstrbuted reputato evaluato model more credble. Defto. Ratg Perod (RP): t s defed as user s several tradg stages whch cota at least oe trasacto behavor. Defto 2. Ratg Credblty (RC): t reflects the authetcty or accuracy of the evaluato submtted by the user ode. Its value s calculated based o the evaluato behavors wth a fxed umber of tradg stages the ratg perod, the hgher the value of reputato, the more credtable the evaluato. Defto 3. Partcpato Degree (Pd): we defe the Pd as follows: R, f E 0 Pd E 0.5, else I the defto 3, R s the umber of evaluato ad E s the total umber of cloud servces users got wth a fxed umber of tradg stages the ratg perod. III. REPUTATION COMPUTING, REVISION AND INCENTIVE POLICY A. Reputato Computg of Cloud Servce The reputato computg for cloud servces cludes global reputato computg ad local reputato computg. Massve user s local reputato were collected to the reputato ceter, whch formed the global reputato. As each user's persoal preferece s dfferet, the user s local reputato evaluato wll be the bass for the recommedato whe the reputato ceter were recommedg the cloud servces. SR GR ( ) LR [0,] () SR s the referece of the servce recommedato, GR s the global reputato stored the reputato ceter, LR s the local reputato stored the user's local storage, s a weght parameter. User wll make the evaluato after havg used the cloud servce. The user gve the obectve quattatve score accordg to the qualty of servce parameters ad gve the subectve qualtatve score accordg to ther satsfacto. The the obectve evaluato ad the subectve evaluato wll gather to form the LR, whch wll be upload to the reputato ceter. SID,S, F s defed as the dex for the regstered cloud servce. SID s a specfc servce, the S s the umber of tmes the dex of ths servce has reached the specfed level ad F s the umber of tmes the dex of ths servce has ot reached the specfed level. I the evaluato, the satsfacto s marked as s, whle the ot satsfacto s marked as f. Ad s f. LR( SID) F ( q, q,, q ) F ( user) (2) o 2 s The F o s a fucto whch tegrated related dcators q for QoS. The F s s a fucto to hadle user satsfacto. As s show formula (3). S s Fo( q, q2,, q) ( ) S F (3) The global reputato are calculated by collectg all the evaluato of the same servce. I order to reflect the tmeless of reputato ad reduce the mpact of hstorcal reputato, we troduce the expoetal decay factor to crease the fluece of real tme servce. As s show formula (4): GR GR e ' t f ( LR ) (4). B. Reputato Revso Based o the Credblty of Users Due to the malcous bad revew or delberate good revew, we revsed the reputato evaluato by troducg the credblty of the evaluato ode. The the dfferet user wll have dfferet credblty ad the feedback formato of dfferet user wll have dfferet treat. The fucto f s show formula (5). f ( LR ) F ( q, q,, q ) F ( user) RC (5) o 2 s The user's ratg credblty s usually flueced by two factors: ). subectve ad obectve ratg smlarty; 2). subectve ad maorty ratg smlarty. ) The subectve ad obectve ratg smlarty (SORS) The subectve evaluato ad obectve evaluato measure the performace of servce from two agles, ad the two groups should be cosstet or have hgh smlarty. Because the obectve evaluato s calculated based o the actual motorg value, wth a hgh referece value, t ca be used as the referece pot to measure the subectve evaluato. We regard the subectve evaluato sequece Sr ( sr, sr2,, sr ) ad obectve evaluato sequece 35

3 Or ( or, or2,, or ) as two pots -dmesoal apace. By calculatg the Eucldea dstace of the two pots, we ca measure the subectve ad obectve ratg smlarty. The smaller the dstace, the greater the smlarty ad the hgher the credblty of subectve evaluato. As s show the formula (6). Iteratoal Joural of Egeerg ad Advaced Research Techology (IJEART) ISSN: , Volume-2, Issue-3, March (6) SORS sr or 2) The subectve ad maorty ratg smlarty (SMRS). It s geerally cosdered that the subectve evaluato of most user odes the evaluato system s reasoable ad relable. Based o ths, we use the k-meas algorthm to cluster the evaluato formato R r,2,, a perod of tme. C,2,, k dcates k class of clusterg, c,2,, k dcates the tal clusterg ceter, the Eucldea dstace betwee two data obects s the formula (7):, 2 d x x x x (7) The clusterg ceter s the formula (8): c x (8) x C The core dea of k-meas algorthm s to dvde the data obect to dfferet clusters by terato, so as to make the obectve fucto (formula (9)) mmal ad the geerated clusters as compact ad depedet as possble. E k d x,c (9) The, we choose the ceter of the maxmum cluster as the maorty ratg (MR) value of R. MR ceter max C (0) MR s used as a referece, ad the Eucldea dstace betwee the user subectve evaluato sequece Sr ad MR s used to calculate the subectve ad maorty ratg smlarty SMRS. As s show the formula (): 2 () SMRS sr mr The smaller the value of SMRS whch explas that the evaluato of ths partcular user s close to the evaluato of most users, the hgher the credblty of users. Fally, the calculato of the user s ratg credblty s show the formula (2): 2 arcta RC SORS SMRS (2) 0 RC, 0, s the weght factor,. C. Dyamc Pealty-cetve Mechasm Based o User Credblty I order to solve the problem that users oly use cloud servces but ot gve evaluato actvely, we troduce the cetve mechasm based o pealty ad costruct the reputato evaluato cetve model, as s show fgure 2. User ode Partcpato degree Recommed dfferetal cloud servces based o user ratg credblty (servce rage,servce qualty,etc.) Dyamc pealty-cetve mechasm Fg.2. Reputato evaluato cetve model Republca ceter Ratg Credblty The core dea of the model s that dscoverg the partcpato of user s evaluato ad determg the push degree of the ratg credblty based o the evaluato behavor. The pushmet dyamcs wll cotue to adust dyamcally, accordg to the hstorcal evaluato behavor ad cumulatve ratg credblty of the user ode. I order to obta hgh qualty recommeded cloud servce from reputato ceter, users wll actvely mata ther ow evaluato credblty ad actvely partcpate the evaluato. Pd s lower tha Pdthreshold, eter the pealty perod Normal perod 2 3 pealty perod Normal perod Fg.3.The cetve flow chart based o pushmet Tme Tme As s show Fgure 3, whe the partcpato degree of the user ode s lower tha a certa threshold, the eterg the pealty perod ad wll accept a certa amout of tme ad frequecy of cotuous pushmet. I the pealty perod, the user ode must cotuously ad actvely partcpate the evaluato before the ed of the pealty perod to retur to ormal perod. s a pealty cout factor, whch dcates the umber of stages the pealty perod. The cetve mechasm determes the pushmet dyamcs of dfferet evaluato behavor by adustg.the value of s related to the hstorcal behavor (partcpato degree, ratg credblty, etc ) of user ode the tme wdow of the fxed w trasacto before ths curret trasacto s evaluated. Its calculato method s show the formula (3). wk w Nk N N l w k N RC k l Pd k l k (3) k k w l log,,,, N represets the umber of trasactos produced the k k phase; Pd, k, l represets the partcpato degree that the user ode fshed the l trasacto the k stage ad 0 Pd RC, k, l represets the ratg credblty ; that the user ode fshed the l trasacto the k stage ad 0 RC ; 0 represets the proporto dstgush factor; s the pushmet testy factor, the smaller the value, the greater the pealty cout factor. The umber of remader pealty phase the pealty perod wll be calculated accordg to a certa rule based o 36

4 A Reputato Maagemet Polcy Based o the User Credblty for Cloud Servces the stuato volved the evaluato of the stage after the ed of each phase, as s show formula 4. t s the umber of curret remader pealty phase the pealty perod of user ode, t s the umber of the ew phase, r s s a radom fucto, s a referece factor ad 0. Uder ormal crcumstaces, f the user ode the pealty perod partcpates evaluato cotuously ad actvely, the umber of remader pealty phase wll be reduced by. Ad the pushmet wll be ed f 0. O the cotrary, f the user s evaluato behavor s egatve durg the pealty perod, the pealty cout factor wll be recalculated as s show formula (3). Obvously, the egatve evaluato behavor the pealty perod wll evtably lead to the exteso of the pealty perod. To ed the pealty perod, the user odes must cotue to partcpate the evaluato more. Ad, eve f the user ode actvely partcpates the evaluato of the curret stage, the probablty of reducg remader pealty phase s oly. Ths makes the user odes ad cloud servce provders ca ot cospre to quckly ed the pealty., fpd, k, l Pdthreshold, t 0 max, t, fpd, k, l Pd, k, l, t 0 t, fpd, k, l Pd, k, l, t 0, r s t t, fpd, k, l Pd, k, l, t 0, r s 0, otherwse Whe 0 k,,, (4) RC k l dcates the credblty that the user ode fshed the l trasacto of the k stage the pealty perod. Whe k 0, the user ode s the ormal perod. 0 odes the pealty perod, represets the tal umber of stages for user t represets the remader umber of stages for user odes the pealty perod. As the rules of RC, k, l are show the formula (5): m RC, k, l,, fpd, k, l Pd, k 0 threshold RC, k, l RC, k, l RcD, fpd, k, l Pd, k 0 threshold (5) 0 t RC,, RcA, fk 0 0 s a very small credblty cremet for user odes the ormal perod, RcD s a credblty pealty whe the partcpato degree s lower tha the threshold value Pd threshold. RcA s a credblty bous whe the user ode actvely partcpated evaluato the pealty perod. Usually, RcA RcD. Obvously, f the evaluato behavor of the user ode the pealty perod s egatve, ts ratg credblty wll be reduced by stages. If the behavor s postve, the ratg credblty wll be gradually restored. The pseudo code of the dyamc cetve mechasm based o pushmet s as follows: Beg // Get put parameters Get(Pd threshokd, Pd(,k,l), RC(,k,l),k); Whle k=0 // update ratg credblty the ormal perod If Pd(,k,l)>Pd threshokd the RC(,k,l)=m(RC(,k,l-)+,); Else f Pd(,k,l)<=Pd threshokd the RC(,k,l)=RC(,k,l-)-RcD; k=; //the sg of the pealty perod s Ed whle Whle k= // Eter the pealty perod // The dyamc calculato of the umber of the remader phases the pealty If Pd(,k,l)<Pd threshokd ad (t)=0 the (t+)=θ ; Else f Pd(,k,l)<=Pd(,k,l-) ad (t)>0 the (t+)=max(θ, (t)+); Else f Pd(,k,l)>Pd(,k,l-) ad (t)>0 the (t+)= radom( (t), (t)-); Else (t+)=0; Ed f If (t+)=0 the k=0; // the sg of the ormal perod s 0 Ed f //update the ratg credblty RC(,k,l)=RC(,,l)+RcA*( (0)- (t))/ (0) Ed whle //Submtted the ratg credblty to the reputato ceter Sed RC(,k,l) to reputato ceter. Ed IV. EXPERIMENTS AND ANALYSIS A. Reputato Evaluato Smulato Expermet I We desged a ava smulato program to smulate the behavor of the user's evaluato the cloud evromet, whch s used to verfy our proposed reputato revso method. Ths system cossts of 00 cloud servces provded by cloud servce provders ad 60 dfferet types of users. Cloud servces are dvded to may categores accordg to fucto ad cofgurato. The S represets cloud servces, ad ts reputato evaluato s dvded to four categores: ormal, good, bad, ad excellet. The U represets users, ad t s dvded to three categores: preferece evaluato, obectve evaluato ad devato evaluato. I order to reflect the user's type, the true qualty of cloud servces s kow for each cosumer the process of evaluato. Accordg to the type of user they belog to, they make dfferet evaluato behavor. After each trasacto, the user should make a score of the cloud servce. I order to revse the abormal reputato score, the reputato data were extracted from the data the cluster ad revsed after each roud. I our expermets, the ma valdato of the two hypotheses: ) Calculatg the user's credblty of evaluato ca mprove the accuracy of the results of reputato computg. 2) Itroducg the evaluato cetve model ca effectvely mprove the ethusasm of the user to partcpate the evaluato. Fgure 4 shows the reputato value of each servce after fve roud of the evaluato, ad the these data are aggregated to test the effectveess of our proposed method. Fgure 5 shows the comparso for the accuracy of reputato value betwee havg troduced the modfed cetve ad wthout troducg the modfed cetve. 37

5 accuracy raputato value user average partcpato degree ( %) Iteratoal Joural of Egeerg ad Advaced Research Techology (IJEART) ISSN: , Volume-2, Issue-3, March default value frst secod thrd fourth ffth troduced cetve mechasm omal servce tme Fgure 4 the reputato value of each servce after fve roud of the evaluato Fgure 4 shows the dstrbuto of reputato value for cloud servce after fve roud of reputato evaluato. The tal value s set as 0.5. After a perod of accumulato, t ca clearly dstgush dfferet cloud servce qualty level. As s show clearly Fgure 4-2, the servce has roughly four levels, but accurate reputato value s stll exst, whch s affected by the exstece of the dshoest evaluato ad subectve prefereces of users. The devato of reputato for cloud servce wll gradually decrease whe the system has suffcet umber of users accuracy troduced the modfed cetve accuracy wthout troducg the modfed cetve tme Fgure 5 the accuracy of reputato evaluato for cloud servce As s show Fgure 5, the tal accuracy s oly 0.45 after the tal value of the cloud servces s set. However the tal reputato value s ofte ot able to characterze the true qualty level. The accuracy of global reputato value rema at the level of 75% wth cotuous chage after a roud of reputato evaluato, whch s hgher tha the accuracy wthout troducg the modfed cetve. Ths also proves that the process of reputato calculato s dyamc ad teds to be obectve, ad t ca descrbe the qualty level of servce after a lot of reputato accumulato. B. Smulato Test of Icetve Mechasm for Reputato Evaluato I order to accurately verfy the evaluato results wth the troducto of cetves, we set that the proporto user had partcpated evaluato at each roud was the geeral computg, the proporto user had partcpated evaluato at each roud was the ormal perod after troducg the cetve mechasm ad the proporto user had partcpated evaluato at each roud was the pushg perod after troducg the cetve mechasm. ad radomly geerated ad 0. Fg 6.user partcpato degree after 0 rouds of reputato evaluato As s show Fgure 6, the user's tal average partcpato degree of 0.5, the same codtos, before troducg the cetve mechasm, the crease of the average partcpato degree teds to be getle, ad fally, t s about 80%. After troducg the cetve mechasm, the user s average partcpato degree mproved rapdly after the secod roud evaluato. The overall average partcpato degree of the user wll gradually be mataed at a hgher level. V. CONCLUSIONS The emergece of massve cloud servces forms a challege for the user to detfy ad select the cloud servces wth hgh qualty. ) I ths paper, we propose a reputato maagemet polcy based o the credblty of the user to provde some referece for the servce selecto. 2) We troduced the dea of the cetve mechasm to provde metalty for solvg the egatve pretermsso the evaluato partcpato. 3) The method we proposed s effectve, but there s some defceces. Next step, we wll cosder the fluece of computatoal complexty o the etre reputato system, whch may mprove the effcecy of the system. ACKNOWLEDGMENT Ths work s partally supported by the Natoal Natural Scece Foudato of Cha (NSFC) uder Grat o ad U4046, part by Program for Scece & Techology Iovatve Research Team Uversty of Hea Provce uder Grat o. 4IRTSTHN02. REFERENCES [] J Y, Zh m G U. A Survey of Key Problems of Trust Based o Reputato Peer-to-Peer Systems, [J]. Joural of Chese Computer Systems, 2007, 28(9): [2] Yogru Cu, Mgchu L, Yag Xag. A QoS based fe graed reputato system the grd evromet, [J]. Cocurrecy & Computato Practce & Experece, 202, 24(7): [3] Achm O M, Pop F, Crstea V. Reputato based selecto for servces cloud evromets, [C]. Network-Based Iformato Systems (NBS), 20 4th Iteratoal Coferece o. IEEE, 20: [4] Wag S, We J, Su L. Reputato Measuremet of Cloud Servces Based o Ustable Feedback Ratgs, [C] Parallel ad Dstrbuted Systems (ICPADS), 203 Iteratoal Coferece o. IEEE, 203: [5] Pg Wag. Maagg servce reputato wth vague sets, [C]. e-busess Egeerg (ICEBE), 202 IEEE Nth Iteratoal Coferece o. IEEE, 202: [6] Hu S M, We G J, L N E, et al. RulerRep: Flterg out Iaccurate Ratgs Reputato Systems Based o Departure Degree: RulerRep: 38

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