An Approach to Incentive-based Reputation for Communities of Web Services

Transcription

1 An Approach to Incentve-based Reputaton for ommuntes of Web Servces Babak Khosravfar 1, Jamal Bentahar 1, Phlppe Thran 2, Ahmad Moazn 1, and Adren guot 2 1 oncorda Unversty, anada, 2 Unversty of Namur, Belgum b khosr@encs.concorda.ca, bentahar@cse.concorda.ca, pthran@f undp.ac.be a moaz@encs.concorda.ca, aguot@student.f undp.ac.be Abstract ommunty of web servces (WS) s a socety composed by a number of functonally dentcal web servces. The communtes always am to ncrease ther reputaton level n order to obtan more requests. In ths paper, we propose an effectve mechansm dealng wth reputaton assessment for communtes of web servces. The proposed mechansm s based on after-servce feedbacks provded by the users to a run-tme loggng system. The proposed method defnes the evaluaton metrcs nvolved n reputaton assessment of a communty, and supervses the loggng system n order to verfy the valdty and soundness of the feedbacks provded by the users. In ths paper, the proposed framework s descrbed, a theoretcal analyss of ts assessment and ts mplementaton along wth emprcal result dscussons are provded. We also show how our model s effcent, partcularly n very dynamc envronments. Keywords. Web Servce, ommunty, Reputaton. 1 Introducton Lterature Revew. As one of the recent technologes for developng loosely-coupled, cross-enterprze busness processes (usually referred to as B2B applcatons), a plethora of web servces exsts on the web watng to receve users requests for processng. Ths contnuous choce s usually reputaton-drven. In lterature, the reputaton of web servces have been ntensvely stressed [10]. In [1], the authors have developed a framework amng to select web servces based on the trust polcy expressed by the users. The framework allows the users to select a web servce matchng ther needs and expectatons. In [2], Malk et al. have proposed a model to compute the reputaton of a web servce accordng to the personal evaluaton of the prevous users. The characterstc of ths method s that the credblty of the users evaluatng the web servce s taken nto account. If the rater tres to provde a fake ratng, then ts credblty wll be decreased and the ratng of ths user wll have less mportance n the reputaton of the web servce. In [4], the authors have desgned a mult-agent framework based on an ontology for QoS. The users ratngs accordng to the dfferent qualtes are used to compute the reputaton of the web servce. In [6, 10], some web servces reputaton mechansms have been proposed, that would lead to an effectve servce selecton, and n [5], servce-level agreements are dscussed n order to set the penaltes over the lack of QoS for the web servces. In general, n all the mentoned models, web servces are consdered to act ndvdually and not n collaboraton wth other web servces. Recently, there have been few attempts to address the formaton and reputaton of ommuntes of Web Servces (WSs) [7, 13]. The man property of a WS s to facltate and mprove the process of web servce selecton and effectvely regulate the process of user requests. There are underlyng reasons for ths. Frst, the communty gathers a set of functonally homogeneous web servces regardless of who developed them, where they are located, and how they functon. Gven that some communtes offer the same functonalty (hotels bookng, weather forecastng, etc.), there s a competton between dfferent communtes. In ths case, reputaton s consdered as a dfferentaton drver of the communtes. Moreover, reputaton helps users to select the most reputable communty, whch would provde the best QoS, and helps provders to jon the best communty, whch would brng them the most value. In [7], Elnaffar et al. propose a reputaton-based archtecture for WSs and classfy the nvolved metrcs that affect the reputaton of a communty. They derve the nvolved metrcs by processng some hstorcal performance data recorded n a run-tme loggng system. The purpose s to be able to analyze the reputaton n dfferent ponts of vew, such as users to WSs, WSs to web servces, and web servces to WSs. The authors dscuss the affect of dfferent factors whle dverse reputaton drectons are analyzed. However, they do not derve the overall reputaton of a WS from the proposed metrcs. Moreover, they assume that the run-tme loggng mechansm s an accurate source of nformaton. Proposed Model. In ths paper, we extend the work done n [7] by two contrbutons. In the frst contrbuton, 1

2 Fgure 1. Archtecture of reputaton-based WS we propose a reputaton model of a communty of web servces, whch s based on nvolved metrcs (responsveness, ndemand, satsfacton and tme recency). Ths model s used by users and provders to estmate the reputaton of a communty. In the second contrbuton, we dscuss more on the feedback loggng mechansm and gve a relable mechansm (capable of managng malcous acts of agents). We assume that the WS may be encouraged to volate such run-tme loggng mechansm n support of themselves or aganst other communtes. To ths end, we dscover the ponts of volaton n the sense that the controller agent g (the agent, that s assgned to montor the loggng data and ntroduced n Secton 4) to some extent, makes sure that the volaton s taken place. Then we propose a method to properly react for such volatons. We provde a theoretcal analyss based on backward nducton to prove that there s an ncentve for communtes not to volate the loggng system. In ths analyss, we derve the comparatve values of reward and penaltes for WSs n order to obtan such an ncentve. The smulatons results reveal how, emprcally, our trust model yelds a system that autonomcally adjusts the level of WS s reputaton. What specfcally dstngushes our model from other smlar works n the lterature s: (1) ts sound formaton of the reputaton assessment for the WSs; and (2) ts ncentve-based reputaton adjustment n the sense that although the communtes are capable of msleadng the loggng system n support of themselves (or aganst ther opponents), they wll not take the rsk to do that, gven the fact that they are aware of possble consequent penalty that would decrease ther current reputaton level. The ntutons behnd the ncentve-based mechansm are: (1) we obtan an accurate nformaton for dervng the nvolved metrcs used for the reputaton of a partcular communty; and (2) we obtan an overall hgher relablty and effcency n the sense that upon volaton detecton, WSs are strctly encouraged to show an acceptable performance n ther further user request processes. Ths factor s analytcally proved n Secton 4.3 and expermentally shown n Secton 5. Organzaton. The remander of ths paper s organzed as follows. In Secton 2, we defne the archtecture of reputaton-embedded WSs, whch s composed of extended UDDI, user and provder agents and reputaton system. In Secton 3, we dscuss the reputaton model by ts nvolved metrcs and we propose a methodology to combne them. In Secton 4, we extend the dscusson about mantanng a sound loggng mechansm used as source of nformaton for the metrcs. We dscuss the fake postve and negatve correctons and provde the ncentve to avod fake attempts. In Secton 5, we represent the smulaton and outlne the propertes of our model n the expermental envronment. Fnally, Secton 6 concludes the paper. 2 Archtecture of reputaton-embedded Web servces ommuntes In ths secton, we represent the WS archtecture [7]. Ths archtecture s desgned to mantan the reputaton of the communtes. Here we assume that each web servce has s assocated wth a communty and do not functon alone. If a web servce s not regstered n a communty, t could not be nvoked by a user. However, a web servce can be regstered n one of many communtes. In fgure 1, we represent dfferent components of the archtecture, wth ther reputaton and nteractons. These components together wth ther detaled performance are explaned as follows: User agent. It s a proxy between the user and the other nteractng partes such as the extended UDDI, WS and the reputaton system. 2

3 Master agent. Ths agent s consdered as the representatve of the communty n the sense that t manages the communty requests n selectng the proper web servce. Meanwhle the master agent hres (or fres) some web servces to jon (or leave) the communty. Provder agent. Lke the user agent, t relates the provder wth the extended UDDI, WS and the reputaton system. Extended UDDI. The tradtonal UDDI XML schema s based on sx types of nformaton, allowng people to have nformaton n order to nvoke the web servces [8]. In the UDDI regstry, we restrct the access of the agents n the sense that user and provder agents only consult the lst of masters, whereas the masters have access to the lst of the web servces n the UDDI regstry. By addng ths new knd of nformaton concernng the WSs, we would clarfy whch WS a web servce belongs to. Reputaton system. onsderng the fact that the WSs could offer the same servce, thus they always compete n order to obtan more requests. Therefore, evaluatng WSs s unavodable for users and provders. To be able to compute the reputaton of WSs, the user and provder agents must gather operatonal data, reflectng dfferent performance metrcs, about the nteracton between the user, the provder and the WS. The user agents should ntercept some logs lke Submssons log, Response Tme log, Invocaton log, Successes log, Falure log, Recoveres log and so on. It s mportant that the user and provder agents are ndependent partes n order to ntercept trusted run-tme data about each web servce nteracton. The reputaton system s the core component n ths archtecture. Its frst functonalty s to regster the run-tme logs; and the second functonalty s to rank the communtes based on ther reputaton by usng a rankng algorthm. The rankng algorthm would mantan a restrctve polcy, avodng the rankng volaton, whch could be done by some malcous WSs. The volaton, whch has not been consdered n [7], s done by provdng some fake loggng data (by some colludng users) that reflect postve feedback n support of the WS, or by fake negatve data that s regstered aganst a partcular communty. To deal wth ths volaton, we propose to assgn a controller agent g. The task of ths agent s to update the WS reputaton rankngs n order to drop naccurate regstered data and thus enhance accuracy of the reputaton system. The detaled dscusson of ths ssue s provded n Secton 4. 3 Reputaton Model For smplfcaton reasons, n the remander of ths paper, we only consder the users pont of vew (rather than users and provders) n reputaton assessment. In order to assess the overall reputaton of a WS, the user needs to take some correlated factors nto account. In Secton 3.1, we present the nvolved metrcs that a user may consder n ths assessment. onsequently, n Secton 3.2, we explan the methodology that the user uses to combne these metrcs n order to assess the reputaton of a WS. 3.1 Metrcs Responsveness Metrc: Let be the communty that s under consderaton by user U j. Responsveness metrc depcts the tme to be served by a WS. Let Res U j,r t k be the tme taken by the master of the communty to answer the request receved at tme t (Rk t ) by the user U j. Ths tme ncludes the tme for selectng a web servce from the communty and the tme taken by that web servce to provde the servce for the user U j. Equaton 1 computes the response tme of the communty, computed wth U j durng the perod of tme [t 1, t 2 ] (Res Uj,[t1,t2] ), where n s the number of requests receved by ths communty from U j durng ths perod of tme. Res U j,[t 1,t 2 ] t 2 t k = 1 Res Uj,R n t=t 1 e λ(t 2 t) Here the factor e λ(t 2 t), where λ [0, 1] reflects the tme recency of the receved requests so that we can gve more emphasze to the recent requests. If no request s receved at a gven tme t, we suppose Res U j,r t k = 0. InDemand Metrc: It depcts the users nterest for a communty n comparson to the other communtes. Ths factor s computed n equaton 2. (1) InD [t 1,t 2 ] Req [t 1,t 2 ] = M (2) k=1 Req[t 1,t 2 ] k In ths equaton, Req [t1,t2] s defned as the number of requests that has receved durng [t 1, t 2 ], and M represents the number of communtes under consderaton. Satsfacton Metrc: Let Sat U j,r t k be a feedback ratng value (whch s supposed to be between 0 and 1) representng the satsfacton of U j wth the servce regardng hs request Rk t sent at tme t to. Equaton 3 shows the overall satsfacton of the user U j to communty. Sat U j,[t 1,t 2 ] t 2 t k = 1 Sat Uj,R n t=t Metrcs ombnaton e λ(t 2 t) In order to compute the reputaton value of a WS (whch s between 0 and 1), t s needed to combne these metrcs n a partcular way. Actually, the Responsveness and Satsfacton metrcs are the drect evaluatons of the nteractons between a user and a WS whereas the n- Demand metrc s an assessment of a communty n relaton to other communtes. In the frst part, each user adds (3) 3

4 up hs ratngs of the Responsveness and Satsfacton metrcs for each nteracton he has had wth the WS. Equaton 4 computes the reputaton of the communty durng the nterval [t 1, t 2 ] from the user U j pont of vew. In ths equaton, ν represents the maxmum possble response tme, so that f a communty does not respond, we would have Res U j,[t 1,t 2 ] = ν. In the second part, the ndemand metrc s added. Therefore, the reputaton of from the users pont of vew s obtaned n equaton 5. Rep U j,[t 1,t 2 ] Rep [t 1,t 2 ],[t1,t2] = η(1 ResUj ) + κsat U j,[t 1,t 2 ] ν (4) = χ 1 m ( ) Rep U j,[t 1,t 2 ] m + φ InD (5) j=1 Where η + κ = 1 and χ + φ = 1. 4 Sound Loggng Mechansm Wthout loss of generalty, n a network composed of WSs, master agents (as representatves of communtes) are selfsh and may alter ther ntentons n order to obtan more benefts (n terms of popularty). Ths could happen by mprovng one s reputaton level or by degradng other s reputaton level. We respectvely refer to these cases as fake postve/negatve alteraton. Volatng the loggng feedbacks (dstractng the reputaton levels) could lead to system nconsstency n the sense that low qualty WSs may obtan more users or hgh qualty communtes may loose some users. Therefore, t s mportant to avod such attacks and keep the loggng mechansm accurate. In the rest of ths secton, we explan how to perform fake postve/negatve correctons and thus effectvely mantan a reputaton adjustment. In the proposed archtecture for the WS, the reputaton s computed based on the nformaton obtaned from the loggng system that over the elapsng tme, users leave ther feedbacks. Thus, t s essental to keep such loggng fle accurate and dscourage malcous actons. To ths end, a controller agent g s assgned that hs responsblty s to mantan an accurate attack-reslent loggng fle. As a part of the UDDI system, g has the authorty to update nformaton such as overall reputaton level of any WS. Wthout loss of generalty, we assume that ths agent s hghly secured n order to avod beng compromsed. However, f g gets compromsed wth a gven communty, then nconsstent actons of g could be recognzed by some other communtes, gven the fact that they are competng wth one another. But ths ssue s out of the scope of ths paper. 4.1 Fake Postve orrecton Fake postve recognton. One of the man responsbltes of the controller agent g s to perform fake pos- Fgure 2. Fake postve correcton cases. tve correcton. To ths end, ntally g should recognze a malcous behavor from one or a set of user agents (that could possbly collude wth a partcular communty). Ths recognton s done based on the recent observable change n the reputaton of a communty. To ths end, g would always check the recent feedbacks of the communtes. So g would consder the reputaton that s computed for a specfc perod of tme [t 1 ɛ, t 1 ], where t 1 s the current tme. The value ɛ s set by the controller agent regardng to the system nconsstency n the sense that f the network s nconsstent, so g would need to check most recent feedbacks (ɛ as relatvely small amount). Otherwse, g would take even older feedbacks nto account (ɛ as relatvely large amount). So, Rep [t 1 ɛ,t 1 ] s the reputaton of the communty obtaned from data measured from t 1 ɛ to t 1. Let be the set of users that durng ths tme nterval provded a feedback for ths communty, and t b be the begnnng tme of collectng feedbacks. g would consder the postve feedbacks to be suspcous f the reputaton m- U [t 1 ɛ,t 1 ] provement (Rep [t 1 ɛ,t 1 ] Rep [t b,t 1 ] ) dvded by the number of users that caused such mprovement s greater than the predefned threshold ϑ,.e: Rep [t 1 ɛ,t 1 ] Rep [t b,t 1 ] > ϑ U [t 1 ɛ,t 1 ] In that case, t s assumed that communty had a drastc reputaton ncrease n the recent ɛ tme. Fake postve Adjustment. Exceedng the threshold ϑ, g would fgure out that a partcular communty s recevng consequent postves. Then g, n order to reload the prevous and actual reputaton level, would freeze the recent postve logs and notfes the correspondng communty of such suspendng. So, g would observe the upcomng behavor (n terms of satsfacton and responsveness) of the communty n order to match the actual effcency wth the suspended enhanced reputaton level. Durng ths perod, the communty s encouraged to behave n such a way that reflects the suspended enhanced reputaton level (see Fgure 2). If the communty showed the real mproved performance, the suspended reputaton trust level would be redeemed and consdered for hs reputaton. But f the communty faled to do so, the prevous reputaton level wll 4

5 be decreased by some appled penaltes. In ths case, the communty would be n such a stuaton that ether has to outperform ts past n order to mprove the enhanced reputaton level, or would loose ts current reputaton, whch s not wanted. Therefore, we form an ncentve that communtes would not rsk ther current reputaton level and thus they do not by any means (colludng wth users or provders) provde fake postves n support of themselves. We assume that t 0 s the start tme of leavng fake postves n the loggng fle, t 1 s the tme that g recognzes such fake actons and consequently starts nvestgatng the upcomng effcency of such communty, and t 2 s the tme that g would update the partcular communty s reputaton value. Let Evol [t 1,t 2 ] be the evolutonary reputaton value for the communty that s measured by the g durng specfed tme nterval [t 1, t 2 ] (nvestgaton perod). Ths value s computed n equaton 6, where δ s a small value that the reputaton s measurable wthn [t δ, t]. Evol [t 1,t 2 ] = t2 t=t1 +δ Rep[t δ,t] t 2 t 1 (6) Also, let P n t be the general penalty value, that s assgned by g at a specfc tme t. Equaton 7 computes the adjusted reputaton level of (Rep *[t b,t 2 ] ). Ths equaton reflects the ncentve that we provde, so that WSs n general would be able to analyze ther further reputaton adjustments upon fake acton. Rep *[t b,t 2 ] = { αrep [t b,t 1 ] αrep [t b,t 0 ] P n t 2, f redeemed; f penalyzed. (7) where α + β = 1. As dscussed before, g wll decde to redeem the communty f the evolutonary value for the reputaton s more than s prevous reputaton value,.e.: Evol [t 1,t 2 ] > Rep [t b,t 0 ]. If g decdes to redeem the communty, then the prevous reputaton value (from tme t b to nvestgaton tme at t 1 ) would be consdered together wth the evolutonary reputaton value as a result of nvestgaton durng [t 1, t 2 ]. If g decdes to penalze the communty, then the prevous reputaton s consdered regardless of the mproved reputaton obtaned n the perod of [t 0, t 1 ]. And n addton to the evolutonary reputaton, a penalty P n t 2 would be assgned at tme t 2. False alarm detecton. It s worth to dscuss more about alternatves of g s fake postves recognton. onsder the two cases that g falsely, and truly recognzes the fake postves. In the former case, the postves are real, therefore, they reflect the actual performance of the communty. Then even beng suspended, the communty can easly prove the qualty level as t contnues as before and bascally would not loose anythng. In the later case, the postves are fake, so the communty needs to mprove ts actual qualty level to prove suspended enhanced reputaton level. If the com- Fgure 3. Fake negatve correcton cases. munty faled to fulfll such reputaton, g would decrease ts prevous reputaton level. 4.2 Fake Negatve orrecton Smlar to fake postve case, there mght be some fake negatves n order to decrease the reputaton level of a partcular communty. Ths could happen when a communty or a set of communtes would lke to weaken a partcular communty (by droppng ts reputaton level) hopng not to compete wth them. However, one unque case should not be excluded n whch, a partcular communty would mal-behave and after certan number of provdng servces and obtanng negatve feedbacks, clams that the feedbacks were fake and do not reflect ts actual reputaton level. To avod such a stuaton, each communty s responsble to recognze a change n ts reputaton level and consequently report to g. Upon receved report, g would decde whether the negatve feedbacks were really as a result of the mal-behavor of the communty or as a result of some other partes fake negatves. If g ntates the nvestgaton at tme t 1, after a perod of evolutonary tme, g would decde for the reputaton adjustment at tme t 2. In case of redeemng the communty that was suspected to have fake negatve feedbacks, the negatves are dscarded (Rep [t0,t1] s not consdered), and a reward Rw t 2 s assgned at tme t 2. The reason s to dscourage the opponent communtes not to cause a fake negatve feedbacks for and hope to degrade ts reputaton level. However, f after evolutonary nvestgaton, g decdes to penalze, then the negatve feedbacks are also consdered (reputaton s computed by Rep [t b,t 1] ), and a penalty P n t 2 s assgned to the communty. Equaton 8 computes the updated reputaton value of the communty (Rep *[t b,t 2] ). { Rep *[t b,t 2 ] αrep [t b,t 0 ] = + Rw t 2, f redeemed; αrep [t b,t 1 ] P n t Theoretcal Analyss f penalyzed. (8) In ths secton, we would lke to dscuss n detals the updates of reputaton level when a partcular communty 5

6 Fgure 4. The tree of backward nducton reasonng. causes fake feedbacks that eventually s benefcary for tself. To ths end, we follow the steps over ths reputaton updates and elaborate g s actons on them. For smplcty reasons, here we only analyze the case of self-postve feedbacks and generalze our dscusson to fake negatve feedbacks. We objectvely assume that penalzng a communty s relatve to the reputaton mprovement that communty had obtaned. In ths secton, we use backward nducton reasonng technque to show that WSs loose nterest n dong malcous acts that cause extra (fake) postves for themselves or extra (fake) negatves for some others. To better analyze the ntentons the communtes could follow, we calculate the expected reputaton value of a partcular communty n the case that the communty acts malcously to provde fake postve feedbacks for tself and the case that the communty acts as normal and performs ts actual capabltes. By comparng the two expected values, the typcal communty wll decde ether to act malcously or as normal. Let q t be the probablty that the controller agent g notces the real ntenton of the communty and take actons wth penalzng at tme t. We compute the expected reputaton of as a result of a malcous acton n equaton 9 and as a result of normal acton n equaton 10. E(Rep *[t b,t 2 ] faked) = q t 2 (αrep [t b,t 0 ] P n t 2 ) + (1 q t 2 )(αrep [t b,t 1 ] ) (9) E(Rep [t b,t 2 ] notfaked) = Rep [t b,t 2 ] (10) Fgure 4 s the tree representng the backward nducton reasonng through actons of the communty and correspondng reactons made by the controller agent g n two steps. In ths fgure, IMP s consdered as the state that the communty has provded some fake postves ad thus mproved ts reputaton level. We also refer n ths fgure to P N as the state that the communty s fake acton s detected and thus penalzed by g. As t s llustrated, the communty that provdes fake postves, obtans an mprovement, whch could be followed by a penalty. Here we state that the probablty of g s detecton gven the fact that the has faked before s hgh. Therefore, f has been already penalzed, t s so hard to retalate and mprove agan. There s a slght chance that fakes and g gnores, whch comes wth a very small probablty. Thus, we compute the expected reputaton level of both cases and compare them. Defnton 1. Let Imp [t b,t 2] be the dfference between the adjusted reputaton (n the case where the communty s under nvestgaton) and normal reputaton (n the opposte case) wthn [t b, t 2 ],.e: Imp [t b,t 2 ] = Rep *[t b,t 2 ] Rep [t b,t 2 ] The followng lemma gves the condton for the penalty to be used, so that the communtes wll not act malcously. Lemma 1. If P n t 2 > 1 Imp [t b,t 2] q t 2 αrep [t 0,t 1 ], then communtes obtan less reputaton value f they act malcously and provde fake feedbacks for themselves. Proof. To prove the lemma, we should consder the condton true and prove that E(Rep *[t b,t 2 ] faked) < E(Rep [t b,t 2 ] Not faked). By smple calculaton we get: E(Rep [t b,t 2 ] Not faked) E(Rep *[t b,t 2 ] faked) = P n t 2 1 Imp [t b,t 2] q t 2 + αrep [t 0,t 1 ] The obtaned value s postve, so we are done. 6

7 Table 1. Smulaton summarzaton over the obtaned measurements. WS Type WS Densty WS Type WS QoS Ordnary [25.0%, 35.0%] Good [0.5, 1.0] Faker [25.0%, 35.0%] Bad [0.0, 0.5] Intermttent [25.0%, 35.0%] Fckle [0.2, 0.8] 5 Expermental Results In ths secton, we descrbe the mplementaton of a proof of concept prototype. In the mplemented prototype, WSs are composed of dstrbuted web servces, that are mplemented as Java c T M agents. The agent reasonng capabltes are mplemented as Java modules. The testbed envronment s populated wth two agent types: (1) servce provder agents that are known as web servces and gathered n a communty (we assume only one type of servce s provded and therefore consumed); and (2) user agents that are seekng for the best servce provded by a web servce. In general, the smulaton conssts of a seres of emprcal experments talored to show the adjustment of the WS s reputaton level. Durng the elapsng RUNs, web servces, masters and users (that are ntally actvated) buld ther prvate knowledge. Table 1 represents three types of WSs we consder n our smulaton: ordnary, faker and ntermttent. Ordnary communty acts normal and reveals what t has, the faker communty s the one that provdes fake feedbacks n support of tself, and the ntermttent communty s the one that alternatvely changes ts strateges over the tme. WSs contan a number of web servces, that offers a qualty of Servce (QoS). As t s shown n table 1, the QoS value s dvded nto three ranges. In each RUN, a number of users are selected to search for the best servce. Strctly speakng, users are only drected to ask WSs for a servce and thus user would not fnd out about the web servce that s assgned by the master of the communty. In order to fnd the best communty, the requestng user would evaluate the WSs regardng to ther reputaton level. Some tmes, the users are n contact wth some communtes that are very good for the user, so the users re-select them. If the user s rejected from the best selected communty, he would ask the second best (and so on) communty n terms of reputaton level. After gettng a response from a communty, the user agent would provde a feedback relatve to the qualty of the obtaned servce and the communty responsveness. The feedbacks are logged n the loggng mechansm that s supervsed by g. The accumulated feedbacks would affect the reputaton level of communtes. In other words, the communtes would loose ther users f they receve negatve feedbacks, by whch ther reputaton level s dropped. Web servce QoS ommunty Reputaton web servces QoS WS ndvdual QoS average QoS Average web servces QoS 8 web servces QoS p1 p Number of Runs 8.Rep WS ndvdual QoS average QoS Fgure 5. ommuntes overall qualty of servce vs. the number of smulaton RUNs. Takng nto account the general ncentve for the WSs to process most possble users, communtes n general, compete to ncrease ther reputaton level. Ths s done by colludng wth a user (or a small group of users) to provde consecutve postve feedbacks. In the emprcal experment, we are nterested to observe the over-run reputaton level of dfferent types of communtes and how fast and effcent the adjustment s performed by the g. Fgure 5 llustrates the plot of reputaton level for a faker communty 8. The upper plot represents the ndvdual QoS for the communty s assgned web servces. In ths plot the gray lne defnes the average QoS for the web servces. The most promnent feature of the plot s the comparson of the reputaton level wth the average of the communty web servces QoS. The average value s assumed to be the actual QoS for the communty. In general, there would be convergence to such value f the communty s actng n an ordnary manner (for 8 s 0.173). The lower plot llustrates the reputaton level of ths communty over the elapsng RUNs. Here we notfy that the master of a communty s responsble to assgn the web servces to the user requests. To ths end, normally the hgh qualty web servces are assgned frst untl they become unavalable, whch forces the master agent to assgn other lower qualty web servces. Thus startng the RUNs, 8 gans reputaton value (up to 0.313), whch s better than ts ndvdual average qualty of servce. In fgure 5 the peek P 1 defnes the RUN n whch the communty 8 s out of hgh qualty web servces. After passng ths pont, the reputaton level of ths communty s decreased. Fgure 6 llustrates communty 8 reputaton level n comparson wth an ordnary communty 6. 8 at pont P 3 decdes to provde fake postve feedbacks for hmself to ncrease self reputaton level. For the nterval of 19 RUNs, ths communty gans hgher reputaton level up to the pont P 4. The controller agent g, perodcally verfes the feed- 7

8 ommunty Reputaton Level average web servces QoS 8 average web servces QoS 61 P1 P P4 151 P Rep 8.Rep 6 socal reputaton level over tme 301 Number of Runs 8 socal reputaton level over tme Fgure 6. ommuntes overall qualty of servce vs. the number of smulaton RUNs. back logs, n order to recognze the malcous actons. At P 4 the controller agent g notces the malcous act of 8 and freezes the obtaned feedbacks n order to decde to penalze. Peek P 2 s the pont n whch the communty 8 s penalzed n hs reputaton level. After P 2 a drastc decrease n reputaton value s seen whch goes underneath 8 s average qualty of servce (up to 0.112). There s also a contnung but slower ncrease for the reputaton of the faker communty 8 that perssts long after the frst fake acton recognton. Thus, there appear to be strong restrcton effects, n whch eventually the faker communtes loose ther users. However, there s also an ongong effect of socal nfluence, n whch users doubt n communtes that have drastc decrease n ther reputaton level. 6 oncluson The contrbuton of ths paper s the proposton of a new ncentve-based reputaton model for communty of web servces gathered to facltate dynamc users requests. The reputaton of the communtes are ndependently accumulated n bnary feedbacks reflectng the satsfacton or dssatsfacton of the users beng servced by a web servce assgned va the master of the communty. The model represents a sound loggng mechansm n order to mantan effectve reputaton assessment for the communtes. The controller agent nvestgates the loggng feedbacks released by the users to detect the fake feedbacks as a result of colluson between a communty and a user (or a group of users), whch are provded n support of the communty. Upon detecton, the controller agent mantans an adjustment n the loggng system, so that the malcous communty would be penalzed n ts reputaton level. Our model has the advantage of provdng a sutable metrcs used to assess the reputaton of a communty. Moreover, the assessed metrcs are vald havng a sound loggng mechansm n the sense that communtes would obtan the ncentve not to fake and act as ther ordnary capabltes The proposed mechansm effcency s analyzed through a defned testbed. Our objectve for future work s to advance the assessment model to enhance the model effcency. In the loggng system, we need to optmze detecton process, tryng to formulate t n the sense to be adaptable to dverse stuatons. Fnally, we plan to extend the emprcal analyss n order to capture more results reflectng the proposed model capabltes. References [1] A.S. Al, S.A. Ludwg, and O.F. Rana. A cogntve trust-based approach for web servce dscovery and selecton. Proc. of the 3 rd European onf. on WS, pp , USA, EOWS [2] Z. Malk and A. Bouguettaya. Evaluatng rater credblty for reputaton assessment of web servces. 8 th Int. onf. on Web Informaton Systems Engneerng (WISE 2007), 14(2-3), Nancy, France, Decr [3] S. Rosaro, A. Benvenste, S. Haar, and. Jard. Probablstc QoS and soft contracts for transacton based Web servces. IEEE Int. onf. on Web Servces, pp , IWS [4] E. M. Maxmlen. Multagent system for dynamc web servces selecton. Proc. of 1 st Workshop on Servce-Orented omputng and Agent-Based Eng., pp 2529, SOABE2005. [5] R. Jurca, B. Faltngs, and W. Bnder. Relable QoS montorng based on clent feedback. Proceedngs of the 16th Internatonal World Wde Web onference (WWW07), pp , Banff, anada, May [6] E. M. Maxmlen, M. P. Sngh. onceptual model of web servce reputaton. SIGMOD Record 31(4): 36-41, [7] S. Elnaffar, Z. Maamar, H. Yahyaou, J. Bentahar, Ph. Thran. Reputaton of communtes of web servces - prelmnary nvestgaton. Proc. of the 22 nd IEEE Int. onf. on Advanced Inf. Networkng and App., pp , AINA2008. [8] Organzaton for the advancement of structured nformaton standards. Introducton to UDDI: Important features and functonal concepts. October [9] W. Yao, J.Vassleva. A Revew on trust and reputaton for web servce selecton. 1 st Int. Workshop on Trust and Reputaton Management n Massvely Ds. omp. Sys. TRAM2007. [10] S. Kalepu, S. Krshnaswamy, S. W. Loke. A QoS metrc for selectng Web servces and provders. Proceedngs. Fourth Internatonal onference on Web Informaton Systems Engneerng Workshops, pp , [11] E. M. Maxmlen, M. P. Sngh. Toward automatc web servces trust and selecton. Proceedngs of the 2nd Internatonal onference on Servce Orented omputng (ISO), New York, November [12] M. Ruth, and T. Shengru. oncurrency Issues n Automatng RTS for Web Servces. IEEE Internatonal onference on Web Servces, pp , IWS [13] J. Bentahar, Z. Maamar, D. Benslmane, and Ph. Thran. An Argumentaton Framework for ommuntes of Web Servces. In IEEE Intellgent Systems, 22(6): 75-83,