Qualification Evaluation in Virtual Organizations Based on Fuzzy Analytic Hierarchy Process

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1 Qualfcaton Evaluaton n Vrtual Organzatons Based on Fuzzy Analytc Herarchy Process Fan Zhang, Junwe Cao,*, Lanchen Lu, and Cheng Wu, Natonal CIMS Engneerng and Research Center, Department of Automaton, Tsnghua Unversty, Bejng, 8, P. R. Chna Research Insttute of Informaton Technology, Tsnghua Unversty, Bejng 8, Chna Tsnghua Natonal Laboratory for Informaton Scence and Technology, Bejng 8, Chna * Correspondng emal: jcao@tsnghua.edu.cn Abstract Conventonal grd nfrastructure mplements CA to ensure securty and authentcaton access control whch s not flexble, whle peer-to-peer applcatons are flexble enough but no evaluaton system to ensure members n a vrtual organzaton are proper and safe enough to corporate wth each other. In ths paper, we propose an easly extendable evaluaton system usng the Fuzzy Analytc Herarchy Process (FAHP) to quantfy the sutablty of request ntators who decde to jon a vrtual organzaton for fle sharng and transferrng. Eleven crtera, derved from lteratures revew and practcal applcatons, are selected to enrch the whole evaluaton framework. We have conducted an extensve expermental study to show the mpacts of mnor change of each crteron on fnal qualfcaton values. Smulaton results show that FAHP s effcent to evaluate fuzzy ntegrated factors and the system can be easly appled to dfferent scenaros wth mnor changes.. Introducton. Backgrounds Grd computng was motvated by enablng wdearea and cross-doman sharng of computatonal resource [] whch has nvolved nto cyber nfrastructure [] and cloud computng [,, 5] for revolutonzng scence and engneerng these days. One problem we should not shy away s how to guarantee all members n one Vrtual Organzaton (VO) safe and proper enough to corporate wth each other. Tradtonal CA and the related protocols are safe for authentcaton verfcaton, access control, etc. But t s not flexble to mplement large scale corporaton and no measurement can be made about whether t s proper for members of one VO to share resources. PP content delvery network[,7], whch s wdely used for fle sharng and transferrng, s flexble enough to allow any member to jon n and qut any vrtual organzaton at wll, but no evaluaton or safety model to keep VO from malcous attacks or potental hgh overload that probably leads to large scale collapse. Proper measurement should be made to evaluate the qualfcaton and sutablty of the request ntator thus lots of dfferent factors should be consdered. In the envronment of fle sharng and exchangng, request ntator wth lots of related fles are more prone to be the potental canddates. Those hghly overload network, such as frequently accessed stes are more concerned of the potental vrtual organzaton load whch mght collapse the whole system, thus the requests wth too large amount of fle exchangng are less lkely to be welcomed. What s more, those secure transactons such as e-aucton or busness that probably nvolves lots of anonymous peers should embed more trust or reputaton elements for far and safe actvtes. The lst goes on, and more crtera should be ntegrated nto the system to gan a complete evaluaton. Concluded from the analyss made above, dfferent envronments needs dfferent evaluaton crtera or even evaluaton models, t s a tough job to qualfy reputaton because many dynamc factors are nvolved [8], not less to say total framework to farly evaluate request ntators. Ths paper s not ntended to provde a method that sutable for all crcumstance, rather t mplements one method wdely used n ndustral and socal evaluaton FAHP, to gve a proper ntegrated framework to quantfy and farly score request ntators, whch s relatvely useful n promotng secure and effectve cooperaton n vrtual organzatons.. Prevous Related Work Secure-bndng scheme and trust ntegraton are

2 ntroduced n [9] to gude the securty upgrade of grd stes and predct the grd performance of large workloads. The performance workloads and securty problems are factors that we should frstly notced. Trust management n PP overlay network s fully nvestgated n [,,, ], although the lst I provde here s by no means exhaustve, the common feature of these work s usng values or scores to evaluate the reputaton of peers. Feedback system [] used n e-bay s now wdely accepted for e-transactons n real world and e-commerce trust evaluaton. Smlar feedback to evaluate trust s shown n L Xong and Lu Lan s work [] whch develops peer trust models. In [], smlar feedback systems wth dfferent parameters are used to schedule remote access to scentfc nstruments to get a hgh QoS. Credt evaluaton models [5] are used more than we can magne n CC and e-commerce. Such factors are also should be reasonably ncluded n the ntegrated evaluaton model. Potental malcous attack or vcous detecton should be fully nvestgated based on ther abnormal behavors. All these crtera are ncluded n my work and ther comparatve mportances are also carefully nvestgated by referrng to experts, related lteratures and combnng wth practcal applcaton scenaros. Detaled analyss can be referred to secton. Fuzzy Analytc Herarchy Process (FAHP) [] s wdely used to evaluate mult-crtera decson-makng problems that ncorporate unquantfable nformaton, ncomplete nformaton, non-obtanable nformaton and partal facts nto the decson model [7]. Although FAHP requres tedous computatons, t s capable of capturng a human s apprasal of ambguty when complex factors are consdered. It s used n locaton selecton [8], R&D project selecton [9], quanttatve performance for servo control systems [], etc. Although the method tself s essentally applcable to these domans, none research have ever been done on the qualfcaton evaluaton of members that ntend to corporate wth members n one vrtual organzaton n grd or peer-to-peer organzatons, ths paper fully nvestgate the characterstcs for fle sharng and transferrng, hope to establsh far and proper judgment framework to make qualfcaton evaluaton. The rest of ths paper s organzed as follows. We present fuzzy theory and analytc herarchy process n Secton. Secton ntroduces the crtera we choose and the weght vector. Ther mpacts on the fnal score are ntroduced n Secton and the trend of ther nfluence are clearly depcted n the smulaton Fgure. We conclude the paper n Secton 5 and propose some future work.. Prelmnares. Fuzzy theory Fuzzy theores are used to descrbe those concepts that are not easly and clearly descrbed. For example, t s dffcult to show the clear bound between cold and hot wth tradtonal defnte or exact fgures. Fuzzy mathematcs gves us good method to analyze those fuzzy concepts. Defnton: Let A be a fuzzy set of doman U, x U μ, ( x A ) [,] A ( ), the value of μ x s member- shp grade or degree of membershp. The correspondng mappng μ : U [,], whch map A μa : x μa( x) s membershp functon. For example, U can be vewed as a reasonable regon of a common person s age, say, from to. x s an actual age value of a specal person such as, then A can be the fuzzy concept such as young or old. Based on our common sense, a person whose age s should be vewed as young much more than that be vewed as Old. So we can use the related age.9 μ age = =.. value μ ( = ) = and ( ) young Member functons can be mplemented to descrbe the mappng fuzzy concept to ts fuzzy value whch s between and. Member functon μ A ( x ) quantfes the membershp s grade of the element x to the fuzzy set A. It can take varous forms. In our applcaton, three forms n fgure and are shown here. Member functons are the prmary cause that descrbe dfferent values affect the afflatons of each other, the detaled member functons are followed ntroduced. Ther correspondng member functons can be categorzed nto two types. For those factors whose ncrease results to the fnal qualfcaton value ncrease, such as fles relevance, average recommendaton members, prevous average credt score number, and pc or network avalablty rate, ther related member functons are:, x s ( x s r = ), s ( ) < x s s s, x > s old

3 r r j = x s ( j+ ), s ( ) < x [ ( ) ] s s j j j s + j+ s( j ) x, sj x < s ( ) j =, j s( j ) s j, x s( j ) or x s + ( j), x s ( s x), s ( ) < x s s s, x > s = r Fgure. Fuzzy member functon of monotonous ncreasng crtera For those factors whose ncrease results to the fnal qualfcaton value decrease, such as traffc jam rate, abnormal behavor, potental vrtual organzaton load. Ther related member functons are:, x s ( s x r = ), s ( ) < x s s s, x > s x s ( j ), s ( ) < x [ ( ) ] s s j j j s j s( j ) x + rj =, sj x < s ( ) j =, j+ s( j ) s + j, x s( j ) or x s ( j+ ), x s ( x s r = ), s ( ) < x s s s, x > s r r j r Fgure. Fuzzy member functon of monotonous decreasng crtera x s the characterstc value of the th evaluaton r j r S S X S (j+) S (j-) X S S X S S X S (j-) S (j+) X S (j-) S X factor, s j s the standard value of the th evaluaton value whose rank s j.. Analytc Herarchy Process The Analytc Herarchy Process (AHP) was frst proposed by Thomas L. Saaty n the 97s to help people deal wth complex mult-crtera decson problems. The AHP provdes a comprehensve and ratonal framework for structurng a problem, for representng and quantfyng ts elements, for relatng those elements to overall goals, and for evaluatng alternatve solutons. Nowadays, t s more generalzed and applcable to lots of decson makng problems. Its basc dea s dvdng a complex problem nto several component elements, group and cascade them to form herarchcal layng structure, then make par wse comparson to get the comparatve mportance between all par elements, combng wth people s judgments to get total order. Weght vector, egenvalue and characterstc ndces can also be derved. Four steps are used to mplement ths method: () Constructng herarchcal structure to smplfy complex problems. () Constructng sngle element par comparatve weght matrx. () Consder consstency of weght matrx () Evaluate element compound weght to form order. Suppose our ntal sngle element par a comparatve weght matrx s A= ( aj ) =, n s the n n a number of elements. a and a j are the mportance of elements and j respectvely and a j s the par comparatve mportance of to j. The weght of all elements s computed lke ths: () Get ntal weght of all elements by n * ω n = aj, =,,, n j = () Normalze ntal weght to get normalzed weghts * ω by ω = n, =,,, n * ω = In the evaluaton system, t s mpossble to a calculate the exact value precsely, what we can do a j s to provde an estmate of t. Consequently, the error s unavodable. We can use the followng steps to get f the matrx A s proper for us to do further analyss. () Get column elements sum j

4 n by Sj = aj, j =,,, n = () Compute the maxmum egenvalue by n λmax = ωs = λmax n () Compute Consstency Index (CI) by CI = n. () Compute more relable ndex (CR) CI by CR = RI s the rectfed value n fgure. RI, If CR s less than., the weght s not well and thus an adjustment of ntal matrx A should be mplemented untl the condton s satsfed. On the other hand the computed weght s ok and next analytcal steps can be furthered. n 5 RI n RI Fgure. RI of n dmenson matrx Detaled steps of FAHP can be referred to some related papers.. Evaluaton crtera and models The evaluaton model of qualfcaton values s fully nvestgated n ths secton to evaluate the sutablty that whether current request of jonng one vrtual organzaton s allowable. Together wth that, we should predefne some basc concepts used n our crtera. () Fles Relevance: The request ntator who wants to jon one VO should provde the typcal fles t needs and resources that t offers back to the VO. So the fles or resource relevance s one factor needs consderng. Logs on request ntator s hstorcal fles sharng nformaton are clearly recorded and should be explcted provded just lke the credt that wll be ntroduced n the followng crteron. Request ntators wth hger fles revelence are prone to get hgher acceptance rate snce they can also provde lots of related fles n retuen. The unts of ths crteron can be derved from the percentages of related fles that logs descrbe. () Credt: It descrbes the trustablty of all members n the envronment, whch ranges from to and dynamcally changeable after a request ntator jonng one VO based on the VO credt. Detaled algorthm of the credt change s depcted n lots of related papers, whch are omtted here. () Prevous Credt Condton: As descrbed n (), Credt of one member s dynamcally changed and the prevous credt value shows the hstory credt nformaton of one ntator and should be clearly consdered as one factor of ts qualfcaton. () PC or Network Avalablty Rate: To ensure consstent and stable envronment n VO for fle sharng and accessng, avalablty rate of request ntator and ts network stuaton s also a factor should be consdered. Together wth that, another two factors, bandwdths and network traffc jam rate are also should be ncluded and consdered. (5) Abnormal Behavor: Malcous attempts of nvadng or hackng VO should be detected and excluded outsde whch s essental to make sure secure and safe network envronment. Queryng Frequency, especally those that of a relatvely hgh rate are more reasonable consdered as malcous attempts and should be negatvely consdered as factors to assst ntator s request. () Potental Vrtual Organzaton Load: Too much load s harmful to the consstent flow of one VO. Thus the more resources the ntator requres, the less qualfcaton value t s prone to get n the end. All these crtera should be fully consdered to evaluate whether the current member s approprate to jon a current VO. However, the sgnfcance or mportance of them are relatvely dfferent and should be judged farly. Frstly ntal judgment s made par wsely to get matrx ( a j ), then based on the method 7 7 provded above n AHP, we should check f the matrx ( a j ) proper for us to calculate the normalzed 7 7 weght. Thus our result are λ = 7., CI =., CR =.<. max whch shows the matrx s approprate enough. Then the total normalzed weght of all seven crtera could be calculated below. Fgure shows the weght of seven crtera of the elements we choose. Together wth that, ther sgnfcance are also ranked by orderng ther weghts, whch shows that prevous credt nformaton of the request ntator s of the most mportant factor snce ts weght s much larger than others. The reason s obvous that trustablty s the frst crteron we should consder n our practcal envronment. Furthermore, recommendaton nformaton, whch demonstrates the relatonshp between the request and the members of the VO s also mportant snce t s another profle to show trustablty. What s more, PC status, whch gves the avalablty rate, should also reasonably be treated as one mportant factor snce frequently jon and qut VO s really burdensome and consumes too much resource, whch s one cause of potental VO load n crtera 7.

5 The herarchcal structure of the elements and ther weghts n our AHP analyss are provded n fgure 5. We could fgure out that credt nformaton of the request ntator s together wth recommendaton nformaton are of the most domnant factor snce they totally account for over. weght of the whole. Ths concluson could be made n that current envronment needs secure and trustworthy members that n our VO to cooperate wth each other. What s more, PC Status, whch demonstrates the stuaton of the request ntator s also mportant snce corporaton needs Crtera. Recom. Info. Prev. Credt Info.8. PC Status 5. Network Status. Abn. Behavor 7. Pot. VO Load Weght. Potental Beneft Rank 5 7 Fnal Qualfcaton Evaluaton Potental Beneft:.5 Recommendaton Info. :. Prevous Credt Info. :.8 PC Status:.5 Network Status:. Abnormal Behavor:. Potental VO Load:. stable avalablty of all members. If otherwse, n generally PP envronments that stress more on the fles sharng and transferrng, the potental beneft together wth PC and network status are the most mportant factor snce effectve and effcent fle sharng should be guaranteed. To evaluate dfferent knds of envronments, we need not change the framework of the whole structure; nstead we can only change the par wse comparson to get the approprate weght to evaluate the related scores. Fles Relevance:.5 Relevant fles rate:.5 Average Recom. Members:.5 Average Recom. Credt:.5 Prevous Average Credt:.8 Avalablty rate:.5 Avalablty rate:. Bandwdths:. Traffc Jam Rate:.8 Queryng Frequency:. Requred Resource amount:. Fgure. Weghts and ranks of the seven crtera. Smulaton Results Normalzed weghts calculated above shows us the general mportance of all dfferent crtera, whch s our fundamental base to analyze the fnal score and judgments. We choose every second rank ndex as our basc unt to show the mpact of whch on the fnal score. There are totally groups of results. One group contans two graphs; the frst shows the comparson of the worst result wth the best result. The red star lnes are the results that we assume all other condtons are worst, whch s easly spotted that the results are n a very close narrow range that more than zero. On the contrast, the blue round lnes are results that corresponds to all other condtons that are best, whch s the same easly spotted from the facts that they are n a very close narrow range that less than one hundred. Also, we can also see the red and blue lnes are the same pattern; the reason s very obvous that all member functons that we select to determne the fuzzy concept are lnearly, whch does not change the structure of the lnes. That s also the same reason why the second graph n every group s pecewse contnuous. We have to stress that the blank space between the red star lne and blue round lne s fully occuped, snce the two lnes are two end extremes of each graph. The second graph n the same group shows the detal characterstcs of the score that change wth the value of the current ndex. groups correspond roughly to the second rank ndex and the values that out of the range of the gven x axs are ether or, thus they are omtted here. Scores(-) Fgure 5. Normalzed weghts of all crtera Potental Benft --> Fles Relevance Strongly Relevant Relevent Not too Relevent Not Relevent Fles Relevence

6 .5 Potental Benft --> Fles Relevance Recommendaton Informaton --> Recommendaton members rate 9 8 Scores(-) Strongly Revelent Revelent Not too Revelent Not Revelent Fles Relevence Scores(-) Recommendaton members rate Recommendaton Informaton --> Recommendaton members rate Fgure. Impact of fle relevence Fgure above shows the mpact of the fles relevance to the total scores. It corresponds roughly to our fnal normalzed weght that the potental beneft of t s.5. Fles relevance share.5 of the weght that should have maxmzed mpact value s.5 theoretcally. From our smulaton result, the exact value s. whch s very close to the estmated value. The same rule apples to fgure 7 that the estmated value theoretcally s.5 and practcally from our smulaton result t comes to. whch s completed the same as n fgure 5. The reason s that they share the same frst rank weght.5. Scores(-) Potental Benft --> Relevant fles rate Scores(-) Scores(-) Recommendaton members rate Fgure 8. Impact of recommendaton members rate Recommendaton Informaton --> Recommendaton members average credt value Recommendaton members average credt value Recommendaton Informaton --> Recommendaton members average credt value Scores(-) Relevant fles rate Potental Benft --> Relevant fles rate Relevant fles rate Fgure 7. Impact of relevant fles rate Scores(-) Recommendaton members average credt value Fgure 9. Impact of recommendaton members average credt value The same analyss apples to fgures from to. Prevous Credt Condton --> Prevous average credt score 9 Fgure 8 shows the mpact of recommendaton members rate. It s the percentage of the recommendaton members to the total members n one VO. Theoretcally the value s.5 snce the weght of recommendaton nformaton s. and the recommendaton members rate occuped half of t. In our smulaton the score s.95. The lttle margn s stem from the round off of the normalzed weght.. Scores(-) Prevous average credt score

7 Scores(-) Prevous Credt Condton --> Prevous average credt score Prevous average credt score Fgure. Impact of prevous average credt score Scores(-) Network Status --> Bandwdths 8 Bandwdths(Mbps).8.7 Network Status --> Bandwdths Scores(-) PC Status --> Avalablty rate Avalablty rate Scores(-) Bandwdths(Mbps) Fgure. Impact of network bandwdths Scores(-) PC Status --> Avalablty rate Avalablty rate 9 Fgure. Impact of pc avalablty rate Network Status --> Avalablty rate Scores(-) Network Status --> Traffc Jam rate Traffc Jam rate Network Status --> Traffc Jam rate.5 Scores(-) Scores(-) Avalablty rate.. Network Status --> Avalablty rate Traffc Jam rate 9 Fgure. Impact of traffc jam rate Abnormal Behavor --> Queryng Frequency Scores(-).8.. Scores(-) Avalablty rate Fgure. Impact of network avalablty Queryng Frequency(Num/s)

8 Scores(-) Scores(-) Scores(-) Abnormal Behavor --> Queryng Frequency Queryng Frequency(Num/s) Potental Overlay Network Load --> Requred Resource amount 8 8 Requred Resource amount(mb) Potental Overlay Network Load --> Requred Resource amount Fgure 5. Impact of queryng frequency 8 Requred Resource amount(mb) Fgure. Impact of potental vrtual organzaton load The trend of fgure to are dfferent from prevous fgures n that the factors we consder here, such as traffc jam rate, queryng frequency and requred resource amount are factors that negatvely affect the total scores. 5. Conclusons The man contrbuton of ths paper s mplementng fuzzy analytcal herarchy process nto evaluatng and qualfyng the score of all request ntator s values, whch s used effectvely n mult-vo overlay content network, especally n PP envronment when one member belongs lots of overlay network. Dfferent VOs have ther own evaluatng standard and mportant crtera, thus we can easly transfer one applcaton to another by redefnng the par wse weght ntroduced above, so t s easly extendable. Fuzzy analytcal herarchy process s easly carred out to satsfy all these applcatons. Further, algorthm to compute the credt value of all members n the envronment s smplfed to ft for the FAHP applcaton. It can be more complcated f we consder global credt and local credt [9], but the essence s more or less the same. Future work can be done from the followng three aspects. () More complcated features or crtera ought to be consdered to satsfy all dfferent stuaton. () Independence of all crtera we consdered are presumably to be satsfed n advance. More analyss can be taken from whether two or more factors, say, the credt of the recommendaton members and request ntators are nterrelated. Malcous attack or potental cheatng s consdered n ths paper, more ways of cheatng should be also consdered to ensure a secure and safe envronment for fle sharng and transferrng. Acknowledgement Ths work s sponsored by Mnstry of Educaton of Chna under the qualty mprovement engneerng program for hgher educaton, and Mnstry of Scence and Technology of Chna under the natonal 8 hgh-tech R&D program (grants No. AAZ7, No. 7AAZ79 and No. 8AAZ8). References [] I. Foster and C. Kesselman, The Grd: Blueprnt for a New Computng Infrastructure, Morgan-Kaufmann, 998. [] D. E. Atkns, K. K. Droegemeer, S. I. Feldman, H. GarcaMolna, M. L. Klen, D. G. Messerschmtt, P. Messna, et. al., Revolutonzng Scence and Engneerng through Cybernfrastructure. Natonal Scence Foundaton Blue - Rbbon Advsory Panel on Cybernfrastructure,. [] E. Hand, Head n the cloud, nature 9, 9(7). [] R. Ramakrshnan, Cloud Computng Was Thomas Watson Rght After All? In Proceedng of th IEEE Internatonal Conference on Data Engneerng, 8. [5] Wkpeda, Cloud Computng, Feb. 8, 8, [] K. Ross, D. Rubesten, PP Systems, Slde Presentaton n Tutoral, Infocom,, HongKong. [7] M. Hofmann, L. Beaumont, Content Networkng, Chap., Peer-to-Peer Content Networks, Kaufman 5. [8] S. Song, K. Hwang, R. Zhou and Y.-K. Kwok, Trusted PP Transactons wth Fuzzy Reputaton Aggregaton, IEEE Internet Computng, Vol. 9, No., pp. -, 5. [9] S. Song, K. Hwang, and Y.-K. Kwok, Trusted Grd Computng wth Securty Bndng and Trust Integraton, J. Grd Computng, vol., no., 5 [] P. Resnck and R. Zeckhauser, Trust among Strangers n Internet Transactons: Emprcal Analyss of ebay s Reputaton System, The Economcs of the Internet and E-commerce, M.R.Baye, ed., Elsever, pp. 7 57,. [] R. Guha et al, Propagaton of Trust and Dstrust, In Proc. ACM World Wde Web Conference (WWW ), ACM Press, pp.,. [] S. Buchegger, and J.-Y. Le Boudec, A Robust Reputaton System for PP and Moble Ad-hoc Networks, In Proc. nd Workshop Economcs of Peer-to-Peer Systems,.

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