CCDEA: Consumer and Cloud DEA Based Trust Assessment Model for the Adoption of Cloud Services

Transcription

1 BULGAIAN ACADEMY OF SCIENCES CYBENETICS AND INFOMATION TECHNOLOGIES Volume 16, No 3 Sofa 2016 Prnt ISSN: ; Onlne ISSN: DOI: /cat CCDEA: Consumer and Cloud DEA Based Trust Assessment Model for the Adopton of Cloud Servces Svakam aa 1, Saravanan amaah 2 1 Department of Informaton Technology, PSNA College of Engneerng and Technology, Dndgul, Tamlnadu, Inda 2 Department of Computer Scence and Engneerng, VS Educatonal Trust's Group of Insttutons, Dndgul, Tamlnadu, Inda E-mals: rsvakam@psnacet.edu.n drectorrvsetg@rvsgroup.com Abstract: Knowng the trust level of cloud servce provders s a sgnfcant ssue n the feld of cloud computng for prvacy and securty reasons. The dea of ths paper s to buld up a Consumer and Cloud-Data Envelopment Analyss (CCDEA) trust assessment model for evaluatng cloud servces n two stages. In frst stage, the belevablty ndex of each cloud Consumer (C) s calculated. The second stage ncorporates Cloud-Data Envelopment Analyss (C-DEA) model for the trust assessment of cloud servces from the vewpont of cloud consumers. Several experments were conducted and the results were analyzed to show the stablty of our method n measurng the relatve effcency and effectveness of cloud servces through rankng mechansm. Keywords: Cloud computng, belevablty ndex, cloud servce, trust assessment, Data envelopment analyss, cloud theory. 1. Introducton In the current age of computng, nformaton s a maor asset. From the local area network to the currently avalable hghly connected nternet, the world s beng benefted from the easness of data storage and access. By cloud computng, resources such as hardware, networks, servers, storage, applcatons and nterfaces are provded as on-demand servces to customers. But, ths ntroduces data securty as a maor ssue snce ntruders and hackers are also enoyng technologes for ther securty-threatenng actvtes. Snce cloud consumers permt external sources to hold control of ther data, trust also becomes an mportant problem. Trust may be defned as determnaton and guarantee that the trustee wll perform n a specfc way as antcpated by the trustor. In a cloud envronment, cloud provders and cloud consumers should have mutual trust between them. In essence, earnng the trust of consumers s essental for provders for the sake of ther busness benefts. On the other hand, snce consumers leave ther data wth 52

2 provders, they have to know whether they can trust the partcular Cloud Servce Provder (CSP) or not. Moreover, f the provder seems to be trustable, consumers lke to know to what extent they can be trusted. Estmatng the trust ndex of CSP s a key ssue n the feld of cloud securty. Ths s due to the fact that cloud users leave ther valuable nformaton wth provders whose honest behavor matters a lot. Consumers often fnd dffculty when choosng cloud servces or cloud servce provders for ther needs. In addton to the cost, several parameters are nvolved n decdng the effcency of cloud servces. One consumer may look for securty, whereas another consumer may prefer lower cost. So, the purpose of ths paper s to apply Fuzzy-Data Envelopment Analyss (DEA) model to decde whch cloud servce s the most effcent one. DEA was proposed n [1] as a mathematcal multcrtera based programmng model to obtan relatve effcency scores of peer enttes (decson makng unts). It evaluates the effcency of decson makng unts relatve to other decson makng unts by processng multple unts to yeld multple outputs. It models a lnear programmng problem wth multple crtera to deal wth real-world engneerng problems whch requre effcency analyss. All the decson makng unts under consderaton should use same resources so that ther effcency can be measured unformly. In our work, we employ output orented method where we try to maxmze the effcency of cloud servce provders by keepng nput parameters as constant. Accordng to a gven set of cloud servce parameters, cloud servces have to be ranked from the vewpont of consumers. We represent these cloud servces as decson makng unts. The rest of the paper s organzed as follows: Secton 2 outlnes a bref overvew of prevous work on assessment of cloud trust and DEA. The concept of determnng parameters whch nfluence the cloud trust s dscussed n Secton 3. Secton 4 gves an ntroducton to cloud theory. Evaluaton of consumers belevablty ndex and rankng mechansm of cloud servces are explaned n Sectons 5 and 6, respectvely. esults of experments whch show the analyss of our mechansm are presented n Secton 7. At last, Secton 8 concludes our work. 2. Prevous work An access control method based on mutual trust s proposed n [2] through authentcaton and authorzaton usng ant colony optmzaton. For a mult-cloud envronment, a trust management plan s suggested n [3] whch several trust servce provders are used. These provders cooperate wth each other n evaluatng the trust of cloud servce provders. In [4], trust evaluaton s done based on completeness, audtablty and transparency. Smlarly, another approach s recommended n [5] to prefer trustable cloud servce provders by usng parameters such as audtablty and nteroperablty. Varous mechansms for trust assessment are explaned n [6]. It further explans the assocaton between ndvdual components of cloud envronment for measurng trust. An approach for workflow schedulng s conveyed n [7] by ncorporatng trust metrcs. Varous trust and reputaton models are dscussed n [8]. Further, the authors of [8] dentfed several mportant parameters n preparng consumers to udge the trustworthness of cloud 53

3 servce provders. In [9], a system for trust orented management s presented based on Bayesan networks. Ths system explaned how to ntellgently make opnon wth respect to publc frameworks. Several protocols were proposed n [10] to calculate trust n a clent wthn a mult-clent envronment. Instead of processng large number of messages, ths method uses small number of messages n trust calculaton. A trust-aware model s recommended n [11] by consderng two parameters called clusterng and typcal path length between nodes. Fnally, ths system concluded that the tghtly clustered network where the path length between nodes s as small as possble wll gve better results. A Cloud trust model proposed n [12] advses users n determnng trustable cloud provders based on varous trust attrbutes. A dynamc cloud based trusted schedulng s explaned n [13] usng Bayesan method. A tree structured fuzzy based trust model s developed n [14] to assess the trust value of cloud servce provders. Nash equlbrum based trust model s suggested n [15] for a cloud envronment usng game theory. In [16], Charnes, Cooper and hodes (CC) model and L, Jahanshahloo and Khodabakhsh (LJK) model are ntegrated for the analyzng the assessment of decson makng unts. A framework based on Analytcal Herarchcal Process s suggested n [17] to enable cloud consumers to apprase cloud provders usng varous characterstcs of cloud servces. A model for evaluatng publc cloud servces s suggested n [18] usng performance parameters. In [19], several tools for estmatng the performance of cloud servces are presented and analyzed. Organzatons such as [20] are also provdng analyss of cloud servces as a servce. An ntegrated fuzzy DEA technque s used n [21] to measure the effcency and effectveness of decson makng unts. Another fuzzy DEA approach s presented n [22] whch convert the DEA model nto parametrc model for evaluatng the relatve effcency of decson makng unts. Performance assessment of cloud servces s done n [23] usng DEA. Ths system s based on the low level attrbutes lke throughput, storage, and data transfer rate. A method s proposed n [24] whch detect both effcent and neffcent components usng data envelopment analyss. In [25], cloud trust s assessed usng cloud model for addressng randomness and uncertanty. Further Bayesan network s used to deal wth the dynamc nature of cloud servces. Several models have been analyzed s [26] for provdng secured servces n an applcaton layer. An approach based on fuzzy theory and ant colony optmzaton has been suggested n [27] for assessng the trust ndex of cloud servce provders. 3. Determnaton of cloud parameters Accordng to our another work explaned n [27], Fg. 1 shows that before avalng servces from a cloud servce provder, each consumer wants to know whether the CSP s trustable or not, and to what level. In order to make them to be aware of the trust level of servce provders, Trust-as-a-Servce (TaaS) layer s ncorporated. Based on the personal experence wth the current servce provder, the ratng n terms of Servce Level Agreement (SLA), performance and securty, s gven by the consumer and stored n the opnon store. Trust database s a repostory of trust 54

4 nformaton about CSPs. It contans trust scores as assessed from the feedback of other consumers. The consumer, who needs to know the trust level, can use ths nformaton after valdatng the belevablty of other consumers. Trust apprasal s done accordng to the proposed method and the trust ndex s calculated based on whch the consumer makes decsons regardng the ftness of cloud servce provder to hs/her requrements. Fg. 1. Model for cloud trust assessment The am of ths work s to propose a method for choosng the most feasble cloud servce. For ths selecton, a lst of parameters have been analyzed and screened as shown n Fg. 2. To measure the trust of a CSP, parameters are essental. We employed a bottom-up approach n ths parameter dentfcaton process. We ntally recognzed several basc parameters and then grouped together nto varous categores called SLA, Performance, Securty, and User opnon. Dynamc nature of cloud leads to confdentalty, ntegrty and avalablty ssues. In order to address these ssues successfully, all enttes of a cloud envronment should be free of dstrust and also they should renew themselves correspondng to the changes happenng n the cloud. Due to the dstrbuted structure of cloud, dverse securty tactcs are offered n the market. Choosng a sutable combnaton of securty approaches s a maor challenge. So, these parameters are placed under a category Securty. Another mportant concern from the vewpont of consumers s the relablty of cloud servces. Both centralzed and dstrbuted managements may experence complcatons n offerng servces wthout nterruptons. Apart from all these affars, nteroperablty, accountablty, flexblty, and agreement of regulatons (laws) become sgnfcant n the context of trust. These parameters are grouped together nto a category Performance. As organzatons prefer to take up cloud servces, servce excellence becomes an nfluental factor. Servce provders vary n terms of servce features and servce consumers also dffer n ther demands. Hence both of them try to establsh a scale of servce. Ths knd of barganng results n a concurrence called as SLA. SLAs are vtal to decson makers to properly fx promses for servce between the cloud consumer and the cloud servce provder. They gve drectons for takng decsons on what to look forward to and what to be aware of as SLAs are assessed. A healthy SLA targets to remedes, n spte of penaltes. Dependng on the requrements of ndvdual delvery model, SLAs must 55

5 be formed carefully. SLA factors are establshed accordng to the busness requrements of cloud consumer and cloud provder. For consumers, bandwdth, relablty, avalablty, trust, and bllng are dentfed as the comprehensve metrcs. For Provders, the requrements am to be competent to carry out the consumer requrements. Common factors nclude abandonment rate, average speed, turn-around tme, mean tme to recover, resource utlzaton, and network uptme. Whenever ssues arse between provder and consumer, a well-framed SLA should aspre to lessen ther loss. They are possbly uncertan n rescung consumers when they meet wth dsputes n cloud servces. So SLA plays crtcal role n measurng the trustworthness of cloud provders and hence become the most mportant parameter. It comprses of certfcaton, customer support and IDentty (ID) management. The categorzaton and groupng of trust parameters s shown n Fg. 2. Fg. 2. Classfcaton of trust parameters for a cloud envronment 4. Introducton to cloud theory Though probablty theory and fuzzy theory are wdely adopted n uncertan problems, they lag n representng vague and uncertan nformaton. Probablty theory employs normal dstrbuton to deal wth uncertanty. Unfortunately, the trust parameters are nterdependent n our model. Hence, the applcaton of probablty becomes vod. Smlarly, determnaton of membershp functons through qualtatve reasonng n fuzzy theory requres rgd numercal expressons. Ths necessty makes fuzzy less approprate. Hence, cloud theory (cloud model) s developed that ntervenes between fuzzy set and probablty dstrbuton. It deals wth uncertanty and encloses added nformaton to make clear nferences than that of conventonal statstcal methods under uncertanty stuatons. Ths model fnds ts applcatons n varous domans such as ntellgent automaton, decson makng, data mnng and etc. Hence, a cloud based trust assessment model s developed n ths work, wth reduced false rates. The purpose of cloud theory s to convert each qualtatve sgnfcance degree nto a normal cloud. Fg. 3 shows a normal cloud generated wth 1000 cloud droplets (drops). Smlarly, N number of related clouds can be developed wth the N-level scalng of normal clouds. A 5-level cloud system s shown n Fg. 4. In an 56

6 N-level scalng of normal clouds, numbers of -th normal cloud are represented by the unverse U. The unverse U of the N-level cloud s defned as U U. The dstance d between the centers of neghborng normal clouds for normalzed values 1 s calculated by d. The Expectaton (Ex ), Entropy (En ) and Hyper-entropy N (He ) of -th normal cloud are the three nput qualtatve sgnfcance degrees. N 1 Fg. 3. Cloud system wth 1000 cloud drops Fg. 4. Cloud system for 5-level evaluaton scalng The Expectaton (Ex) s the qualtatve mathematcal sgnfcance degree that contrbutes the central pont of the doman. In another words, t represents cloud s center pont. The Entropy (En) ndcates the margn of qualtatve sgnfcance that can be ncluded n the doman of evaluaton. In turn, t estmates the ambguty of qualtatve sgnfcance degree and dspersng lmt of each of the cloud drops. Hyper-entropy (He) or excess entropy or entropy of entropy s a parameter that gves the sgn of dsperson of cloud drops and the uncertanty assessment of entropy. These parameters, Ex, En and He, are calculated by the equatons from ( 1) to ( 3), respectvely. (1) 21 d Ex, 2 57

7 (2) d 3Ex d En, 3 (3) En He, 10 for = 1, 2,, N, where 1/10 s a lnearzaton coeffcent. Then these qualtatve nput sgnfcance degrees (Ex, En and He) are converted nto quanttatve output degrees by producng cloud drops n an N-level cloud. Each cloud drop s x x x,, x for p number of dmensons. Cloud drop s 1, 2 represented by p whch le wthn the nterval [Ex 3En, Ex + 3En] are consdered for the evaluaton and the remanng drops whch fall beyond ths nterval are reected. In our mplementaton, we consdered [0, 1] as the nterval of unverse. After convertng the qualtatve nputs nto quanttatve outputs, the proposed Consumer and Cloud-Data Envelopment Analyss (CCDEA) based trust assessment framework for a cloud envronment calculates the relatve effcency and effcency ndex of each cloud servce as explaned n Secton Stage 1: Evaluaton of consumers belevablty Ths level s prevously llustrated n our work [27] for checkng whether each cloud consumer s worth enough to gve opnon about trustablty of cloud servces. Whle consderng the feedback from peer consumers, ther belevablty s an mportant thng n decson makng. Ths level s mportant snce there may be several fraudulent users n cloud who may gve wrong opnon ether to ncrease or decrease the trustablty of a partcular cloud servce. Ths may lead to fluctuatons n the measurement of cloud trust. In order to avod ths problem, the belevablty of each cloud consumer s assessed so that opnon s collected from genune consumers only. The belevablty calculaton s a challengng ssue snce anybody can on and take part n the process of trust calculaton. Belevablty of peers s mprecse and dynamc wth respect to the changes n ther actvtes. Ther trust can not be measured usng crsp values. So, fuzzy theory, where lngustc labels can smoothly represent nterval values, can be adopted. Whle avalng servces from servce provders, cloud consumers behave exactly lke (artfcal) ants of ant colony algorthm. As they wsh to aval servces from cloud servce provders of hgh trust ndex, pheromone of ant colony algorthm can be used to represent trust ndex. Further, belevablty between cloud consumers s dentcal to the pheromone. So ant colony optmzaton can be appled to the trust measurement of cloud computng envronment. Out of four nput parameters, the frst three parameters are used to measure drect trust between consumer and CSP. User opnon s a fuzzy varable whose value ndcates the degree of recommendaton by another consumer. So, t s used to measure ndrect trust between consumer and CSP. From these drect and ndrect trusts, the overall trust s calculated. 58

8 , s Belevablty B, 0 B 1, of consumer by consumer, 1, m, ws k k k 1 (4) B B e( t), 0 N N k1 where N s the total number of servces avalable n a cloud computng envronment, w k, 1 k N, s the weght assocated wth k-th servce, B0 s the ntal value of belevablty assgned to any new cloud consumer, whch s usually zero, e(t) s an error at tme t, and s k, 1 k N, 1 m, ndcates whether k-th servce s avaled by consumer or not. It s expressed as 1 f consumer has avaled servce k, sk 0 f consumer has not avaled servce k. We nterpret the status B 1 as consumer has full belevablty on consumer and B 0 as consumer does not have any belevablty on consumer (Zero belevablty). Belevablty matrx B on C s an nterval-valued fuzzy matrx, whch s defned by a relaton C C and membershp functon B : CC Interval([0, 1]), and where Interval([0, 1]) s the set of closed subntervals wthn [0, 1]. elatve Belevablty B (C ) of each consumer C, and Belevablty Index BI(C ) of consumer (5) C are calculated for w k 1 m : m 1 B ( C ) B, m 1 1 m 1 (6) BI( C ) PB ( C ) B ( C ). Here, (7) m 1 1 P(B ( C ) B ( C )) s the possblty degree [28] whch s defned by P(B ( C ) B ( C )) y C y y y C B C B C max 0, B max 0, B where and B C y, y. Here, B C 1 y y and B C 1 y y B C y, y. Fnally, BI(C ), 1 m, are compared aganst the Belevablty Threshold (BT). If BI(C ) s greater than the BT value, opnon from consumer C s taken nto, 59

9 account for calculatng the trust ndex of CSP. Else t s neglected. Snce cloud consumer on and leave the cloud dynamcally and due to the change n the behavor of consumers, ther partcpaton n the process of assessng CSPs trust ndex s apprecated or neglected based on the up-to-date value of ther belevablty ndex. After calculatng the belevablty ndex of the target, t s compared aganst no belevablty (0) and full belevablty (1) values. If t s less than or equal to 0.2, the target s not beleved. Else, f t s greater than or equal to 0.8, the target wll be beleved and hence t can partcpate n the process of trust assessment. But f the belevablty ndex s between 0.2 and 0.8, an ssue of decdng whether to beleve or not to beleve arses. When one customer decdes to beleve the target customer and another decdes not to beleve, the belevablty of the target customer s affected. Consumers belevablty wll be evaporated gradually wth respect to tme. So we have used the next equaton for updatng t: B t 1 1 B t B t, t 1, (8) where, s an evaporaton factor, t, t 1 ndex from tme t to tme t 1 whch s calculated by B s the change n belevablty t1 t (9) B t t C C, 1 BI BI. Here, BI t (C ) s the belevablty ndex of consumer at tme t and BIt+1 (C ) s the belevablty ndex of consumer at tme t Stage 2: ankng mechansm of cloud servces by CCDEA Fg. 5 shows the order of executon of our work n the trust assessment of cloud servces. Assume that m represents the number of cloud servces. They make use of x x x,, x to generate an output vector an nput vector 1, 2 p y y y,, y 1, 2 q, where p and q represents the dmensons of nput vector and output vector, respectvely. Effcency ndces (Eff) of cloud servces are calculated by frst translatng qualtatve degrees nto correspondng quanttatve degrees. For postve nputs and outputs, the relatve Effcency (Eff k) of a Cloud Servce CS k, 1 k m, s calculated by a Lnear Programmng Problem (LPP): where q p k 1 k 2 k 1 1 Eff s y s x, s 2 and s 1 are the weghts of -th nput and -th output, respectvely. 60

10 Ths can be llustrated as Fg. 5. Flowchart for rankng mechansm of cloud servces Maxmze Eff subect to the constrants: q q Maxmze s y s x k 1 k 2 k 1 1 s y s x 1, l 1, 2,, m, 1 l 2 l 1 1 p s1 0, 1 q, s2 0, 1 p. If ths effcency ndex s equal to 1, ths cloud servce s relatvely effcent. Else, t s relatvely neffcent. Accordng to Charnes-Cooper and hodes varable transformaton, the above model s wrtten as the followng output orented LPP: subect to q max s y, 1 k 1 p q p s2 xk 1, 1 l 2 l s y s x 0, l 1, 2,, m, s1 0, 1 q, s2 0, 1 p. Effcency ndex of each CS s calculated by rerunnng the proposed method for n tmes. The average effcency ndex of -th cloud servce s n Eff Eff k n, = 1, 2,, m, where m s the number of the assessed cloud k1 servce provder, and Eff k s the effcency ndex calculated n k-th teraton for -th cloud servce provder. Once the effcency ndex calculaton of all cloud servces s accomplshed, they can be prortzed n the order of ther effcency ndex values. In our work, we consder SLA, Performance, Securty and User opnon as nput p 61

11 parameters (attrbutes) and trust of cloud servce as an output attrbute. Tables 1-5 show the cloud system of our nput and output attrbutes to convert qualtatve attrbute values nto quanttatve numbers. Smlarly, the relatve Effectveness (Effeck actual output/desred output) of a cloud servce CSk, 1 k m, s calculated by subect to 62 q 1 l l 1 1 r max Effec q r k s1 yk dk, 1 1 s y d 1, l 1, 2,, m, s1 0, 1 q, 0, 1 r, where d k s the -th desred output for k-th cloud servce, and assocated wth -th desred output. 7. Expermental results and dscusson s the weght To assess and demonstrate the effcency of our proposed system, we have smulated a cloud envronment wth the followng ten cloud servce provders: Amazon, Azure, Century Lnk, Cty-Cloud, Cloudera, Google Compute Engne, HP, IBM, OpenNebula, and ackspace. For each of them, 2 or 3 cloud servces are taken and the correspondng descrpton s shown n Table 1 for the total of 26 cloud servces n our experments. Table 1. Descrpton of cloud servces usng qualtatve values CSP Cloud Servce SLA Performance Securty User opnon C1S1 Weak Medum Medum Negatve C1 C1S2 Moderate Good Medum Neutral C1S3 Moderate Good Medum Postve C2 C2S1 Moderate Good Low Neutral C2S2 Weak Poor Medum Neutral C3S1 Strong Poor Medum Postve C3 C3S2 Strong Medum Hgh Postve C3S3 Moderate Medum Medum Neutral C4S1 Strong Medum Low Postve C4 C4S2 Weak Medum Medum Negatve C4S3 Moderate Good Hgh Postve C5 C5S1 Moderate Poor Hgh Neutral C5S2 Weak Medum Low Neutral C6S1 Weak Good Medum Negatve C6 C6S2 Strong Medum Hgh Postve C6S3 Strong Poor Hgh Postve C7S1 Moderate Good Medum Postve C7 C7S2 Weak Medum Low Negatve C7S3 Moderate Poor Low Negatve C8 C8S1 Strong Good Hgh Postve C8S2 Weak Poor Medum Neutral C9 C9S1 Strong Medum Hgh Postve C9S2 Weak Good Medum Neutral C10S1 Moderate Medum Low Negatve C10 C10S2 Moderate Good Hgh Postve C10S3 Strong Medum Medum Postve

12 Fg. 6. Belevablty ndex of good host Fg. 7. Belevablty ndex of bad host Fg. 8. Evoluton of trust wth respect to negatve user opnon 63

13 Fg. 9. Evoluton of trust wth respect to neutral user opnon Fg. 10. Evoluton of trust wth respect to postve user opnon 3D representatons of our results llustrate the progresson of trust ndex wth respect to the nput parameters SLA and performance, whle securty and user opnon are fxed. Fg. 8 shows that as securty ncreases, t affects the trust value postvely. For medum and hgher levels of SLA and performance, securty greatly nfluences the trust assessment. But for lower levels of SLA and Performance, securty does not gve a sgnfcant mpact on trust value. For medum and hgh valued securtes, we get dentcal trust values as maxmum values. But the dfference les n the membershp value of SLA parameter. For medum securty levels, we obtan maxmum trust value for hgher SLA levels, whereas the same maxmum trust value s acheved for medum and hgher SLA levels. Even though for hgh securty, the maxmum value for trust ndex s only 0.5 due to negatve opnon of customers. Ths means that the trust value cannot be mproved by these parameters alone. It gves maor mportance to user opnon. Snce Fg. 8 gves results for negatve opnon, trust ndex does not go beyond 0.5, even for the full membershp values of remanng parameters. On another sde, trust value does not drop below 0.3 for the full membershp values of SLA and Performance, even for low securty. Thus the sgnfcance of these parameters are compared to others s shown n Fg. 8. Fg. 9 and Fg. 10 show the progresson of trust evaluaton wth respect to SLA and Performance, where customers have gven neutral and postve opnons, respectvely. Even for zero membershp value of securty, ther trust value s unformly ncreased by 0.2 n Fg. 9 for neutral opnon and by 0.4 n Fg. 10 for postve opnon. Ths shows the sgnfcance of user opnon n the process of trust evaluaton. Ths provdes ustfcaton for the reason why we gve maor mportance n measurng the belevablty of customers who provde opnon or feedback about the effcent servce provson of cloud servce provders. 64

14 After evaluatng the belevablty of consumers, the decson makng system s gven the values for the nput parameters as shown n Tables 2-5 whch gve the 3-level scalng cloud system of our four nput parameters and Table 6 shows the 5-level scalng cloud system of our output parameter. Table 2. 3-level evaluaton scale cloud system of SLA Level Attrbute value Ex En He 1 Weak Moderate Strong Table 3. 3-level evaluaton scale cloud system of Performance Level Attrbute value Ex En He 1 Poor Medum Good Table 4. 3-level evaluaton scale cloud system of Securty Level Attrbute value Ex En He 1 Low Medum Hgh Table 5. 3-level evaluaton scale cloud system of User opnon Level Attrbute value Ex En He 1 Negatve Neutral Postve Table 6. 5-level evaluaton scale cloud system of CS trust Level Attrbute value Ex En He 1 Complete dstrust (Untrustworthy) Dstrust Weak trust Moderate trust Complete trust (Trustworthy) Table 7 shows how dfferent methods assess ranks for varous cloud servces based on effcency usng CC, LJK and Cloud-DEA model. Table 8 shows the ranks for the same based on effectveness. From the Tables 7 and 8, we understand that the ranks awarded by CC and LJK models often dffer from each other. Comparson between each par of methods n rankng s presented n Table 9 from whch we nfer that our proposed method acheves mnmum devaton and constant results wth respect to other methods. When we compare rankng based on effcency and effectveness ndces, we fnd consstency n the rankng of cloud servces wth the use of our proposed method as shown n Table 10. But the other two methods provde dfferent rankng for the same set of cloud servces wth the same set of resources. 65

15 Table 7. esults of comparson of cloud servces on effcency ndex Cloud CC LJK Cloud-DEA Servce Effcency ndex ankng Effcency ndex ankng Effcency ndex ankng C1S C1S C1S C2S C2S C3S C3S C3S C4S C4S C4S C5S C5S C6S C6S C6S C7S C7S C7S C8S C8S C9S C9S C10S C10S C10S Table 8. esults of comparson of cloud servces on effectveness ndex Cloud Servce CC LJK Cloud-DEA Effectveness ankng Effectveness ankng Effectveness ankng C1S C1S C1S C2S C2S C3S C3S C3S C4S C4S C4S C5S C5S C6S C6S C6S C7S C7S C7S C8S C8S C9S C9S C10S C10S C10S

16 Table 9. ate of dfference between three methods Crtera CC and LJK Cloud-DEA and LJK Cloud-DEA and CC Based on Effcency ndex Based on Effectveness ndex Table 10. esults of consstency comparson Method ate of devaton CC LJK Cloud-DEA Experments are conducted to measure the executon tme of rankng for dfferent number of cloud servces. Fg. 11 shows that, for small number of cloud servces, all the three methods are almost equal n terms of executon tme. But as the number of cloud servces ncrease, they exhbt a dfference. Further, even for 1000 cloud servces, Cloud-DEA method consumes about 6.9 s only. Ths shows the sgn of competence of Cloud-DEA for rankng cloud servces. Fg 11. Executon tme of varous methods for rankng cloud servces 8. Concluson In ths paper, we have proposed a CCDEA based trust assessment framework for a cloud envronment, where the belevablty of consumers s frst evaluated and then the trustworthness of cloud servce provders s assessed based on cloud theory and data envelopment analyss. Here, each cloud servce s symbolzed as a decson makng unt. By representng nput parameters usng a set of 3-level cloud system, trust of each cloud servce s evaluated by a 5-level cloud system. Then, cloud servces are ranked n terms of effcency and effectveness ndces. Smlar experments wth same set of resources are conducted usng CC model and LJK model to compare the results and to show the goodness of our proposed method. 67

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