, pp.247-258 http://dx.do.og/10.14257/jgdc.2015.8.2.22 A CPN-based Tust Negotato Model o Sevce Level Ageemet Cloud Evomet Hogwe Che, Quxa Che ad Chuzh Wag School of Compute Scece, Hube Uvesty of Techology, Wuha, Cha chw2001@sa.com Abstact The egotato pocess of the Sevce Level Ageemet (SLA) cloud evomet s a teacto pocess of access cotol ules ad cedetals. Due to the mass of cedetals cloud evomet, the egotato effcecy s ot hgh. To addess the poblem, ths pape poposes a Automated Tust Negotato (ATN) model based o Coloed Pet Net (CPN), whch exsts a legtmate occuece sequece ad a eachable state space gaph. A Mmal Cedetal Path Seachg (MCPS) algothm s poposed to fd a mmum cedetal dsclosue set the eachable makg gaph. The esults show that the algothm ca effectvely mpove the egotato effcecy. Keywods: Cloud Computg, Sevce Level Ageemet, Automated Tust Negotato, Cedetal set, Coloed Pet Net 1. Itoducto I cloud evomet, the Cloud Sevce Cosume (CSC) ca use esouces elastcally o the demad ad Pay-Pe-Use o Pay-As-You-Go; the ATN method [1-3] o SLA povdes a ew access cotol way to espod the dyamc esouce equest by equestg ad dsclosg dgtal cetfcates gadually betwee the Cloud Sevce Povdes (CSP) ad the CSC, fally to establsh the mutual tust elatoshp. Howeve, the pocess of the actual SLA egotato cloud evomet, each cloud sevce esouces' acqusto eques mass of attbute cedetal to dsclose, the complex access cotol ules ad the lage umbe of egotato hadshakes, whch make the egotato effcecy ot hgh dug the ATN pocess. Theefoe, t s ecessay to study the mmum dsclosue cedetal set to cotol the ATN pocess o SLA cloud evomet, whch educes uecessay cedetals to dsclose ad mpove the egotato effcecy. Wth the advatages of gaphc tutosm ad udestadablty, the CPN [4] s a effectve fomal modelg ad aalyss tool to geeate a secue ad cedble cedetal dsclosue set o SLA cloud evomet. Especally fo a complcated model wth a lage amout of data, the ATN stateges ca be expessed dsjuctve omal fom the CPN, whch ca smplfy geatly cloud evomet. I ths pape, places epeset cedetals betwee two egotato pates, ad tastos epeset the clause of access cotol polces. The essece of ATN s that two stage egotato pates exchage a sees of cedetals to establsh tust elatoshp. I fact, the pocess of ATN s to seach a cedetal dsclosue sequece betwee the esouce povde ad esouce equeste dstbuted etwok [5]. To mpove the attbutes secuty of use's detty dug tust egotato, Fedeca Pac [6] puts fowad a mmum cetfcate sequece method that two pates oly eed to exchage the ecessay popety cedetals. I ths pape, we povde a ATN example o SLA cloud evomet, buld a CPN-based model tems of the example, ad aalyze the CPN-based model accodg to legtmate cedetal sequeces, whch make esouce equest eachable. A eachable ISSN: 2005-4262 IJGDC Copyght c 2015 SERSC
state space gaph ca be mapped fom the CPN model, ad a algothm fo seachg mmum cedetal dsclosue set s put fowad O the bass of the eachable gaph. Ths pape s ogazed as follows: Secto 1 toduces the elated eseach o ATN cloud evomet. Secto 2 poposes achtectue of ATN o SLA cloud evomet ad descbes the fucto of each compoet befly. Secto 3 gves a cocete ATN example o SLA cloud evomet, ad pesets access cotol stateges betwee CSC ad CSP accodg to the example. Secto 4 maly descbes the CPN model ad the eachable state space gaph accodg to the example. The, Secto 5 puts fowad a algothm fo seachg the mmum cedetal dsclosue set ad aalyzes ths algothm, ad the smulato esults show that the algothm ca effectvely mpove the egotato effcecy. Fally, Secto 6 cocludes the wok. 2. Achtectue of ATN o SLA Cloud Evomet I cloud evomet, t s dyamc fo the CSC to use cloud esouces, so the CSP eeds a stadad SLA documet to maage cloud esouces wth the CSC. A SLA documet ca povde the CSC wth dffeet sevce levels, so the CSC ad the CSP eed egotate fo geeatg the SLA successfully befoe the CSP povde cloud esouces ad sevces fo the CSC tems of the SLA. Dug the pocess of tust egotato, the two egotato sdes dsclose cedetals ad access cotol polces gadually to establsh tust elatoshp. Ths secto descbes the achtectue of ATN o SLA ad the fucto of each compoet dug the pocess of tust egotato. The poposed achtectue of ATN o SLA s show Fgue 1, whch maly cludes thee compoets: the SLA documet, cloud esouces, ad the ATN module. The SLA documet cotas may egotato clauses, whch maly clude sevce level, sevce pcg, QoS dcato ad moto maagemet, etc. The CSC ca stat tust egotato tems of dffeet cloud sevce level, whch s coespodg to dffeet sevce pcg ad QoS dcato. Afte successful egotato, the CSP wll deploy cloud sevces ad povdes the cloud esouces to the CSC accodg to the SLA documet. The CSP ow cloud esouces ad povdes cloud sevces by boadcastg the sevce type ad sevce cotet to the CSC va esouce catalogs. Cuetly, cloud sevces ae dvded to thee categoes [7], amely Ifastuctue as a Sevce (IaaS), Platfom as a Sevce (PaaS), ad Softwae as a Sevce (SaaS). The ATN module maly cludes access cotol polces, cedetals, egotato stateges ad egotato potocol. Cedetal ad access cotol polces have played a mpotat ole potectg sestve fomato togethe. Both egotato sdes who pocess cedetals ca access ceta esouces by the dsclosue of the cedetals. I ode to guaatee successful ATN o SLA, cofdece teval s toduced to choose the most cedble cedetal to establsh tust elatoshp [8]. Oly whe tust values of the cedetals fall to the coespodg cofdece teval, the cedetal ca dsclose. Cedetals usually cota some sestve fomato, such as the ID of cloud sevce applcato, the esouce type ad access cotol polces, tust value ad vaous cotol paametes. Both egotato sdes hope to dsclose as lttle fomato as possble to each a SLA cotact fo cloud sevce. Negotato stategy decdes how to elease cetfcates ad access cotol polces [9-11]. Ths pape adopts the cautous egotato stategy, amely the egotato sde povde the elevat cedetal oly whe the access cotol polcy equest the ceta cedetal. I ode to mpove the egotato effcecy, t's ecessay to fd a set of mmum cedetals. The egotato potocol s esposble fo egotato sequeces of both sdes o the bass of egotato stategy. 248 Copyght c 2015 SERSC
Boadcast CSP Negotato esult Access Cotol Polcy Cedetals Sestve Ifomato Potecto CSC Negotato esult cofdece teval Cotol Negotato Stategy Negotato Potocol Resouces catalog ATN Sevce Level Request Sevce Level Cloud Resouces IaaS/SaaS/PaaS Sevce Pcg Deploymet QoS Idcato Moto Maagemet : : SLA Documet Fgue 1. The Achtectue of ATN o SLA Cloud Evomet 3. Access Cotol Polces o SLA Cloud Evomet The access cotol polcy o SLA the pape uses dsjuctve omal fomula, R D D... D, D C C... C, k. C epesets the cedetals that 1 2 k 1 2 m both egotato sdes eed dsclose. I ths pape, we gve a example, ad the scee s as follows. A egula CSC wats to et a ole maagemet softwae the cloud, ad the CSP povdes a dscout ( Dscout ( ) )to the egula CSC o those who et the cloud sevce (executo tme T) fo moe tha 3 yeas. I ode to potect the ow teests, t s ecessay to cay out a ATN to make a SLA documet befoe etg ths sevce. The CSC as a esouce equesto stats the SLA-based tust egotato pocess. I ode to avod beg deceved ad mpovg safety, fst of all, both egotato sdes eed dsclose the detty fomato. The CSP eed dsclose the cetfcate of the elevat pemts (CSP.c), ad the CSC eed dsclose the egsteed cetfcate (Use.ID ad passwod) ad popose the sevce paametes about ths maagemet softwae such as elablty( R el().level ), avalablty ( Av ( ). level ),ad sevce tme(t) to get a sevces dscout. Accodg to the achtectue of fgue 1, the cloud sevce the example belogs to the applcato of SaaS, the the SLA tust egotato betwee both sdes maly focus o the followg aspects: (1) Safety of the CSC ad the CSP. Befoe egotatg sevce paametes, both sdes eed vefy the authozato of the sevce ad pemsso, such as the Use. ID ad Passwod. (2) The sevce pcg. The budget of the cloud sevce s calculated as ceta pcples, such as dscout, sevce level (level) ad sevce executo tme T. (3) Cedblty of cloud esouces. The cedblty of the sevce popetes ca be dvded to thee levels: tust, ukow ad dstust. The CSC chooses the sevce level they wat, ad the cedblty s evaluated by the oveall tust value a peod of tme. Copyght c 2015 SERSC 249
(4) Relablty of cloud esouces, such as avalablty, elablty, ad mea tme to estoato (MTTR). Fgue 2 s access cotol polces fo the CSC ad the CSP accodg to above example. Access Cotol Polces fo the CSC: U s e. ID C S P. c E ( f ) P a s s w o d C S P. c R E ( f ) ID passw od N ( ). p c e B c D s c o u t ( ) E ( f ) N ( ). p c e A v ( ). le v e l A V ( ) E ( f ) A v ( ). le v e l R e l ( ). le v e l R E L E ( f ) R e l ( ). le v e l t ( ). le v e l T E.( f ) t ( ). le v e l M tt ( ). le v e l T u e CSP. c Use. ID Passwod E Access Cotol Polces fo the CSP: CSP. c ( f ) Sevce ( ) ( N ( ). pce Av ( ). level ) ( Rel ( ). level t ( ). level ) Mtt ( ). level E Sevce ( ) ( f ) AV ( ) N ( ). pce Av ( ). level E AV ( ) ( f ) REL ( ) N ( ). pce Rel ( ). level E REL ( ) ( f ) Bc Tue R Tue MTTR ( ) Mtt ( ). level E MTTR ( ) ( f ) T Tue Dscout ( ) Use. ID (( T 3) R )) Fgue 2. Access Cotol Polces fo the CSC ad the CSP 4. The CPN Model fo ATN o SLA Cloud Evomet 4.1. Mappg fom Access Cotol Polces to the CPN Gaph Whe a CPN gaph s mapped to access cotol polces, places epeset the lmted access esouces o cedetals, ad tastos epeset the dsclosue of cedetals, whch s a clause of access cotol polces. Wth Boolea opeato expesso, the logcal elatoshp betwee places ad tastos s defed as follows: (1) The flow elatoshp fom tastos to places s deoted as : R D D... D ; 1 2 k (2) The flow elatoshp fom places to tastos s deoted as: 1 2... (3) The tal upotected cedetal s expessed as: C T u e. D C C C ; Fgue 3 shows the mappg fom access cotol polces to the CPN gaph wth the CPN tool [12], ad C ( p ) { C, C } deotes the colo set of cedetals fom both S C egotato sdes. m 250 Copyght c 2015 SERSC
1 `( 2,4 ) M T T R ( ) t1 6 M tt().le v e l t1 5 t1 3 S e v c e ( ) t1 4 R e l().le v e l t1 1 t1 2 t( ).le v e l 1 `( 1,1 ) + + 1 `( 2,2 ) + + 1 `( 3,3 ) + + 1 `( 4,4 ) + + 1 `( 5,5 ) N ( ).p c e A v ( ).le v e l t1 0 R E L ( ) t7 t8 B c t6 A V () D s c o u t( ) IN T x D A T A t9 T IN T x D A T A IN T x D A T A 1 `( 1,1 ) + + 1 `( 2,2 ) + + 1 `( 3,3 ) + + 1 `( 4,4 ) + + 1 `( 5,5 ) 1 `( 1,"tu s t" ) + + 1 `( 2,"d s tu s t")+ + 1 `( 3,"u k o w " ) t5 C S P.c R (,d ) t2 IN T x D A T A U s e.id t1 t4 t3 P a s s w o d (,d ) (,d ) IN T x D A T A Fgue 3. Mappg fom Access Cotol Polces to the CPN Gaph C S { CSP. c, Bc, REL ( ), AV ( ), R, Dscout ( ), MTTR ( ), T } s a cedetal set fo sevce level. Bc deotes the colo set of dffeet sevce levels' budget to the CSC. The CSC should meet the demad of sevce budget ode to access a ceta level of cloud sevce. The place R epesets the colo set whch shows the tust level of cloud esouces. The tal tust level ca be obtaed dectly by boadcastg. T deotes the colo set of the executo tme. C C { Use. ID, Passwod, N ( ). pce, Av ( ). level, Rel ( ). level, Mtt ( ). level, t ( ). level } coespods to the cedetal set whch the CSC eed dsclose to access the cloud sevce the pocess of egotato, such as ID vefcato( U s e. ID, P a s s w o d ), sged sevce pce cetfcate( N ( ). p ce ), ad the sevce level of avalablty( A v ( ). level ), elablty( R e l.le v e l ), mea tme to estoato( Mtt ( ). level ), executo tme( t ( ). level ) ad dscout ( D sco u t () ). Of whch, Mtt ( ). level deotes the colo combato of mea tme to estoato, ad the cedetal accesso s ot estcted. C ( U tlty ) { (, )} s a applcato fucto, whch epesets a theshold fucto o a Boolea expesso to decde whethe dsclosg the access cotol polcy dug the tust egotato. The applcato fucto ca make the place chaged ad tasfe egotato to the ext cedetal, oly whe the tust value of the cetfcate meets the Copyght c 2015 SERSC 251
demad of cofdece teval. The applcato fucto vaes wth dffeet egotatos, ad the egotated fomato s C C C ( t ) C ( U tlty ). 4.2. Reachablty Aalyss of the CPN Model S C I ode to fd a cedetal dsclosue set fo accessg the equested cloud esouces, t s ecessay to aalyze the above CPN model. Due to the boudedess of the CPN, thee exsts oe o moe legtmate eachable sequeces. If a sequece fom the tal maks M 0 to the ed maks M F exsts, that s M F M 0, ad the sequece s eachable. I Fgue 4, all states ad chages ca be see fom a eachable makg gaph. Thee ae 3375 odes the state space mappg fom the CPN model, so Fgue 4 s oly a pat of eachable makg gaph fom the above CPN model. Each ode epesets a eachable makg, ad each ac epesets the occuece of a bdg elemet fom souce ode to destato ode. The eachable makg gaph ecods all chages of places ad tastos the CPN model, ad expesses the logcal elatoshp amog vaous evets. Each ode's fomato about the umbe of subsequet odes ad the pecuso odes s gve the gaph, such as the "1" ode "0:10" deotes t has 10 subsequet odes ad o pecuso ode. Whe egotato pocess stats, the eabled makg place makes a tasto, whle othe places ae watg state. If a ac fucto meets the equemets of a tasto, the the place makg coveys to the othe place. The eachable makg gaph Fgue 4 demostates the feasblty of the CPN model. Fgue 4. Pat of Reachable Makg Gaph fom the CPN Model 5. The Algothm fo Mmum Cedetal Dsclosue Set 5.1. The Mmal Cedetal Path Seachg Algothm I the pocess of actual tust egotato o SLA cloud evomet, each acqusto fo cloud sevce eques dsclosue of may attbute cedetals to the othe paty. Both egotatos sdes wsh to dsclose as lttle fomato as possble to acheve successful tust egotato o SLA cloud evomet, cludg some sestve 252 Copyght c 2015 SERSC
fomato o uecessay cedetals. I a example lke the oe llustated above, thee ae 14850 acs ad 3375 odes. The moe odes, the moe complcated CPN model, so t s ecessay to fd the shotest path to each the taget ode sevce () that s to fd a mmal cedetal dsclosue set. The followg s the pseudo code of the Mmal Cedetal Path Seachg (MCPS) algothm fo mmum cedetal dsclosue set: Table 1. The Mmal Cedetal Path Seachg (MCPS) Algothm Algothm 1: The Mmal Cedetal Path Seachg Algothm OPEN { S }; CLOSED ; w h le (! O P E N ) { } g e t _ m _ e v a lu a to ( O P E N ); f ( Sevce ( )) //Sevce() s the taget ode beak ; fo ( ea ch ch ld u fo m ) { } g u eva lu a te ( u ); f ( u O P E N ) { } fo c a lc u la te _ e v a lu a to ( O P E N ); f ( g u fo ) { } u. p a e t ; fo u p d a te _ e v a lu a to ( O P E N ); else f ( u C L O S E D ) co t u e ; e lse { // u O P E N && u C L O S E D } u. p a e t ; O P E N O P E N { u} ; C L O S E D C L O S E D { } ; s o t _ e v a lu a to ( O P E N ); m _ p a th u p d a te _ p a th ( O P E N ); m _ d s t u p d a te _ d s ta c e ( O P E N ); The MPCS algothm ths pape s o the bass of the heustc seachg algothm A*, whch s used to fd optmal path. A successful SLA egotato eeds kds of sevce qualty popety ad detty fomato to establsh tust elatoshp fo cloud sevce. As may cedetals exst the pocess of SLA egotato cloud evomet, cedetals wll be mapped to the CPN model as may odes. The MCPS algothm evaluates odes whch eed to be exteded, ad chooses the best ode; the cotues to exted the ode, utl to fd the taget ode sevce (). I the pocess of tust egotato cloud evomet, may cedetals eed to be classfed to educe the state space ad mpove the effcecy. I fact, classfcato s a clusteg poblem [13-14], so ths pape also caes o the cluste aalyss fo states of odes. Accodg to the chaactestc X of each ode state space gaph, the tal samples ae bulded. The ogal data matx has samples ad m dmesos, ad the matx fom s as show Fomula (1): Copyght c 2015 SERSC 253
x x... x 1 1 1 2 1 m x x... x 2 1 2 2 2 m X............ x x... x 1 2 m Fomula (2) s the stadadzato of stadad devato esult fom Fomula (1). x ' j 1 x j 1 1 ( x j 1 1 2 x j ) ( 1,2,...,, j 1,2,... m ) (1) x j 1 (2) Calculatg the smlaty j betwee the sample ad the sample j, ad costuctg the fuzzy smlaty matx R, 11 21 R 1 12 22 2......... 1 m 2 m m m, j 1 c x k x (3) jk k 1 I Fomula (3), c s a pope selectve paamete, ad the matx R s a symmetc matx. We ca judge whethe a uclassfed ode belogs to the th ode by meas of the followg fucto, x j 0, 1, j j, whch s the eghbo theshold. So, whe tavesg the gaph, the kow th classfcato theshold categoy. 5.2. Smulato Results ad Aalyss satsfes I ( x j ) /. If I, the ode belogs to the 1 I ode to vefy the valdty ad elablty of the Mmal Cedetal Path Seachg algothm, we coduct smulato test accodg to the above example. Thee exst 15 kds of cedetals the example, so the tusted odes ad the egotato goal (Sevce ()) ca classfy 15 categoes. Accodg to the algothm, fst of all, 3600 odes ae geeated adomly, the a smlaty matx s ceated, ad dffeet categoes epeset dffeet attbutes of tusted odes, such as elablty, cost budget, etc. I Fgue 5, t s about 15 kds of tusted odes' classfcato dagam, tusted odes' value falls to dffeet cofdece teval afte omalzato of the tust value, t s coveet fo the CSC to seach dectly fom a class to choose a elgble tusted ode ad tu to the ext class of tusted odes quckly. (4) 254 Copyght c 2015 SERSC
Rug tme(s) Cofdece Iteval Iteatoal Joual of Gd Dstbuto Computg 1 The classfcato of the cedetal 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Cofdece Iteval Fgue 5. Tusted Nodes Classfcato Dagam based o the MCPS Algothm Wth the ceasg of odes, the ac umbe ad teacto of odes become moe complcated, ad classfcato tme ases shaply, thus classfcato eeds a lage umbe of odes codto whe tavesg odes. We cease the umbe of odes gadually ad ecod the elapse tme that dffeet umbe of odes to be classfed. The smulato esult s show Fgue 6. Fom the smulato esult, we ca see that wth the cease of tusted odes, the classfcato tme ceases whe usg the MCPS algothm to tavesal all tusted odes. Whe the umbe of the odes s small (<600), the classfcato tme s shot, ad the classfcato tme ses obsevably whe tust odes become lage gadually. 30 Relatoshp betwee the umbe of odes ad tme cosumpto 25 20 15 10 5 0 0 500 1000 1500 2000 2500 3000 3500 4000 The umbe of odes Fgue 6. Relatoshp Dagam betwee the Numbe of Nodes ad Tme Cosumpto Copyght c 2015 SERSC 255
The shotest dstace to fd the odes Iteatoal Joual of Gd Dstbuto Computg Wth the cease of tusted odes, the shotest dstace both gow whe usg the MCPS algothm wth classfcato ad the BFS [15] algothm wth o classfcato. The MPCS algothm fo seachg the shotest dstace s less tha the latte. Fgue 7 shows compaso betwee the MCPS algothm ad the BFS algothm shotest dstace.i Fgue 7, the shotest dstace equals to the shotest path to sevce () that meas the least cedetal exchage. I cloud evomet, o the same codto of the same umbe of odes, the legth of shotest path geeated by the MCPS algothm s less tha that by the BFS algothm. 600 500 MCPS Algothm BFS Algothm 400 300 200 100 0 0 500 1000 1500 2000 2500 3000 3500 4000 The umbe of odes Fgue 7. Compaso betwee the MCPS Algothm ad the BFS Algothm Shotest Dstace 6. Coclusos Ths pape we popose a CPN-based tust egotato model o SLA cloud evomet. I the CPN model, a cedetal s egaded as a tusted ode, ad the cedetal wth a tust value fallg to cofdece teval detemes the coespodg sevce level. I cloud evomet, thee ae a lage umbe of cedetals eed to be exchaged, whch make the tust egotato effcecy ot hgh. Theefoe, we eed smplfy the complex model ad dsclose the mmum cedetals. The eachable makg gaph s the ma measue fo qualtatve aalyss ad quattatve aalyss the CPN model, whch s also the foudato of elated popetes aalyss. A Mmal Cedetal Path Seachg algothm s poposed to fd a mmum cedetal dsclosue set the eachable gaph. The esults show that the algothm ca effectvely mpove the egotato effcecy. I the futue, we wll make a futhe exteso fo the poposed achtectue of ths pape, ad buld the layeed CPN model o SLA, that adapts to the complex cloud evomet bette. Ackowledgemets Ths wok s suppoted by the Natoal Natual Scece Foudato of Cha (No. 61170135, No. 61202287, No.61440024), ad the Geeal Pogam fo Natual Scece Foudato of Hube Povce Cha (No. 2013CFB020, No. 2013CFA046). 256 Copyght c 2015 SERSC
Refeeces [1] A. V. Dastjed ad R. Buyya, A Autoomous Relablty-Awae Negotato Stategy fo Cloud Computg Evomets, 12th IEEE/ACM Iteatoal Symposum o Cluste, Cloud ad Gd Computg, (2012), pp. 284-291. [2] A. C. Squcca, F. Pac ad Elsa Beto, Tust establshmet the fomato of Vtual Ogazatos, Compute Stadads & Itefaces, vol. 33, o. 1, (2011), pp. 13-23. [3] D. Zou, S. Du, W. Zheg ad H. J, Buldg Automated Tust Negotato achtectue vtual computg evomet, The Joual of Supecomputg, vol. 55, o. 11, (2011), pp. 69-85. [4] P. Katsaos, A oadmap to electoc paymet tasacto guaatees ad a Coloed Pet Net model checkg appoach, Ifomato ad Softwae Techology, vol. 51, o. 2, (2009), pp. 235-257. [5] C. A. Adaga, S. De C. d Vmecat, S. Foest, S. Paabosch ad P. Samaat, Mmzg dsclosue of clet fomato cedetal-based teactos, Iteatoal Joual of Ifomato Pvacy, Secuty ad Itegty, vol. 1, o. 2, (2012), pp. 205-233. [6] F. Pac, D. Baue, E. Beto, D. M. Blough, A. Squcca ad A. Gupta, Mmal cedetal dsclosue tust egotatos, Idetty the Ifomato Socety, vol. 2, o. 3, (2009), pp. 221-239. [7] I. Muttk ad C. Bato, Cloud secuty techologes, Ifomato secuty techcal epot, vol. 14, o. 1, (2009), pp. 1-6. [8] Q. Ha, Y. L, R. Zhag, H. We, Y. Xe, X. Zhu, Y. Jag ad X. Guo, A P2P ecommeded tust odes selecto algothm based o topologcal potetal, 2013 IEEE Cofeece o Commucatos ad Netwok Secuty (CNS), IEEE Pess, (2013), pp. 395-396. [9] X. Dogme, Z. Guosu, H. Yu ad B. Yu, Aalyss of Automated Tust Negotato Polcy, 2010 2d Iteatoal Cofeece o e-busess ad Ifomato System Secuty (EBISS), IEEE Pess, (2010), pp. 1-4. [10] C. Ke, Z. Huag ad M. Tag, Suppotg egotato mechasm pvacy authoty method cloud computg, Kowledge-Based Systems, vol. 51, (2013), pp. 48-59. [11] A. V. Dastjed ad R. Buyya, A Autoomous Relablty-Awae Negotato Stategy fo Cloud Computg Evomets, Poceedgs of the 2012 12th IEEE/ACM Iteatoal Symposum o Cluste, Cloud ad Gd Computg (ccgd 2012), IEEE Pess, (2012), pp. 168-171. [12] L. Yoghao ad L. Yu, Reseach o Modelg of Multpaty Tust Negotato Based o Coloued Pet-et P2P Netwok, 2010 Secod Iteatoal Cofeece o Netwoks Secuty Weless Commucatos ad Tusted Computg (NSWCTC), IEEE Pess, (2010), pp. 437-441. [13] P. Jag ad M. Sgh, SPIC: a fast clusteg algothm fo lage bologcal etwoks, Bofomatcs, vol. 26, o. 8, (2010), pp. 1105-1111. [14] M. Polczysk ad M. Polczysk, Usg the k-meas Clusteg Algothm to Classfy Featues fo Choopleth Maps, Catogaphca: The Iteatoal Joual fo Geogaphc Ifomato ad Geovsualzato, vol. 49, o. 1, (2014), pp. 69-75. [15] M. Kuat, A. Makopoulou ad P. Tha, Towads ubased BFS samplg, IEEE Joual o Selected Aeas Commucatos, vol. 29, o. 9, (2011), pp. 1799-1809. Authos Hogwe Che, 2006, he gaduated fom Najg Uvesty of Posts & Telecommucatos ad eceved PHD degee Cha, majoed Commucato ad Ifomato System. He s a assocate pofesso at School of Compute Scece Hube Uvesty of Techology, Wuha, Cha. Fom August of 2013 to Febuay of 2014, he was a academc vstg schola at Temple Uvesty USA. Now hs majo study feld s Pee-to-Pee Computg, Cloud Computg ad SDN. Quxa Che, She s fom Guagx Povce of Cha, ad a maste caddate at School of Compute Scece Hube Uvesty of Techology, teested Cloud Computg. Copyght c 2015 SERSC 257
Chuzh Wa, She s fom Hube povce of Cha, PHD, pofesso ad dea at School of Compute Scece, Hube Uvesty of Techology. She s teested Pee-to-Pee Computg ad etwok secuty. She s a membe of CCF, ACM ad IEEE. 258 Copyght c 2015 SERSC