Joural of Theoretcal ad Appled Iformato Techology 0 th Aprl 204. Vol. 62 No. 2005-204 JATIT & LLS. All rghts reserved. ISSN: 992-8645 www.jatt.org E-ISSN: 87-395 AN ALGORITHM ABOUT PARTNER SELECTION PROBLEM ON CLOUD SERVICE PROVIDER BASED ON GENETIC KANG YANFANG, 2 NIE GUIHUA Wuha uversty Of Techology, School of ecoomcs, Wuha Hube, 430070,Cha 2 Wuha uversty Of Techology, School of ecoomcs, Wuha Hube,430070,Cha Emal : 5462649@qq.com, 2 egh@mal.whut.edu.c ABSTRACT Amg at choosg sutable cloud servce provders to costruct a dyamc allace ad the satsfyg termal customers more effcetly, to acheve the optmal allocato of cloud servces. Ths paper uses gray relatve comprehesve evaluato model to determe the optmzg dex of cloud servces market. We use the mult-objectve optmzato model to study parter selecto problem quatfcatoal ad expla the model by geetc algorthm. Selected the provder the cloud computg market to offer the computg servces, storage servces, software servces as a research object, extract the cost, respose tme, qualty of servce as a research dcators. Mult-objectve plag was chaged to a sgle objectve by weght, the model s to be solved by geetc algorthm.though the best ftess value, the provder fd the parters wth the terests of the varous cloud servce provders. Fally, a example show that the algorthm s ratoal to solve the problem about fdg the best cloud servce provder parters, ad t s the valdty of the model ad algorthm. Keywords: Cloud Servce Provder, Cloud Computg, Parters Selecto, Grey Relatoal Aalyss,Geetc Algorthm, Mult-Objectve Optmzato. INTRODUCTION Cloud computg s oe of the popular, fashoable words owadays. Uder the form of global ecoomc tegrato, the problem of how to reduce the operatg costs IT eterprse cotuously growg. Cloud computg as the emergg commercal calculato model, the model the computg servce task s to be dstrbuted the calculato of server resource pool through the etwork. The storage servce, computg servces, software ad frastructure servces s to be accessed o-demad accordg to varous applcato systems o the Iteret. I ths cotext, the cloud servce provder was bor. Cloud servce provders, just as ts ame mples, s the eterprse for provde servce to the ed user, through the ew busess model such as the cloud computg. As the more ad more cloud servce provders to eter the market, how to choose the sutable cloud servce provders as the parters s to cause our atteto. The cloud servce provders obta complemetary formato resources, order to hghlght ts advatages the market, wll form a partershp combed wth ther cloud servce provders owed the same terests. Based o the cloud servces market has ts ow specal servce mode. Whe select the cloud servce provder parters, t eed to cosder ts uqueess - ablty 225
Joural of Theoretcal ad Appled Iformato Techology 0 th Aprl 204. Vol. 62 No. 2005-204 JATIT & LLS. All rghts reserved. ISSN: 992-8645 www.jatt.org E-ISSN: 87-395 of rapd elastcty, o-demad self-servce, broad etwork access, shared resource pool, measurable servce. Therefore, The parter selecto problem o cloud servces market attract much atteto from over the world. As the survvors of cloud servces market the etwork evromet, order to form the desred purpose of the ecoomc cooperato, to form a good partershp, ad at the same tme to form the optmal allocato of resources the cloud servces market, besdes there s the compettve ablty of the cloud servce provder ad the compatblty betwee the cloud servce parters. Above all the factors fluece the crtera of the parter selecto. So the dffcult pot of ths paper s determe the reasoable optmzato dex ad elmate the terferece the process of formato choce the cloud servces parter selecto problem. The problem of parter selecto dyamc allace,t has attracted the terested of a few scholars to study dfferet areas at home ad abroad. Camarha-Matos, Cardoso [] proposed the basc framework of the vrtual eterprse parter selecto ad descrpt ts fucto detal. Zha Su, Poul d. [2], the bass of qualtatve aalyss, puts forward some prcples o the maagemet of the relatoshps betwee parters. T. Srvas ad R. C. Baker [3] rase the model about the two stage parter selecto process, ad t s cosdered that the large amouts of quattatve factors ad overlooked some qualtatve factors them odel. Korho - e P [4] research the feld of formato maagemet system, he make the I-depth aalyss o the parter selecto of vrtual eterprse. MarcoFscher, Hedrk Jah, Tobas Tech [5] dscussed the vrtual eterprse parter selecto problem producto etworks based o the at coloy optmzato theory. W H Ip, M Huag, Yug K L, Dgwe Wag [6] studed the eterprse parter selecto of the egeerg project problem the vrtual evromet. Ad the above research rarely volve the allocato problem of resources optmzato the cloud servces market, the dex selecto about the optmzed allocato of cloud servces resources, ad the specfc optmzato strategy o mplemetato. The overall goal of the paper s the optmzed allocato rate for the cloud resources, based o the comprehesve evaluato model of grey correlato aalyss to select the correspodg optmzato dex, solve mult-objectve programmg problem to fd the best parter of cloud servces by the way of usg geetc algorthm, ad fally to verfy the effectveess ad ratoalty of the algorthm ad the model by the case. 2. THE PROBLEM AND THE MODEL 2. The Problem Descrpto The parter selecto problem about the dyamc allace of the cloud servce provders: I order to select the cloud servce provder parters, to form the dyamc allace, ad to buld the correspodg dex system of dyamc allace. Ths artcle assumes that the overall goal of dyamc allace s to acheve the optmal allocato of the cloud servces market resources, ad to mprove the ecoomc utlty of resources. It select the optmzed dex by the comprehesve evaluato model of grey correlato aalyss [7] [8]. The followg s evaluated ad aalyzed: x ( k ) 0 It should set the referece sequece,whch s the factor sequece about the optmzed allocato rate for cloud servce resources. The x ( k) s the compare sequece that s dex factor uder optmzato. Amog them k,2,...,,,2,3,..., m, The factor here s the 226
Joural of Theoretcal ad Appled Iformato Techology 0 th Aprl 204. Vol. 62 No. 2005-204 JATIT & LLS. All rghts reserved. ISSN: 992-8645 www.jatt.org E-ISSN: 87-395 chage pot of the target sequece. Based o the theory of the grey relatoal aalyss, t has establshed the followg formula: () mm x0 ( k) x ( k) + ρ maxmax x0 ( k) x ( k) l k k ξ( X0( k), X( k)) x ( k) x ( k) + ρ maxmax x ( k) x ( k) 0 0 0 k ξ ( X ( k), X ( k)) s the grey relatoal coeffcet about the rate of the cloud servce resources optmzed allocato. (2) ξ( X 0( k), X ( k)) r( X 0( k) X ( k)) k The ξ ( X 0( k), X ( k)) s the dex factor of the Servce for optmzato ad the correlato about the rate of the cloud servce resources optmzed allocato. It embodes the fluece measure about the optmal allocato of resources of the dex factor. (3) ξ ( X 0, X ) δ ( X 0, X ) N ξ ( X, X ) 0 The δ ( X 0, X ) s the relatve weght measure of the optmzato allocato rate for the cloud servce resources owed by the dex factor about the optmzed servce. It reflects the relatve mportace of the dex attrbute belog to the optmzed allocato of resources. Through the above theoretcal aalyss, It ca determe the top three optmzato dex factor measured by the relatve weghts accordg to the above formula (3).That s () the cost factor provded by the requred servce, (2) the factor of the reacto tme requred by the servce, (3) the attrbutes factors of the servce qualty. So the optmzato goal of ths artcle s the servce costs (C) geerated by the each cloud servce provders allace, cludg the ew software servce for the establshmet of the dyamc allace (saas), the platform servces (paas), the frastructure servces (Iaas) the eed to cost. The requred reacto tme (T) geerated whe the cloud servce s provder jotly offer the servce to the termal customer the dyamc allace. The servce qualty (Q) provded by cloud servce s provder the dyamc allace. So based o the demad of the above goal, the followg factors should be cosdered as the cloud servce provders: the cost of servce, the servce respose tme, ad the qualty of servce. These factors ca be expressed objectve fucto. So f chose the cloud servce provder parters, the purpose s to optmze the objectve fucto, ad makes the dyamc allace group s more compettve, ad t ca brg more beefts to members of the allace, so we thk t s the mult-objectve optmzato problems to select the proper cloud servce provders. The parter selecto problem about cloud servce provders ca be descrbed as: Assumg that the core of dyamc allace should provde servce to the termal users of.to the specal task, there are m cloud servce provders to offer servces to customers, so our optmzato goal s to complete each task wth selectg the best sutable parter, the problem ca be descrbed as the followg: The task w { wr } [ w, w2, w3,..., w ], r,2,, For the task there s caddate parters as follows: p { p } { p, p,..., p }, j,2,,m r r r 2 rm The caddate parters p [ c, t, q ] represet the cost, respose tme ad qualty. 2.2 Establsh The Mathematcal Model Start wth the basc model of mult-objectve optmzato [9] : G( x) m [ g ( x ), g ( x ),..., g ( x ) ] s. t. f r ( x ) 0 2 227
Joural of Theoretcal ad Appled Iformato Techology 0 th Aprl 204. Vol. 62 No. 2005-204 JATIT & LLS. All rghts reserved. ISSN: 992-8645 www.jatt.org E-ISSN: 87-395 Amog them: f r (x )---c o stra t c o d to ; g r (x)---objectve fucto For the parter selecto problem of the cloud servce provders, order to guaratee the effectveess of choosg the parters, the followg objectve fucto that the dyamc allace s composed of to be met. () Defe the 0- varables: f choose the p θ { r j 0 otherwse p stad for the task r completed by the caddate parters j Amog θ j,2,3...m r (2) defe the objectve fucto Accordg to the problems descrbed above, the selected servce cost, respose tme ad servce qualty as the three objectve fucto: Objectve fucto : the dyamc allace, the mmum cost C of parter caddates for cloud servce: mc(the prce of CP (cloud provder r for servce j). Mmze obj- m j r C θ Objectve fucto 2: the dyamc allace, the mmum respose tme T of parter caddates for cloud servce: MT: (3) The soluto of the mult-objectve optmzato model: For the mult-objectve optmzato problems, the mult-objectve optmzato fucto ca be coverted to the sgle objectve optmzato fucto, so the followg objectves ca be used for ths form: Mmze g (x) wc + w 2 T-w 3 Q w s stad for the weght. It determes the k emphass o the composto of each attrbute the dyamc allace. 3 k w (4) The costrat codtos: Oe parter s chose at least by oly oe cloud servce provders. 3. THE APPLICATION OF ANALYSIS 3. The Backgroud Of The Case It should be cosdered the dyamc allace that the cloud servce provder wll provde the followg three servces: the computg servces (computg), the storage servces (storage), the software, servces to the termal customer ad the umber of caddate parters s 3, 3, 5.The related data attrbutes of the caddate parters s see table : The cloud servce provder that provde the above three kds of servces cloud servces s amed A,2,3, B,2,3, k Mmze obj-2 m j r T θ Objectve fucto 3: the dyamc allace, the best qualty Q of parter caddates for cloud servce: maxq: Maxmze obj-3 m j r q θ C,2,3,4,5. The task s to fd the best cloud servce provder caddate that completed the above task cooperato. The Specfc data ca be see after the artcle the appedx table. 3.2.To Solve The Model 3.2. Determe the weghtg factor To determe the weghtg factor W, 228
Joural of Theoretcal ad Appled Iformato Techology 0 th Aprl 204. Vol. 62 No. 2005-204 JATIT & LLS. All rghts reserved. ISSN: 992-8645 www.jatt.org E-ISSN: 87-395,2,3. To determe the weghtg factor [] should follow some certa prcples: () the prcple of usablty, (2) the prcple of maeuverablty, (3) the prcple of objectvty, (4) the prcple of comparablty, (5) the prcple of comprehesve. Set X{X,X 2, X m } as the research object. It s the selected cloud servce provder, G {G,G 2,...,G }, For the dcators of each object to measure. 2 dex weght vector. W { W, W,..., W } T for the Wj. j Wj 0,( j,2,..., ). A [ a ] a stad m for the object of study. That s to say the value that the cloud servce provders X r to the dex G j, because the coutg dmeso of each dcator s dfferet, so we should stadard the decso matrx, The decso-makg matrx A s bee stadardzed [2]. I order to determe the weght of each dex, t create the followg models: s.t. amog j max (,,..., ) Z Z Z2 Z m W j W j 0,( j,2,..., ). z r a -a w * j j, * j+ a j-a w j value" of G j..i practce, * a j s the "Ideal * a j max{a j,a 2j, a mj }.The meag of Z r s the dstace betwee the research object X { a w, a 2w2,..., a w } ad the deal r r r r r soluto weght vector, makes the Z,Z 2,...,Z maxmum. The am s to dstgush the research object largely ad to fd the optmal soluto clearly. Adopt the method of lear solutos for the above model: max Z z s.t. j a -a w * j j r * r j + a j -a w j Wj W j 0,( j,2,..., ). By the above model, we get the cocluso thatw { W, W2,... W }. 3.2.2. The quattatve process of qualtatve dcators about the qualty of servce (Qos) () The avalablty: The ablty that the cloud servces provded uder the prescrbed codtos ad the costrat tme ca be accessed by the user successfully; you ca use the followg formula to express: QoS avalabty A/ N Amog them A for the umber of servces provded that successful vsted, N for the umber of total vsts by the customers. (2)The relablty: The ablty that the applcato servces offered stable by the cloud servce provders. It ca be represeted by the followg formula: QoS relablt y / R M, R for the umber that the servce mplemeted successful, M for the total umber that the servce called[3]. (3)The scalablty: The ablty that dyamc scalg wth the eeds cloud servce, t ca be expressed by the followg formula: X { a w, a w,..., a w }. Fd a * * * * r r r 2 2 r r 229
Joural of Theoretcal ad Appled Iformato Techology 0 th Aprl 204. Vol. 62 No. 2005-204 JATIT & LLS. All rghts reserved. ISSN: 992-8645 www.jatt.org E-ISSN: 87-395 QoS scalablty k RS k, K for the total umber of requred servce, RS for the success of the servce call [4]. If successful, the RS RS 0.,the (4) The tegrato: The ablty that the cloud servces ca be provded by the system whch ca perform well wth the applcatos systems belog to the eterprse customers, wth a average score of the user sad, QoS t egrato AS.Although the score that a sgle user ratgs s subjectvty, but whe the umber of users s large, the average score s credble, AS represets a sgle user ratgs, represets the geeral users [5]. 3.2.3. The cost detals The specfc cost detals of the flow ca be see the appedx table 2,after the paper. 3.2.4 The problem about computg the qualty of servce(qos) Accordg to secto 3.2., t ca determe the weghtg factor o: The weght of the servce qualty of sub-layer: ω 0.3, ω2 0.27, ω30.22, ω 40.20 Qos ω QoS + ω QoS + ω QoS The specfc attrbute value after the weghted Qos ca be see the appedx table 3. 3.2.5. The processg of data stadardzed Because the dfferet dcators s dfferet dmeso, t s dspesble to stadard these dcators. wth X {X, X 2,... X } for the raw data, Y {Y, Y 2,... Y } for the processg data, mx to the lowest value of a set data, maxx to the largest value of a set data. The process of Stadardzato: X m X Y max X m X The ormalzed data ca be see the table appedx 4. 4. THE GENETIC ALGORITHM IMPLEMENTATION MULTI-OBJECTIVE MODEL OF OPTIMIZATION 4..The Process Of Based Geetc Algorthm follows: The basc steps of geetc algorthm are as Step. The code. For the 0- teger programmg, the varables wll volve oly adopt 0or, t s drectly codg wth bary. Step. The tal populato geerated. The startg pot of the optmzato s tal populato geerated. The sze of the tal populato determes the tal search space. We adopt the populato sze accordg the problem sze N 0. The tal populato s radomly geerated, to the cloud servce provders (such as computg servces), ts formula s as follows: x roud( rad), roud(( j value avalabty 2 relablty 3 scalablty x roud for teger, j geerated betwee[0,]. x ) * rad) rad for a radom umber Suppose that a dvdual codg s as follows: 0 0 0 0 0 0 0 0 230
Joural of Theoretcal ad Appled Iformato Techology 0 th Aprl 204. Vol. 62 No. 2005-204 JATIT & LLS. All rghts reserved. ISSN: 992-8645 www.jatt.org E-ISSN: 87-395 It s sad that we had chose the thrd provder of computg servces, the thrd provder of storage servce ad the frst provder of software provder. Step.Calculate the ftess. After the data stadardzed to the secto [0,]. The scope of the objectve fucto s g ( x) 3, So the moderate fucto s take by ftess x) g( x) + c c f 3.I practce, the value s 5 (, c. Step. 4The choce. The choce s to esure that the best dvdual of the cloud servce provders ca sert to the ext geerato of ew groups. Here we adopt the roulette method ad the method of the optmal reteto. The roulette method s calculated accordg to accumulate the ftess of dvdual, we ca use the formula s expressed as: ftess ft The Molecules for the ftess dvdual's ftess, the deomator for the sum of the ftess to the whole populato. The radom umber s geerated by the radom populato sze. ft rad ft, The dvdual of s If chose to partcpate the geetc operato. The optmum reserved strategy s to save the correspodg dvdual the curret optmal soluto that s ot to partcpate the ext geetc operato, after the other dvduals to partcpate the geetc operato, wth the dvdual stead of the dvduals wth the lowest ftess at ths tme. It ca esure that the umber of dvduals the process of populato evoluto s more ad more. Step. 5The crossover ad The mutato. For the problem of the cloud servce provder selecto, order to satsfy the costrat codtos, ad we adopted a strategy of overall cross betwee parttos. It wll be geerated radomly the three umbers wth the scope of [,], the the task s to see whch rage [, 3], [4, 6], [7,]the three data fall to, to detfy the part of the crossover operato to be doe. The probablty of crossover P 0.9, The varato s a multpot mutato, we also adopted the three radom umber geerated betwee [, ]. Accordg to the scope of data, we detty the code that eed to chage. The probablty of mutato s set accordg to the deas of the smulated aealg algorthm. I the early evoluto, the probablty of mutato s small. Alog wth the uceasg evoluto, the probablty of mutato s gradually crease. Step. 6 Repettve executo. Step.3-- Step.5Utl the termato codtos s satsfy, The termato codto s that the umber of teratos s less tha the maxmum umber of teratos. Here s the umber of teratost 50. 4.2The Aalyss About The Result Accordg to the model, we solve the problem wth the Mat lab programmg. The specfc results as show the fgure below: 23
Joural of Theoretcal ad Appled Iformato Techology 0 th Aprl 204. Vol. 62 No. 2005-204 JATIT & LLS. All rghts reserved. ISSN: 992-8645 www.jatt.org E-ISSN: 87-395 The value of the objectve fucto.2. 0.9 0.8 0.7 0.6 0.5 0.4 The dstrbuto of the tal populato The ftess The relatoshp betwee the ftess ad the umber of teratos 4.84 4.83 4.82 4.8 4.8 4.79 0.3 0.2 2 3 4 5 6 7 8 9 0 The Ital populato Fgure : The Ital Populato Dstrbuto 4.78 0 5 0 5 20 25 30 35 40 45 50 The umber of teratos Fgure 2: The Dagram Of Ftess Ad The Number Of Iteratos 0.9 0.8 The dstrbuto of the ftess terated of N tmes 0.22 The chage of the optmal soluto terated of N tmes The value of the objectve fucto 0.7 0.6 0.5 0.4 0.3 0.2 0. 2 3 4 5 6 7 8 9 0 The umber of teratos N 20 Fgure 3: The Fucto Value Dstrbuto Of Iterato The dstrbuto of the ftess 5 4.9 4.8 4.7 4.6 4.5 4.4 4.3 4.2 4. The dstrbuto of the ftess teratve of N tmes 4 2 3 4 5 6 7 8 9 0 The umber of teratos N 20 Fgure 5: The Average Value Dstrbuto Ad The Soluto Chages Iterated After 50 Tmes The average value of the target objectve fucto 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 The optmal soluto 0.2 0.2 0.9 0.8 0.7 0.6 0 5 0 5 20 25 30 35 40 45 50 The umber of teratos N 50 Fgure 4:The Dstrbuto 2 Of the Ftess Iterated 20 0 T m e s Tmes The chage of the average target fucto terated of N tm es 0. 0 5 0 5 20 25 30 35 4 0 4 5 50 T h e a v e ra g e v a lu e o f th e ta rg e t o b je ctv e fu c to Fgure 6: The Average Value Varato Of The Objectve Fucto 232
Joural of Theoretcal ad Appled Iformato Techology 0 th Aprl 204. Vol. 62 No. 2005-204 JATIT & LLS. All rghts reserved. ISSN: 992-8645 www.jatt.org E-ISSN: 87-395 Optmal Iterated After 50 Tmes Accordg to the secto 3.2.,we determe the factor weghts W 0.9,W 2 0.9,W 3 0.05. The best ftess value of the mult-objectve optmzato ca be see from the fgure 2 s 4.8387, so as to get the parter selecto results of the best optmzato: The computg servce A, The storage servces provders B, The software servces provders C 2, That s to say the best combato of dyamc allace s (A, B, C 2 ). We radomly select the objectve fucto value belog to ay group of 0 servce group to compare wth the best cloud servce provders (A, B, C 2 ), The results are show fgure 7 below: (A,B,C 2 ) Fgure 7: The Comparso Chart Of The Best Servce Composto It ca be see from the above, that the mmum value of the objectve fucto s the best servce group(a, B, C 2 ),correspodg to the rest radomly selected 0 servce groups. Thus t ca show that (A, B, C 2 ) s oe of the best group ths paper. 5. CONCLUSION The dyamc allace parter selecto of cloud servce provders s a mportat ad complex process. It s the key that we choose the sutable ad compettve parter the dyamc allace, ad optmze the cloud servces resource, ad t has very mportat theoretcal ad realstc sgfcace. Ths paper proposes a method of cloud servce provders to choose parters. We adopt the mult-objectve optmzato model, ad cosder the multple factors affectg cloud servce provders, use the geetc algorthm, to fd the best ftess value, fally t fd out the best cloud servces combato pla. Through the expermetal results show that the valdty ad practcablty of the method. I ths paper, t put forward the exploratory model for choosg the best parter of the cloud servce provders dyamc allace. The model provde emprcal research foudato for ths study. ACKNOWLEDGEMENT Fud project: The atoal atural scece fud projects (772043 & 7072077); Natoal scece ad techology support (202BAH93F04&203BAH3F0); The Fudametal Research Fuds for the cetral uversty (203-YB 07 ). REFERENCES [] L.M.Camarha-Matos, A fsarmaesh H. Selecto of parters for a vrtual eterprse. Ifrastructure for vrtual eterprses : etworkg dustral eterprses. Bosto (Lodo):Kluwer Academc Publshers, 999.PP.259-278. [2] S. Zha, D. Poul, Partershp maagemet wth the vrtual eterprse a etwork, Iteratoal Coferece o Egeerg Maagemet ad Cotrol(IEMC),August,996,PP.645-650. [3] S. Tallur, R. C. Baker, A quattatve framework for desgg effcet busess process allaces, Egeerg ad Techology Maagemet, August,996, PP.656-66. [4] P. Korhoe, K. Huttue, E. Elorata, Demad cha maagemet global 233
Joural of Theoretcal ad Appled Iformato Techology 0 th Aprl 204. Vol. 62 No. 2005-204 JATIT & LLS. All rghts reserved. ISSN: 992-8645 www.jatt.org E-ISSN: 87-395 eterprse-formato maagemet vew. Producto Plag. Vol. 28, No.7,990,PP.247-296. [5] F. Marco, J. Hedrx, T. Tobas, Optmzg the selecto of parters producto etwork. Robotcs ad Computer-Itegrated Maufacturg,Vol.20, No. 6,2004,PP.593-60. [6]W.H.lp,K.L.Yug, D.W.Wag, A brach ad boud algorthm for sub-cotractor selecto agle maufacturg evromet,iteratoal Joural of Producto Ecoomcs,Vol.2,No. 87, 2004, PP.95-205. [7] J. L, Y.C. L, L. Re, The algorthm o select ad evaluate supplers by grey relatoal theory,joural of computer applcatos ad software, Vol.2, No.8,2004, PP.90-93. [8] B. Su, L. Lu, F.T. Yag, The model of the eural etwork based o grey correlato aalyss, Joural of systems egeerg theory ad practce, NO.9,2008, PP.98-04. [9] W.B. Tu, L.X Zhag., Z.T. Fu,The health evaluato dex selecto model based o mult-objectve programmg rural ecosystem, Joural of systems egeerg theory ad practce, Vol.0, No.0,202, PP.2229-2236. [0] J. Yag, A research o vrtual eterprse parter selecto based o group decso model, Joural of statstcs ad decso, NO.6,20,PP.42-44. [] X.J. Lu, R.F. Zhag, A method of determg the weght o mult-objectve decso-makg,joural of shax teachers uversty (atural scece edto),vol.6,no. 09,2002,PP.20-22. [2]Amazo Elastc Compute Cloud (Amazo EC 2 )[OL].http://aws.amazo.com/ec 2,202 [3]W.Zhou, J.H.We, M.Gao, etal, A QoS preferece-based algorthm for servce composto servce-oreted etwork, Optk - Iteratoal Joural for Lght ad Electro Optcs, Vol.24,No. 20,203,PP.4439-4444. [4]S.J.Q, Y. Che, X.W. Mu, A Optmal Servce Selecto wth Costrats Based o QoS, Physcs Proceda, Vol.25,202, PP.2050-2057. [5] M.K. Jog, O.K. Chag, I.H. Kwo,Qualty-of-servce oreted web servce composto algorthm ad Plag archtecture. Joural of systems ad Software, Vol.8,No.,2008, PP.2079-2090. 234
Joural of Theoretcal ad Appled Iformato Techology 0 th Aprl 204. Vol. 62 No. 2005-204 JATIT & LLS. All rghts reserved. ISSN: 992-8645 www.jatt.org E-ISSN: 87-395 APPENDIX: The fucto of The Caddates for Aual The qualty of Servce (Qos) Provde the the cloud servce cost Respose tme (s) Avalablty Relablty Itegrato scalablty servce provder parters (RMB) (%) (%) (%) (%) The Computg servces The Storage servce A 745.04 9 92 93 89 90 A2 8000 5 95 93 96 97 A3 8002.26 7 94 94 90 92 B 636.32 3 98 99 97 96 B2 849.2 4 97 98 95 94 B3 8688.46 6 96 95 92 96 C 7800 8 95 96 94 93 The Software servces C2 5280 4 93 92 88 90 C3 6400 0 96 97 98 95 C4 7200 2 94 95 92 9 C5 5460 95 94 93 94 Table : The Related Data Of The Caddate Parters [0] (The source: the above data are for amazo,google,salesforce,mcrosoft,oracle,xtools, The cloud of the West lake, sa. ) Table 2: The Table Of Calculatg The Flow Cost The flow Frst a GB/moth Next b GB/moth Next c GB/moth Next d GB/moth The cost C Per GB C 2 Per GB C 3 Per GB C 4 Per GB 235
Joural of Theoretcal ad Appled Iformato Techology 0 th Aprl 204. Vol. 62 No. 2005-204 JATIT & LLS. All rghts reserved. ISSN: 992-8645 www.jatt.org E-ISSN: 87-395 Table 3: The Attrbute Value Table After The Weghted Qos The fucto of Provde servce The Caddates for the cloud servce provder parters The attrbute values of Servce qualty A 9.200 The Calculator Servce A2 95.0800 A3 92.7200 B 97.6500 The Storage servce B2 96.2300 B3 94.8500 C 94.6500 C2 9.0300 The Software servces C3 96.500 C4 93.2300 C5 94.0900 Table 4: The Tables Of Data Normalzed The fucto of Provde servce The Calculator Servce The Storage servce The Software servces The Caddates for the The Aual cost The qualty of cloud servce provder parters The Respose tme (s) (RMB) Servce A 0.0309 0.5455 0.0272 A2 0.39 0.88 0.68 A3 0.0595 0.3636 0.2553 B.0000 0.0000 B2 0.9364 0.0909 0.7855 B3 0.446 0.2727 0.5770 C 0.0020 0.4545 0.5468 C2 0.0000 0 C3 0.0025 0.6364 0.8278 C4 0.005 0.882 0.3323 C5 0.005 0.7273 0.4622 236