SaaS Resource Management Model and Architecture Research

Transcription

1 Sed Orders for Reprits to The Ope Cyberetics & Systemics Joural, 2015, 9, SaaS Resource Maagemet Model ad Architecture Research Ope Access Zhag Xiaodog 1,2,*, Zha Deche 1 ad Chu Diahui 2 1 School of Computer Sciece ad Techology, Harbi Istitute of Techology, Harbi , P.R. Chia; 2 School of Computer Sciece ad Techology, Harbi Istitute of Techology at Weihai, Weihai , P.R. Chia Abstract: Nowadays may eterprises have to face a real problem i their operatio processes, which is mismatch betwee active demads ad resource. To solve this problem, traditioal literatures employed Resource aggregatio of Cloud Computig. However the most of these researches focus o computig resources but oly few o applicatio resources. Ad there does ot exist a complete solutio. I this paper, a resource service model RSM which is dyamic, customizable ad fixable is preseted. This model ca be able to help eterprise add surplus resources ito cloud resource pool. The, a SaaS resource maagemet architecture CARMA is built. The CARMA is used to solve the problems i sharig ad maagemet resource of eterprises. Meawhile, resource optimizatio selectio is take ito CARMA to reduce resource waste ad task delay. Applied i the trasport service field, practical work proved that the proposed models are feasible ad achieve the expected effect. Keywords: Private resource maagemet, resource service model, SaaS resource maagemet, service, service equivalet. 1. INTRODUCTION Cloud computig is a kid of service resource pool i which a lot of resources are aggregated. Teats dyamically select a umber of resources to serve themselves accordig to their ow demads [1, 2]. Cloud computig maily provides the resources for computig, such as CPU, disk, memory, etwork, software, data ad so o. The attribute of quality of service (QoS) is relatively simple. It geerally uses time or cost as the parameters of QoS to establish the model which is based o performace or ecoomic [3]. The purpose is to seek the shortest ruig time (the optimal performace). Besides computig resources, other shared resources are also required i the cloud computig based idustry applicatio system. For example, maufacturig cloud icludes the hard maufacturig resources, the soft maufacturig resources ad the maufacturig capacity [4, 5]. Ad logistics cloud icludes the hard distributig resources (such as trasportatio vehicles, mapower, etc.), the soft distributig resources (such as data, software, kowledge, etc. i the process of trasportatio) ad distributio capabilities (such as distributio, schedulig ad itegratio, etc.) ad so o. Eterprise s addig some resources which ca be shared to the cloud, ad o the oe had, ca improve the utilizatio rate of resources, o the other had ca also make cloud applicatio more widely. However, the type of resources i applicatio system is various ad the umber of them is large, their access ad maagemet are more difficult. I additio, there are some differet costraits ad evaluatios betwee the user s demads for the QoS ad QoS of cloud resources i differet fields. The requiremet of users is ot oly for time ad price, but usually icludig TOPS (Time, Quality, Price, *Address correspodece to this author at the School of Computer Sciece ad Techology, Harbi Istitute of Techology, Harbi , P.R. Chia; Tel: ; z_xiaodog7134@163.com X/15 Service etc.). Therefore, schedulig based o performace or ecoomics caot meet the actual eeds of the eterprise. For a case of trasport of eterprise, there is usually such a situatio: resources provided by Resource Service Provider (RSP) are idle, but eterprises who eed these resources could ot fid them. The cause is as follows: 1) There exist poor iformatio iteractio ad the iformatio isolated islad; 2) the best allocatig solutio, which meets the iterests of both supply ad demad sides at the same time, ca t be made while there are may resources ad tasks. Ad at all the primary cause is: 1) resource maagemet mode is icomplete; 2) resource maagemet mode has some faultiess. So i this paper characteristics of the cloud resources are discussed ad researched, the resource model, maagemet architecture ad service mode are costructed; ad these are applied ito the related field. 2. RELATED WORK 2.1. Resources Service Mode ad Optimizatio Schedule Resource is usually itegrated ito cloud eviromet via service mode. After combiig with cloud computig ad etwork maufacturig, Li Bohu et al. [4, 5] preset a cloud maufacturig architecture ad add virtual resource layer for the itegratig resources. This architecture ca access all kids of resources by the cloud techology, achieve the servitizatio ad virtualizatio of resources, ad advaces the may to oe service mode of scattered resources cocetrated use to may to may service mode. Liu Lila et al. [6] study the curret etworked maufacture ad various patters of maufacturig cloud, ad propose the four levels maufacturig cloud architecture which uses the maufacturig resources as the bottom of cloud computig, emphasizes o that ecapsulatig distributed resource i the form of services ad maage them i a cetralized maer. Casati F 2015 Betham Ope

2 434 The Ope Cyberetics & Systemics Joural, 2015, Volume 9 Xiaodog et al. Table 1. Aalysis of resource s characteristics i cloud eviromet. Resource Feature Features Cause The Cloud Resource Maagemet Techology Autoomy of Distributio Heterogeeity The diversity of techology Dyamic No-real-time Perceptio Collaborative I differet orgaizatios ad geographic areas, the ower masters full kowledge of resources, ca cotrol ad maage resources A wide rage of differet characteristics Differet orgaizatios have differet resources maagemet, schedulig ad maiteace policies to resources Resource s cofiguratio, ability, ad ruig state dyamically chages costatly i the process of operatio Productio cycle is log, the process is relatively idepedet By the Iteret of thigs ad RFID techologies ad so o, the curret iformatio of the resources ca be gotte real-time More complex task requires resources from differet providers work together to complete Provide resources itegratio model, through the iteractio with resource maagemet system of differet domai or resource caledar provided by RSP to schedule resources Defie stadard resource maagemet model ad iformatio iteractio protocol [12] Make stadard mechaism of resources ad users demad expressio[13] ad build extesible resources framework Have a certai adaptive ability ad ca hadle the fault tolerace of failure Establish resource caledar, moitorig resources, schedulig resource Access to awareess techologies of the Iteret of thigs ad RFID Uderstad security mechaism, the resources characteristics of differet areas, do task trackig, form "may-to-may" service mode et al. [7] establish a matchig ad search framework based o the task s fuctioal requiremets, ad propose optimizatio algorithm based o ituitioistic fuzzy set resources. Whe resource is evaluated, tradig experiece, decay with time ad other o-fuctioal services quality attributes are added. This kid of methods caot effectively guaratee displayig the service features comprehesively; pay more attetio to the theoretical study of service selectio rather tha practicality ad geerality. I order to further improve the coditio of resource usig, scholars had studied a large umber of algorithms, such as service etwork of the simulated aealig algorithm [8], at coloy system optimizatio algorithm based o trust perceptio [9] ad geetic algorithm based o multi-objects GODSS [10], etc. The resource aggregatio service chai costructed by these methods is the optimal sigle solutio satisfyig the costrait coditios rather tha acceptable multi-solutios, which caot fully shows the persoality of services ad motivate service providers to optimize service quality. To make resources etworked the most commoly method is to package the resource as a service. After that, may optimizatio selectios ad schedulig for service ca be applied to the resource s optimizatio schedulig. Tao F et al. [11] propose a group decisio makig fuzzy hierarchical aalysis model based o service quality. The whole process of resource schedulig ca be divided ito resource search, resource schedulig based o the service quality ad dyamic iteractio ad cosultatio three stages, ad schedule resources with fuzzy aalytic hierarchy process ad group decisio. The above studies geerally believe that the resource ca be ecapsulated as a service, which ca effectively shield heterogeeity, distributio ad diversity characteristics of kids of differet resources thus reduce the difficulty of the selectio ad schedulig algorithm. I practice, however, if we do ot cosider or less cosider the related features of the resources, it will usually block the optimal selectio of resources, thus caot achieve the ideal effect. If those features are cosidered, the complexity of maagemet will be icreased. Faced with this situatio, this paper proposes a extesible architecture, i which RSP ad RSD ca dyamically select the resource characteristics, build resource model, obtai optimal selectio ad schedulig scheme of resources through relevat algorithms at the same time of reducig maagemet difficulty Aalysis of Resource Features i the Cloud The resources i cloud computig maagemet that come from differet RSPs are wide-area distributed, heterogeeous ad dyamic. The cloud resource maagemet does t have a complete cotrol of resources, ad ca t predict the state of the resources. Ad the heterogeeous resources greatly complicate the resource maagemet. All of above make maagig resources ad schedulig tasks be differet. Therefore, this paper summarizes ad aalyzes the features of resource maagemet i the cloud ad gets the techologies that maagemet i the cloud eviromet. All results are show i Table RESOURCE SERVICE MODEL AND RESOURCE MANAGEMENT ARCHITECTURE 3.1. The Scee Descriptio ad Aalysis Firstly, the cloud resource service maagemet should solve the problem of the service mode. So we eed to aalyze ad desig the resource maagemet framework. The scee aalysis is very useful to defie the style of the system framework [14]. This paper cofirms the framework style of resource maagemet by the scee of Fig. (1). I the service maagemet, Cloud Platform supports RSPs to register kids of resources to the cloud to realize the uified maagemet to scattered resources. At the time of sharig, RSP ca also maage resources idepedetly, which icludes moitorig

3 SaaS Resource Maagemet Model ad Architecture Research The Ope Cyberetics & Systemics Joural, 2015, Volume Fig. (1). Cloud resources service operatio scee. the ruig state of the resources, allocatig resources by utilizatio rate ad beefit maximizatio, ad cooperatig with other resources. Cloud resources, packaged ito service, ca be assembled ito differet processes ad serve for differet users, which is differet from etwork resource maagemet mode. So the mode turs from may to oe ito may to may. The system ca receive feedback iformatio comig from differet users ad formulate ad improve service strategy specifically. RSD ca submit tasks to the cloud ad maage tasks idepedetly. RSD ca divide task, ad make up the workflow. The accordig to the eeds, the tasks ca be executed by the specific resources which are allocated idepedetly by RSD, or ca be assiged by the system automatically. RSD ca moitor the ruig state of the tasks, ivestigate the task optimizatio solutio ad feedback the problems related with quality of service. I most case, RSD eed ot to cosider the discotiuity problems which emerge durig ruig the tasks. Whe a resource produces a problem, the system will assig aother ew resources to execute the task dyamically ad feedback the iformatio to RSD timely. I other words, the cloud platform ca provide the idirect guaratee for the trust relatioship betwee supply ad demad, which is decided by resource aggregatio of cloud platform Resource Service Model As show i Fig. (1), the resource maagemet should be divided ito two parts: a part of it ca show the fuctioal maagemet of resource purpose, icludig capacity, cosumptio, operatios, etc., displays how to match the task; The other shows o-fuctioal of moitorig ad schedulig resource, icludig schedulig, coordiatio, cotrol, optimizatio, etc., displays how to implemet of task o resource. It ca make better use advatages of resource oly if both of two aspects are combied orgaically. The cloud resource maagemet, therefore, ca be able to aggregate differet types of resources ito the system ad fully embody the features of them. O the oe had, it allows RSP to maage their resources idepedetly; o the other had it also allows the system ad RSD to schedule resources accordig to the requiremets of task. The paper builds a multi-teat, customizable, scalable SaaS model resource model based o Fig. (1) scee. Resource maagemet s goal is to make better use of resources. It s closely related with activities of the task o the resources. Here elemets (object) ad the relatioship betwee the elemets i resource model are described by IDEF3, as show i Fig. (2). I order to be able to describe more accurately of objects ad relatioship betwee them i the resource model, ad ease computig, this paper makes the followig defiitio: Defiitio 1: Resource Meta Model RMM={G,P,T} where G is geeral resource type, each kid of resources has attributes. For example G = {resource idetifier, ad resource type, resource locatio...}; P is private resource type, has private attributes ad methods; T is resource types, differet resource type has differet private resources type. Defiitio 2: Resource Private Type P={(t,E,A, f,c) e=fe (E,C,t)&a=fa (A,C,t),e! E,a! A,t! T,fe,fa! f} where resource service capability attributes E = {E 1, E 2, E 3,... E }, E ca describe the ability of resource o multiple dimesios; private resource attribute A = {a 1, a 2,..., a }, is that a resource ca have may features. f e, is capacity costraits of the model, f a is customized method, both of them are used to costrait model computig ad expressio. The biggest differece betwee E ad A is that A has oly two values existece or iexistece. Ad E eed to poit out its value scope, such as vehicle s load is divided ito such as 5 tos, 10 tos etc.. Though both E ad A are customizable, but they are relatively stable durig the etire life cycle of resource. Accordig to defiitio 1 ad 2, RSP ca customize resource iformatio accordig to their ow resource characteristics, register their resources ito the system, ad maage them. Here Job is maagemet task, its properties (JP) express the maagemet task characteristics, ad maagemet tasks ca be performed uder a certai coditio (JEC). Maagemet tasks is a maagemet work for resource, so it combies with resource to product a activity (UOB-J), ad record the target (UJT) ad process (UJP) of the activity. RSD ca select the resource meetig demads (TCQ) from the resource pool to complete the task. Here demad refers to resource capacity. The coditio of performig task is described by TEC, ad task descriptio is TP. The task per-

4 436 The Ope Cyberetics & Systemics Joural, 2015, Volume 9 Xiaodog et al. Fig. (2). Resource service model. formace shall be doe by the resource. Therefore, tasks ad resources ca produce a series of busiess activities (UOB-T). The Fig. (2) shows that the iteractio betwee the compoets is through iterface, which simplifies costructig the followig system architecture o the whole ad improves the visibility of iteractio. Reality ad service provided by the compoets are decouplig, it promoted idepedet resolvability. I order to better show SaaS software egieerig priciples of desig patters, this paper itroduces two symbols ito the IDEF3, oe is a compoet customizatio file (*. XML), aother is the ifluece operator. Customizatio file makes differet teats (RSD ad RSP) expressio accordig to their ow demads. Ad the ifluece operator expresses the ifluece of resource service quality evaluatio (Ev) to model of resources ad resource selectio. Ev associated with the task (Task ad Job). Data comes dyamically from two activities (UOB-T ad UOB-J), ad it impacts o activities (such as the choice of resource). I additio, it also reflects the ifluece o evolutio of the resource model (such as icreasig or decreasig the relevat attributes or methods). Defiitio 3: Let R be resource set which meet demads of resource model RM i the cloud resource pool, T types of resources ad E be service capacities. Kids of resources to complete task is R q ={r 1,r 2,,r i,,r }, where resource type of r i is t i, capacities required is E qi ={e i1,e i2,,e ik }. Relatioal operatio! T =ti (R) is selectio to resource type ad the process of filterig the set by service capacity is! Fi (! T =ti (R)), where F i = (e j1! e i1 ) " (e j2! e i12 ) "..." (e jk! e ik ), e j1,e j2...e jk! E j, E j! E, the resources provided by the cloud resource pool to complete the task: R p = U! Fi (! T =ti (R)) if ad oly if R p! R q (1) Defiitio 3 is derived from defiitio 1 ad 2, is oe of coditios to perform activity UOB-T.UEC, where U! Fi (! T =ti (R)) " f e,! Fi (! T =ti (R)) " f j. It ca be see by defiitio 3 that selectio of resource may eed to match may capacities of a resource. Thus, type ad capacity coditios ca t be used to filter resources cocurretly i geeral. Because differet types of resources have differet service attributes. It is difficult for computig cocurretly. Obviously, this is decided by the resource model, sice differet types lead to differet models. Thus, it ca be proved that the resource model described by defiitios 1 ad 2 has a certai elastic extesible ability. By the formula (1), the supply of resource is ot exactly as same as demad of task. The amout of supply resource must be greater tha the amout of demad resource; otherwise the task caot be completed. Eve R p R q met, formula (1) has still the followig problems: (1) The demad ad supply of the same resources are ot cosidered i differet period, i.e. resource caledar costrait is abset. Therefore, the result of selectio may ot complete the task. The result provided by the formula a ecessary coditio to meet the task requiremets but ot sufficiet coditios. (2) The quality, efficiecy ad cost etc. of resources to perform a task are ot cosidered, so the result of selectio may ot be optimal. (3) It is suitable for a sigle resource selectio, but the efficiecy is ot high while selectig multiple resources. If let schedule of task i be c i, it is easy to get the followig corollary:

5 SaaS Resource Maagemet Model ad Architecture Research The Ope Cyberetics & Systemics Joural, 2015, Volume Fig. (3). Resource service model. Corollary 1: R p = U! Fi (! T =ti (R)) ad there must be R "C=c p R q (2) i The evaluatio of the resource service quality is maily from the dyamic data of activities UOB-T ad UOB-J. The followig defiitio ca be defied: Defiitio 4: Let QoS of resource R-QoS={Q1, Q2,, Q} is multi-dimesioal, all of evaluatio values are from the followig formula: ev! i= 1 i ev! i= 1 R-QoS = f ( UOB " J. UJP) + f ( UOB " T. UBP) (3) Where i is a task sequece. Defiitio 4 poits out that the resource QoS is from aalysis of historical data which resources have udertook tasks. It is a objective evaluatio method of the resource QoS. Thus similar time efficiecy, service, cost, reliability, availability etc. aspects of the evaluatio of each resource ca be obtaied by it. While the task is assiged, the resource selectio is optimized with formula (2) after filterig. At the same time, it also has the effect to moitor QoS. It ca help system fid imperfectios ad correct them i real time, ad is a importat part of the extesible mechaism. i As the above defiitio show, although the geeral resource model is almost same, differet types of resources ca have a differet descriptio of resource private type. Capacity attributes of private resource type reflects service features of resources. Differet resources service capacities are differet, which support the operatio of differet resources. Capacity ad private attributes are the resource static descriptios, is the basis of resource selectio. With the resource caledar, various tasks are completed accordig to the differet busiess processes. Ad all kids of the UOB-T data are produced; it is a dyamic descriptio of resources. The Ev gets these feedback data through the UOB-T iterface to aalyze aspects of the busiess, which is the perspective of the optimizatio schedulig resource from RSD. Ad UOB-J reflects more about autoomy of the resource distributio. RSP maiteaces ad maages resources from their ow iterests, determie whether to accept the schedulig, ad trasmit iformatio to Ev maagemet to aalyze through the UOB-T iterface, which is the perspective of the optimizatio schedulig resource from RSP Resource Maagemet Architecture The resource model ca exted to be Cloud Applicatio Resource Maagemet Architecture (CARMA), as show Fig. (3). This architecture ca be approximately divided ito several modules, such as resource maagemet, task maagemet, system maiteace, model parsig egie ad load balacers, resource base, mate-model base, model base, domai kowledge base ad so o. We do ot itroduce all modules i detail ad oly describe some critical parts due to

6 438 The Ope Cyberetics & Systemics Joural, 2015, Volume 9 Xiaodog et al. Fig. (4). CARMA. limited space. The model parsig egie f metioed i RMM is the most critical part i CARMA. The domai experts make all kids of meta-resource models ad save the models o the provisios to the domai kowledge base. The model parsig egie displays these models o RSP(visibility). Ad RSP registers ad maages relevat resources, which is parsed ad executed by model parsig egie. Furthermore, this egie schedules the resources whe we choose ad optimize them i task assigmet ad load balacig. The model parsig egie shields the heterogeeity amog differet resources ad the differece to the maagemet techology, which realizes customized maagemet to multi-teat resources ad improves the resource aggregatio. The model parsig egie also ca realize the virtualizatio resources uder the cotrol of resource caledar. The four bases (resource base, mate-model base, model base, domai kowledge base) ca ehace the scalability of the system ad improve more resource aggregatio which guaratees that every task has may implemetatios (reliability). Ad based o this, we ca realize optimizatio selectio. The CARMA aggregatio ca make the resource capacity cotiuous i a wide scope(for example, the load of a truck chages cotiuously from 0.5 tos to 100 tos), which ca simplify matchig algorithm ad improve the executable sequece probability of the task matchig with resources (availability). I order to support CARMA fully, this paper describes the resource maagig layers architecture (RMLA) i detail, which is show as Fig. (4). RMLA is divided ito five layers: resources provisio, resources aggregatio, resources service, service workflow ad service demad (task) from the bottom to up. (1) Resources provisio: it is the layer of executig tasks, which cosists of physical resources provided by RSP. (2) Resources aggregatio: RSP registers resources ito the system based o the maagemet strategy of RSP ad the itegratio rules of CARMA. The resources, redistributed by type, become a free ad scalable layer of resources aggregatio, which ca reduce the iterferece betwee service ability ad services selectio ad the difficulty of desigig ad realizig relevat algorithm. From the defiitio of meta-resource models, we kow that registerig resources ca support large amouts of resources without the limitatios of regio, RSP ad RSD s demads, which guaratees the eough resource supply durig the task assigmet process [15]. (3) Resources service: it is called the layer of resource combiatio. Ad it ca become a more powerful uity by the itegratio of oe or more resources with their service characteristics. For example, the collaborative coupligs to the forklifts load 1 to ad 90cm up, the trucks height 70cm ad load 10 tos ad the truck drivers become a group of trasport service. It also supports RSP to package resources as a service, access the system directly, which is accessed through two differet system resource layers. (4) Service workflow: the packaged resource service based o busiess flow becomes a more perfect service which icludes o-resource service. It is equivalet to a kowledge base composed of services. (5) Service demad(task): users ca depart a task ito several subtasks o this layer. O oe had, it ca choose relevat workflow model from service workflow layer by model matchig. O the other had, it ca also choose resource service to compose directly based o the eeds of the tasks. The selectio of resource service is a bottom-up process which passes five layers: task decompositio workflow/service selectio service bidig resource bidig resource performig. The optimizatio of service workflow layer is the optimizatio of service path. Ad the optimizatio of service layer is the optimizatio of choosig resources ultimately. 4. RESOURCE SELECTION ON QOS AND SERV- ICE CAPACITY MATCHING RMLA ot oly shows the idea of Resource maagemet, but also shows the relatio ad coectio betwee

7 SaaS Resource Maagemet Model ad Architecture Research The Ope Cyberetics & Systemics Joural, 2015, Volume tasks ad resource, icludig matchig models from the service requiremet (task) layer to the service process layer, selectios from the service requiremet layer to the resource service layer, optimal combiatios of resource ad tasks, ad the task assigmet, resource schedulig ad related load balace. This sectio, takig the service maagemet i logistics field as a example, discusses the foudatio of implemetig the processes above: matchig of tasks ad resource service capability ad resource optimizatio based o service quality Resource Optimizatio Based o QoS I RMLA, after filtered based o capacity matchig of resource service ad service by formula (2), may resources satisfyig service quality ca be elected, but ot all of the resources are able to become fially service providers, oly some of which are eeded for the task. Therefore, the optimal group are demaded to be selected from the resources. Thus, the factors ifluecig the service quality eed be determied ad multidimesioal service quality model eed be built, as is state i defiitio 4. Every parameter i the service quality model is derived from evaluatio results of service usage records by resource service evaluatio model, as is obtaied by formula (3). For example, a five-dimesio service quality model icludig time Qti(r, ti), cost Qco (r, co), reliability Qrel(r, rel), availability Qav(r, av) ad reputatio Qrep(r, rep) is represeted as QoS ={Qti (r, ti), Qco (r, co), Qrel(r, rel), Qav(r, av), Qrep(s, rep)}. So, the formula (3) eed to obtai iformatio from UOB-T ad UOB-J i order to provide the basis for resource selectio. I order to uify computig stadard ad simply the computig process, the ideal of service equivalet is ivolved, the defiitio of which is followig: Defiitio 5: Service Equivalet is quatizatio stadard for service capability, which is the ratio of the capability of certai type of resource i idustry stadard uits to the miimal resource capacity i this field, is λ. For example, the loadig capacity for a lorry is 10 to, if which for the miimal lorry is 5 to, the service equivalet for the lorry should beλ w =10/5=2; If the miimal ship of a certai type of cargo ca load 20 20GP cotaiers, the service weight of the ship should be! cotaier = (40! 20) / (20! 20). I geeral, itroducig the ideal of service equivalets ca simplify the complexity of calculatig service capabilities. Similar with service equivalet, ordial utility fuctio is ofte used for service electio i micro-ecoomics, which ca provide weak ordial relatios i umerical value ad promote ratioal cosumptio for service. Costructive Ordial Utility Fuctio is used herei as a scale of service resource to obtai the optimal solutio of service resource selectio. Defiitio 6: Resource Optimizatio Model Based O QoS (ROMBOQ), let R = R={r 1,r 2,, r } is the set of cadidate service resources obtaied by formula (2). Q=[q ij ] m is QoS decisio matrix, where q ij =Q i (r j,i) is resource r j (r j R) is valued from QoS attributes q i (q i QoS), obtaied by formula (3) uder data ormalizatio. I costructive ordial utility fuctio f (r i ) =! w j q ij, if ad oly if j=1 f (r i )! f (r j ), the service that resource ri provides is better tha r j ; where w j is weight of attribute of service resource, i,j=1,2,...,m. Accordig ROMBOQ, followig decisio model ca be obtaied: m S opt = mi( f (r 1 ), f (r 2 ),..., f (r m )) = " " (q * j! q ij ) 2 w 2 (4) $! w j=1 j = 1 & s.t % w j " 0, j = 1,2,..., & m & ' E q (r,# ti ) =! E p (r i,r ti ) * where q j = max( q1 j, q2 j,..., qmj ) is the ideal value of attribute qj i decisio matrix, the objective fuctio Sopt restrict the ideal value i the miimal variace with attribute qj for other cadidate services. So, the ordial service resources R satisfied that:!r i,r j " R, if ad oly if f (r i )! f (r j ), the resource ri is better tha rj, Eq deoted as capability requiremet ad E q deoted as capability supply. Formula (4), (5), (6) shows that the resource weight wj is determied by stadard deviatio. Formula (7) represets that the resource optimal must revolve aroud the busiess of services, which ot oly just choose oe resource, but there is the strog possibility that a group of resources which ca accomplish a certai task will be provided Evaluatio of Resource Service Quality I the whole Cloud, registered resources i differet locatio form a huge ad complex etwork by their related busiess. It is importat to moitor, estimate ad modify the etwork as well as pretty complex. It ca help observer obtai the situatio of resource service, fid the advatage ad disadvatage of the algorithm ad cope with the problems ad modify the algorithm o time. The evaluatio herei is divided ito 3 aspects: the cosumptio of resource idividual service capability; the cosumptio of the whole resource service capability; the load balacig of resource. (1) Resource utilizatio rate: it reflects the task saturatio degree for resource i the etwork, obtaied by the followig formula: C CoS p = ( (! qj"x (r i,# x ) $ j ) % t i C pj"x (r i,# x ) T j=1 where C qj!x (r i," x ) is the service capability i use ad j=1 (5) (6) (7) C pj!x (r i," x ) is the maximum service capability provided by resource j.! j is the importace of the service capability i the service process. For example, the regulatio rules that the weight baggage for airlie should be o more that 45kg ad (8)

8 440 The Ope Cyberetics & Systemics Joural, 2015, Volume 9 Xiaodog et al. Fig. (5). A logitudial compariso. the sum of height, width ad legth should be o more tha 203cm, i reality, the maximum of which will be the limitatio ad! j should be 0 or 1. Formula (8) shows the ratio of service resource supply to service resource cosumptio i a certai period. The more the ratio is, the more loadig the certai idividual is ad the higher resources utilizatio rate is. CoS p is amed as the saturatio of resources load. T is the work time desigated by RSP, ti is reality work time i T. For istace, if a RSP desigate its resource i the rage of work time from 8 am to 6 pm ad the actual work time is 4 hour, there will be ti/t=4/10. Obviously, every RSP sets differet work time period for differet resources that is the usage of resource caledars cocered i sectio 3.2, which is a data structure with SaaS characteristic. (2) load balacig of resource it reflects the equilibrium of resource load i etwork, which ca be obtaied from the saturatio of idividual resource service capability, the formula is followig: avgl = (! CoS pi ) / (9) B = " (CoS i! avgl) 2 / (10) As is show from formula (9), (10), the smaller B is, the more load balacig of the resource will be. It is improper here to use absolute resource load as the stadard, because that the resource load caot be balaced whe resource which has large service equivalet loads as may as resource which has small service equivalet do. For example, if a 5- toe lorry carries 2-to cargo as the same time a 2-to lorry also carries 2-to cargo, it is a ubalaced taskig with serious resource waste. So, idividual service saturatio is used to calculate relatively load herei. 5. EXPERIMENTAL METHOD AND RESULTS ANALYSIS I order to test the correctess of the model, BirisCloud of Harbi Istitute of Techology was used i this experimet as the base platform, where CARMA was built, RMLA was realized ad various resource models were built. The, follow three represetative types of eterprises were compared: (1) petty idividual-owed trasport aget with o more tha 5 vehicles i o more tha 2 types; (2) small trasport compay with 10 to 20 vehicles i o more tha 5 types; (3) large trasport eterprise with more tha 80 vehicles i ay type. The emphases of resource maagemet for the 3 types of eterprises are differet. Thus, it was permitted that the eterprises ca customize ad maagemet the attributes of the geeral resource type, private resource type i coveiet for the cooperatio ad performace compariso of eterprises. The capability attribute, UOB-T ad UOB-j were set by idustry stadard. I this perspective, buildig RMLA both helps for the idividual maagemet of eterprises for resources ad improves the maagemet mode. It ehaces the competitive power of eterprises i market evetually. Data filterig is doe whe all of RSP, RSD ad related idustries are ivolved ito the platform ad pass to stable phase. Accordig to the estimatio model metioed i sectio 4.2, experimet ad results aalysis are carried o two aspects Resource Utilizatio By formula (8), a logitudial compariso amog the 3 type of eterprises was doe, as is show i Fig. (5). Before joiig the Cloud, there were lowest load saturatio ad stability for type (1). Otherwise, the load of type (3) was the most stable. As is see from the left lie chart i Fig. (5), there were large quatities of tasks i type (1), but caot cooperate with type(3) which was i eager of tasks. This verified the problem metioed i sectio 1. The right of Fig. (6) shows that busiess of every type of eterprise has bee improved. The state of idividual-owed agets has bee closed to that of small compaies. Ad rate of the load saturatio icreasig of type (2) is obviously larger tha that of type (1), which is related with the reputatio ad market recogitio Load Balacig of Resource Due to the load saturatio used as parameter, value B is always lower tha oe. Therefore, small chage derived from the variace may reflect a great load differece. For example, if a average saturatio was 0.3 ad the task afforded by a vehicle was 0 i a certai day, the perturbatio would oly be Absolutely, this result also showed that the whole utilizatio of resource was low. By service trackig ad data aalysis, result i Fig. (6) was obtaied. Due to

9 SaaS Resource Maagemet Model ad Architecture Research The Ope Cyberetics & Systemics Joural, 2015, Volume Fig. (6). B of resource load. the stadard schedulig policy for resources, employers ad tasks i type (2) ad (3) of eterprises, the load is far more balaced tha that of type 1. After joiig the Cloud, the load balacig of type (1) improved a lot( Pay attetio differet umerical rage betwee the left ad the right i Fig. (6). It also idicated the maagemet mode of medium-sized ad small eterprises has improved. CONCLUSION This paper focuses o the characteristics of resource maagemet i Cloud ad builds resource model. Based o these, a resource maagemet architecture is costructed. Meawhile, this paper proposed evaluatio method of this resource maagemet. The, the solutio i this paper is implemeted o BirisCloud platform ad followig advatages are reflected durig the applicatio process: 1) Good opeess ad extedibility: It maily behaved i the ability of customizig ad addig ew resources, resource service capability ad quality with o chage i model architecture ad keepig the disturb from resource heterogeeity; 2) The solutio combies distributio self-maagemet ad uified schedulig ad is able to moitor ad schedule the resource ad balace load dyamically, adjust the relatios betwee high efficiet operatio ad safety maiteace, reduce the busiess risky of eterprise; 3) Improvig the resource utilizatio sigificatly: the solutio both ca adjust the supply ad demad relatios betwee service cosumers ad service providers, ehace the providers to improve the quality of service ad promote the cooperatio amog eterprises, raisig the competitive power of eterprises. The practical work proved that the proposed models ad architecture are feasible ad achieve the expected effect. CONFLICT OF INTEREST The authors cofirm that this article cotet has o coflict of iterest. ACKNOWLEDGEMENTS The atioal sciece ad techology support fudig project (2012BAF12B16), the atioal atural sciece foudatio of Chia ( ), the scietific ad techological torch-pla project of Shadog Provice (2010GZX20126). REFERENCES [1] P. Mell, T. Grace, The NIST Defiitio of Cloud Computig, Natioal Istitute of Stadards ad Techology: US, [2] F. Doelitzscher, A. Sulistio, C. Reich, H. Kuijs, ad D. Wolf, Private cloud for collaboratio ad e-learig services: from IaaS to SaaS, Computig, vol.91, o.1, pp , [3] H. Ji, H. H. Che, Z. P. Lu, X. M. Nig, Q-SAC: towards QoS optimized service automatic compositio Proceedigs of the 5 th IEEE/ACM Iteratioal Symposium o Cluster, Computer ad the Grid (CCGRID), vol. 2, pp , [4] P. Li, L. Zhag, S. Wag, F. Tao, J. Cao, X. Jiag, X. Sog, X. Chai, Cloud maufacturig: a ew service-orieted etworked maufacturig model, Computer Itegrated Maufacturig Systems, vol. 16, o. 1, pp. 1-7, [5] P. Li, L. Zhag, L. Re, D. Chai, F. Tao, Y. Luo, Y. Wag, C. Yi, Further discussio o cloud maufacturig, Computer Itegrated Maufacturig Systems, vol. 17, o. 3, pp , [6] L. Liu, T. Yu, Z. Shi, Research o QoS-based resource schedulig i maufacturig grid, Computer Itegrated Maufacturig Systems, vol. 11, o. 4, pp , [7] F. Casati, S. Ilicki, L.J. Ji, V. Krishamoorthy, M.C. Sha, eflow: A Platform for Developig ad Maagig Compositio e- Services, Techical Report, HPL , HP Laboratories Palo Alto, [8] S. L. Liu, Y. X. Liu, F. Zhag, G. F. Tag, N. Jig, A dyamic Web services selectio algorithm with QoS global optimal i Web services compositio, Joural of Software, vol. 18, o. 3, pp , [9] T. Yu, Y. Zhag, K.J. Li, Efficiet algorithms for web services selectio with ed-to-ed QoS costraits, ACM Trasactios o the Web, vol. 1, o. 1, Article 6, [10] K. Czajkowski, I. Foster, N. Karois, C. Kesselma, S. Marti, W. Smith, S. Tuecke, A Resource Maagemet Architecture for Metacomputig Systems, Job Schedulig Strategies for Parallel Processig. Spriger: Berli, Heidelberg, pp , [11] F. Tao, Y. Hu, D. Zhao, Z. Zhou, Study o resource service match ad search i maufacturig grid system, The Iteratioal Joural of Advaced Maufacturig Techology, vol. 43, o. 3-4, pp , [12] R. Rama, M. Livy, M. Solomo, Matchmakig: distributed resource maagemet for high throughput computig, High performace distributed computig, Proceedigs The 7 th Iteratioal Symposium o IEEE, pp , [13] R. Buyya, Ecoomic-Based Distributed Resource Maagemet ad Schedulig for Grid Computig, arxiv preprit cs/ , 2002.

10 442 The Ope Cyberetics & Systemics Joural, 2015, Volume 9 Xiaodog et al. [14] R. T. Fieldig, Architectural Styles ad the Desig of Network- Based Software Architectures, Uiversity of Califoria: Irvie, [15] X. Xu, From cloud computig to cloud maufacturig, Robotics ad Computer-Itegrated Maufacturig, vol.28, o.1, pp , Received: September 16, 2014 Revised: December 23, 2014 Accepted: December 31, 2014 Xiaodog et al.; Licesee Betham Ope. This is a ope access article licesed uder the terms of the Creative Commos Attributio No-Commercial Licese ( liceses/by-c/3.0/) which permits urestricted, o-commercial use, distributio ad reproductio i ay medium, provided the work is properly cited.