They aim to select the best services that satisfy the user s. other providers infrastructures and utility services to run

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1 End-to-End Qo Mapping and Aggegation fo electing Cloud evices Raed Kaim, Chen Ding, Ali Mii Depatment of Compute cience Ryeson Univesity, Toonto, Canada Abstact In cloud computing envionment, softwae sevice often collaboates with othe suppoting utility sevices in ode to povide a complete solution to end uses. The softwae sevice itself could povide the equied functions to uses tt fulfill thei needs. Howeve, the non-functional equiements, which epesent the main citeia in the sevice selection pocess, need the collaboation fom all involved paties. Given the end-to-end non-functional equiements submitted fom end uses, the cllenge is to select the best cloud solution tt satisfies these equiements, and to let a softwae povide select the best utility sevices. In this pape, we popose a mapping mecnism tt helps map uses Qo equiements to the equied softwae sevice and utility sevices at diffeent cloud levels. Ontology is used to semantically define diveging concepts of the non-functional equiements and guaantees and thei elationships at diffeent levels of cloud sevices. o implementation, OWL- (Ontology Web Language fo evices) and WRL (emantic Web Rule Language) ae used. The expeiment, in this pape, showed tt the mapping pocess does not incu much calculation ovehead, if integated with any sevice selection system. Keywods- Qo Mapping; Cloud Computing; Utility evice election; Ontology; OWL-; WRL I. INTRODUCTION Many entepises ae adopting cloud computing as a computing paadigm fo thei IT pocesses and businesses. They begin to offe thei computing esouces (e.g. memoy, CPU, netwoking and stoage) o softwae applications as sevices. With these suppots eadily available, the baie to be a softwae povide becomes much lowe. o small companies o even individual developes, as long as they ve a billiant idea and a obust softwae sevice, they could offe it to millions of customes without woying about the esouces and othe necessity sevices (e.g., database, secuity, etc.). If softwae povides undestand the demands fom the uses, the cllenge is to find the best utility sevices (e.g. infastuctue, platfom, database, stoage, etc.). The latte could collaboate with thei own softwae sevice to povide a whole solution to end uses. In this matchmaking and selection pocess, it is impotant tt both uses functional and non-functional equiements ve to be consideed. In cloud envionments, the functional equiements nomally can be satisfied by softwae Xumin Liu Depatment of Compute cience Rocheste Institute of Technology, U. xl@cs.it.edu povides alone. Howeve, the uses non-functional equiements need collaboation of all involved paties at diffeent cloud levels (i.e. the softwae sevices and its suppoting utility sevices). ince Quality of evices (Qo) is the most typical non-functional equiement, in this pape we use Qo to efe to all non-functional equiements. The Cloud evice Measuement Index (CMI) is intoduced to epesent the Qo popeties of cloud sevices. It includes a set of business-elevant key pefomance attibutes []. The eseach poblem known as Qo-based web sevice selection s been addessed by seveal woks [][2][22]. They aim to select the best sevices tt satisfy the use s Qo equiements and optimize paticula Qo constaints. Howeve, the pesented appoaches do not fit into cloud settings to solve the cloud sevice selection poblem. In web sevices envionment, povides own the majo computing components equied fo povisioning thei applications. Theefoe, they ve a full contol on guaanteeing end-toend Qo values of candidate solutions. In cloud computing, the case is completely diffeent as softwae povides use othe povides infastuctues and utility sevices to un thei applications. o, Qo values ae obtained as a esult of collaboation of all involved sevices. eveal Qo-based cloud sevice selection woks ve ecently been poposed [9][4][8]. Howeve, they addess the selection poblem fom the pespective of a single sevice type such as infastuctue, softwae o stoages when pocessing the selection on a set of functionally equivalent sevices. The majo limitation of these appoaches is thei inability to collaboatively model the Qo popeties (e.g. sevice pefomance, secuity, cost, etc) at multiple cloud levels. The othe shotcoming is thei assumption tt uses Qo equiements ae end-to-end which means they can identify low level Qo constaints. Howeve, end uses usually do not know o ae not concened about this type of constaints. In ode to addess the poblem of Qo-based sevice selection in cloud envionments, the end-to-end Qo values of the combination of cloud sevices tt compose the whole solution ve to be computed. Then using the obtained values, we can pefom the matchmaking and selection pocess. The main questions tt we attempt to answe in this pape ae: i) how to map the uses Qo equiements to the softwae sevice itself and the collaboating utility sevices in a dynamic envionment? ii) onto which cloud level(s) a paticula Qo equiement s to be mapped, /4/$ IEEE 55

2 consideing the sevice combinations? iii) if thee ae moe tn one sevice types involved in a solution, how thei Qo values can be aggegated? Afte answeing the above questions, the end-to-end Qo values can be obtained to select o ecommend the best solutions to the end uses. In this pape, we popose a mapping mecnism tt can be used as a mapping tool to pefom the mapping between the use s Qo equiements and cloud sevices Qo guaantees at multiple cloud levels. We define thee ules fo the mapping pocess. We adopt ontology-based solution which defines diveging tems of the Qo popeties and thei metics, and the elationship between Qo equiements and guaantees fom softwae sevices and othe utility sevices. We use WRL to define the mapping ules and the aggegation models. Ou ontology empsizes on the mapping elationships and povides a knowledge base fo the mapping pocess. When it is used togethe with othe well-accepted Qo ontology [3], an automatic cloud utility sevice selection mecnism can be implemented. Thee ae thee majo contibutions of the pape: To the best of ou knowledge, mapping fom use s Qo equiements to the Qo equiements on the softwae sevice itself and othe collaboating utility sevices on vaious cloud levels is a novel appoach. Any equiements fom softwae povides on those utility sevices should always be ooted fom thei end uses demands. The mapping becomes necessay in this context because softwae povides alone cannot guaantee the end-to-end Qo values. Ou semantic solution could achieve the automatic Qo mapping, and eventually the utility sevice selection fo softwae povides. It could povide a vey good decision suppot tool fo softwae companies. Ou mapping mecnism can help solve the cold stat poblem fo newly developed and published applications in the cloud. These applications ve no histoy ecods fo thei Qo values tt can be used to calculate the end-to-end Qo of the whole solution. The est of the pape is oganized as follows: ection II shows a simple motivating example. Then ection III defines ou methodology including the ontology, the mapping ules, the aggegation and the computation models. ection IV explains the design of ou pefomance test and discusses the esults. ection V eviews the elated wok. ection VI concludes the pape and lists the futue wok. II. A MOTIVATING EXAMPLE A small softwae company Tinyoft just developed new and easy to use poject management softwae (PM) to plan, schedule and execute pojects fo MEs. ince Tinyoft does not ve enough esouces to povide an all-in-one solution, it s to use othe utility sevices such as infastuctue, database and secuity to wok collaboatively. Tinyoft plans to use two cloud sevices, infastuctue and database sevices. igue shows a sevice flow and an execution plan fo Tinyoft. Thee ae multiple tasks (t t 5 ) in the sevice flow tt epesent Tinyoft s tasks fo seaching fo and selecting cloud sevices. The sevice flow follows a specific wok flow pocess (i.e. sequence flow). An execution plan is fomed by composing the infastuctue and database sevices tt ae selected though the sevice flow. The execution plan collectively achieves Tinyoft s goal of finding the best cloud sevices to ceate a solution tt satisfies thei uses equiements. In the figue, s n and s m efe to and DBaa sevices espectively; s p efes to the cloud solutions. evice low Tinyoft Execution Plan Tinyoft t each s n t 2 t 4 igue. Cloud sevice selection example. ollowing Tinyoft s execution plan, suppose thee ae 8 offes and 3 DBaa offes. When they collaboate with Tinyoft s PM sevice, thee ae altogethe 24 candidate solutions which could meet its uses functional needs. The best of those solutions will be selected and offeed to a potential end use. Afte the maket analyses, Tinyoft s d a basic undestanding of the typical Qo equiements fom end uses. ome impotant sevices Qo popeties tt end uses concen about ae availability, eliability, esponse time, data owneship, cost, and secuity. The question now fo Tinyoft is tt given the end-to-end use s Qo equiements, wt Qo equiements it should ve on potential and DBaa offes. o instance, Qo equiements fom a use on cost, esponse time and availability could be < $5 pe month, <= 4 seconds and >%99.5, espectively. Ou mapping tool could help Tinyoft map each of these equiements to the equied cloud levels (i.e. Tinyoft sevice- aa, DBaa and levels) using the poposed ules. By applying the mapping ules, the end-to-end Qo values of the 24 solutions could be calculated. Then the solutions can be anked and the best one is selected. Without ou mapping tool, it would be difficult fo Tinyoft to select the best utility sevices so togethe can satisfy uses Qo equiements. Now both the selection accuacy and efficiency could be lagely impoved t 3 elect s n each s m s n s m s p Candidate sevices {s s n} Candidate sevices {s s m } t 5 elect s m Compose s p Candidate solutions {s s p } Uses Uses 56

3 because of the mapping tool and its automatic easoning pocess based on the defined ontology and the ules. III. QO MAPPING AND AGGREGATION OR CLOUD ERVICE A. Qo Mapping Ontology Definition We popose an OWL based ontology (as shown in igue 2) tt epesents the knowledge base fo ou poblem domain; tt is Qo specifications (equiements and guaantees) fo cloud sevices. In ode to use concepts, tems and elationships defined in OWL and ve the infeence capability, we adopt WRL, which is an OWL based ule language [2]. The fist pat of ou ontology defines the elationships between thee cloud entities tt ae consideed in this pape: Cloud evice, Cloud Povide and Cloud Use. The second pat empsizes on the Qo equiements (QoReq) fom cloud uses and Qo guaantees (QoGua) fom cloud sevices; both ae subclasses of QoPopety. Ou ontology gives detailed desciptions on QoGua, which include the Qo specifications on diffeent levels, thei elationships and thei metics. We define thee categoies of Qo popeties: PefomanceQo, CustomeQo, and DeivedQo. They ae subclasses of QoGua. The fist categoy includes popeties tt define sevice pefomance such as availability, esponse time and eliability. The second categoy includes customes (uses) oiented popeties such as cost, secuity, data owneship. The thid categoy includes Qo popeties tt ae consideed as an aggegation of othe popeties fom the fist two categoies such as stability (T) and accuacy (AC). T efes to the sevice ability to emain uncnged in tems of its pefomance. AC measues the degee of the sevice s functional confomance to the evice Level Ageement (LA). QoMapped class is defined as an object popety, and we need this constuct when implementing the easoning pat of the mapping pocess. To save space, we use the following lettes to epesent diffeent data types, fo &xsd;float, fo &xsd;sting. Most of the Qo popeties coesponding to the fist level subclasses ae pat of the CMI (evice Measuement Index) definition. We choose some of them mainly to illustate ou idea, and moe popeties can be included in the futue. In ou ontology, the popeties ae epesented as subclasses. They ae defined as follows: AV (availability): it measues the degee of sevice accessibility. AV is calculated using the MTT (Mean Time To ail) and MTTR (Mean Time To Repai) metics [5]. The two metics, imttr, imtt, efe to the MTTR and MTT measued at the sevice. The dbmttr and dbmtt efe to the MTTR and MTT measued at the DBaa sevice. Two subclasses ae defined: DBaaAV (i.e. AV measued at DBaa sevice) and AV (i.e. AV measued at sevice). RT (esponse time): it measues the time between sending a equest to a sevice and eceiving a esponse [8]. It s thee subclasses: aart, DBaaRT and RT. They epesent the esponse time measued at the thee sevices. Its metics ae: aaproc (aa pocessing time), PROC ( pocessing time), DBaaPROC (DBaa pocessing time), TRAN ( tansmission time), DBaaTRAN (DBaa tansmission time). DO (Data Owneship): it efes to the povide s mecnisms tt guaantee a cetain level of contol fo uses ove thei data stoed and pocessed in the cloud. It s two metics: DBaaPol and Pol. They efe to DBaa s and s data contol policies fo stoing and pocessing use s data in the cloud. (Cost): it efes to the cost of accessing and using a sevice. Thee subclasses ae defined, aa, DBaa and. They epesent the sevice cost at the thee cloud levels. They ve thee metics: aah, DBaah, and h (pice pe hou fo aa, DBaa and ) espectively. U (ecuity): it efes to the degee of secuity tt a sevice offes. Thee ae thee subclasses: aau, DBaaU and U. They epesent the secuity measued at the thee sevices. even metics ae defined unde the subclasses: aaacc, DBaaACC and ACC (Access contol methods used at the thee sevices; DBaaPRIV and PRIV (Pivacy methods at DBaa and ); DBaaALG and ALG (Encyption algoithms used in DBaa and ). B. Qo Mapping Rules and Computation Models The focus of ou mapping mecnism is to map the use s Qo equiements to diffeent utility sevice levels and pefom the equied calculation of the aggegated Qo values fo the final matchmaking. In this pape, we develop thee mapping ules with thei coesponding computation models fo calculating the end-to end Qo values. igue 3 shows ou concept of the Qo mapping in the cloud. Qo Mapping Rule #. Based on this ule, use s Qo equiements ae mapped to the aa level and then to othe utility levels (i.e. and DBaa). Example: uses equiements on esponse time, cost and secuity ae mapped to the softwae level (e.g. Tinyoft PM application) and to the level (e.g. an Amazon EC2 VM) and the DBaa level ( e.g. ClustixDB). Qo Mapping Rule #2. Based on this ule, use s Qo equiements ae only mapped to the utility sevices levels (i.e. and DBaa). Example: uses equiements on availability and data owneship ae only mapped to the level (e.g. an Amazon EC2 VM) and DBaa level (e.g. ClustixDB). 57

4 2 g o Gu db om DBaa y Cloud evice s ua G s DBaa U DBaaALG DBaaACC DBaaPRIV ACC ALG PRIV aa U aaacc Class/ubclass G U ua 2 AC T U DBaaPol Pol aa aa U DBaaPRO N TRA DBaa aapro DBaa RT TRAN PRO dbmttr dbmtt imttr imtt un DeivedQo aah ua sa TG VG sr ua DBaa DO DO aa RT RT in O sd a Gu DBaah DBaa pp ed fo aa Povide d Ma DO RT egat CustomeQo AV AV agg QoGua PefomanceQo DBaa AV Cloud Use 3 sq e o QoReq s q h sq e R Cloud Povide a QoPopety e id ov p By susereq QoMapped Povide DBaa Povide 3 datatypepopety ObjectPopety igue 2. Ou poposed cloud Qo ontology. AV RL RT ^4 < 2 DO U E aa RP veyhigh 95.0 excellent < 20 80% Use ubmits Qo Requiements Use s Qo Requiements Qo Mapping Legend DBaa s Qo Guaantees iii s Qo Guaantees VM VM2 VM3 DBaa aa s Qo pecifications Pocess DBaa Qo Mapping et of Qo Requiements Qo Mapping chemes Output aa aa Qo Aggegation Matchmaking Pocess aa2 End-to-End Qo values aa 3 igue 4. Computing the end-to-end Qo values in the cloud. igue 3. Mapping uses Qo equiements onto multiple cloud levels. obtained fom the hosting sevice ( level). inally, the esult fom the second step is aggegated with the Qo value of the collaboating sevice used by the softwae sevice to obtain the (final) end-to-end Qo values of the whole cloud solution. igue 4 illustates an abstacted pocedue fo calculating the end-to-end Qo values fo cloud sevices solutions afte a use submits he Qo equiements. Based on the above desciption, the sequence wok flow of the sevice composition systems [2] can be adopted to model the aggegation pocess. In this paticula wok flow, the end-to-end values of esponse time and cost ae calculated by using a sum function fo the individual values. The endto-end values of the availability, data owneship and secuity ae calculated using poduct function. Ou computation models ae shown below: Qo Mapping Rule #3. Based on this ule, use s Qo equiements ae mapped to the aggegated values of Qo guaantees fom diffeent cloud levels. Example: DeivedQo popeties follow this ule, including T and AC. The value of T is calculated by aggegating the values of PefomanceQo popeties and calculating the aveage of them. imilaly, the AC value is calculated by aggegating the values of CustomeQo popeties. The output of the Qo mapping stage is mapping schemes. The latte detemines the cloud sevices (levels) involved in computing the end-to-end Qo values fo each equiement. The final stage is the aggegation of the Qo values fom the cloud levels detemined by the mapping schemes. The aggegation is done as follows: ist, the Qo values at the softwae level (the Rule # and Rule#3 cases only) ae obtained based to the claimed values in the LA. econd, the Qo values ae aggegated with the values End-to-End Availability (AV) AV (AV, DBaaAV) 58 ()

5 We measue AV at and DBaa using the MTT and MTTR paametes. Ou models ae pesented below: (4) The aart is obtained fom the aaproc value. The RT is calculated by summing up PROC and TRAN. imilaly, DBaaRT is calculated by summing up DBaaPROC and DBaaTRAN. ontology. WRL is a combination of OWL pesentation syntax and RuleML XML syntax. The WRL extension gives us the advantage of meging the knowledge base fom ou ontology with the mapping ules and Qo computation models. Theefoe, the easoning can be pefomed efficiently without the need fo additional intepetation. We use WRTab of Potégé [3] to wite the ules. To execute the mapping ules, we use a thid pat easoning agent called Pallet which can be integated as a plug ins into Potégé. In igue 5, we show one examples of WRL implementation fom ou Rule #. End-to-End Data Owneship (DO) Rule # : Response Time (t) AV= imtt / (imtt+imttr) DBaaAV= dbmtt / (dbmtt+dbmttr) (2) (3) End-to-End Response Time (RT) RT aart RT + DBaaRT DO (DO, DBaaDO) (5) The data owneship is calculated at each cloud level by measuing the similaity between the claimed policies of a cloud povide (e.g. and DBaa) and the pedefined data owneship policies. The latte epesents the standad policies fo maintaining the data contol in the cloud. CloudUse(?u) DBaa (?dbaas) (?iaas) aa(?saas) QoReq(?) susereq(?u,?) QoGua(?dbaasg) sqogua(?dbaas,?dbaasg)qogua(?iaasg) sqogua(?iaas,?iaasg) QoGua(?saasg) sqogua(?saas,?saasg) srtgua(?dbaasg,?dbaast) srtgua(?iaasg,?iaast) srtgua(?saasg,?saast) DBaaTRAN(?dbaast,?dbtans) DBaaPROC (?dbaast,?dbaaspoc) TRAN(?iaast,?itans) PROC (?iaast,?iaaspoc) aaproc(?saast,?saaspoc) swlb:add (?esultdbaast,?dbtans,?dbaaspoc) swlb:add (?esultiaast,?itans,?iaaspoc) swlb:add (?utlt,?esultdbaast,?esultiaast) swlb:add (?cloudt,?utlt,?saaspoc) QoMapped (?m) sqomapping (?,?m). End-to-End ecuity (U) (6) To compute the secuity value at each cloud level, we assume tt the values of the secuity metics ae pedefined in cloud envionment. To compute the aa s secuity we measue the similaity between the offeed access contol mecnisms (e.g. authoization, authentication) and the pedefined ones. In the same fashion, we compute the secuity at and DBaa levels by measuing the similaity between the offeed and the pedefined values consideing the thee metics (access contol, encyption algoithms and sevice pivacy). U aau, U, DBaaU igue 5. WRL-based model fo calcualting the end-to-end esonse time value fo cloud sevices. D. OWL based Reasoning In this section we demonstate the easoning capability of ou ontology. uppose a easoning agent tt, specifically, makes use of the OWL axioms (i.e., classes, subclasses, and popeties) and diffeent elationships (i.e., data-type popety and object popety) to infe knowledge necessay fo mapping and aggegating Qo values of diffeent cloud levels. Below we show some examples fo the easoning. End-to-End Cost () (7) = (aa+ιaa+dbaa) The sevice cost is calculated by summing up the cost values at the thee cloud levels. The agent undestands tt DBaa, and aa sevices ae subclasses of Cloud evice class. In ou example, each sevice is povided by one cloud povide. Cloud sevices can be povided by DBaa, o aa povide (only DBaa class is shown). Anonymous class is ceated tt s a popety "foundby" whee all instances ae values of the Cloud Use class. In this wok, we intoduce a method to aggegate the Qo values claimed acoss multiple cloud levels. We povide end uses with an appoximation of the end-to-end values which can be used to make decisions duing the matchmaking pocess. Thee ae othe aspects tt we may conside in ou futue wok such as sevices wokloads in the couse of time. </owlx: Class> <owlx: Class owlx: name= Cloud evice owlx: complete = false> <owlx: ObjectRestiction owlx: popety= foundby > <owlx: allvalueom owlx: class= Cloud Use /> </owlx: ObjectRestiction> </owlx: Class> <owlx: Class owlx: name= DBaa owlx: complete= false > <owlx: Class owlx: name= Cloud evice /> <owlx: ObjectRestiction owlx: popety= povidedby > <owlx: cadinality owlx: value= /> <owlx: allvalueom owlx: class = DBaa Povide /> </owlx: ObjectRestiction> </owlx: Class> C. OWL and WRL Pesentation OWL- is a semantic makup language fo web sevices which is witten using OWL technology. OWL is designed based on XML technology so the sevice inteopeability is achieved. We popose to use OWL- language to semantically define cloud sevices and thei associated Qo popeties along with thei diveging teminology. OWL facilitates a easoning agent s task to pocess the existing knowledge and infe the knowledge fo the Qo mapping. We use WRL to constuct ou poposed ules and the computation models, and integate them into ou OWL- The agent undestands tt Qo popeties of cloud sevices can be guaanteed and equested. Qo equiements and guaantees ae distinct fom each othe 59

6 (they ve no common values). It undestands tt Qo can be equested by cloud sevices and only aa sevices can ve Qo equiements. <owlx: DisjointClasses> <owlx: Class owlx: name= QoReq /> <owlx: Class owlx: name= QoGua /> </owlx: DisjointClasses> <owlx: ObjectPopety owlx: name= sereq > <owlx: domain owlx: class= Cloud evice /> <owlx: ange owlx: class= QoReq /> <owlx: allvalueom owlx: class= aa /> </owlx: ObjectRestiction> The agent undestands tt PefomanceQo and CustomeQo ae two types of Qo guaantees offeed by cloud sevices. The Qo guaantees of one type can neve be of othe types. o example the agent undestands tt availability is a PefomanceQo guaantee. The availability values can be povided as AV and DBaaAV instances. It can be measued using two vaiables: MTTR and MTT. Thei values must only belong to AV o DBaaAV. <owlx: DisjointClasses> <owlx: Class owlx: name= #PefomanceQo /> <owlx: Class owlx: name= #CustomeQo /> </owlx: DisjointClasses> <owlx: ObjectPopety owlx: name= savgua > <owlx: domain owlx: class= PefomanceQo /> <owlx: ange owlx: class= AV /> </owlx: ObjectPopety> <owlx: DatatypePopety owlx: name: MTTR > <owlx: domain> <owlx: UnionOf> <owlx: Class owlx:name= #AV /> <owlx: Class owlx:name= #DBaaAV /> </owlx:unionof> E. Illustating Example In ection II, we gave an example in which a company called Tinyoft aims to select the best utility sevices so tt togethe povide the best cloud solution (among the 24 solutions) to its end uses. In this section, we show how ou solution appoach can help in calculating the end-to-end Qo values of these solutions. uppose an end use submits he equest including he functional and Qo equiements. Let s conside the use s Qo equest mentioned in section II. ist, the mapping tool will detemine which mapping ule is applied on each equiement (i.e. cost, availability and esponse time). The Rule# is used fo the cost and esponse time equiements. The Rule#2 is used fo the availability equiement. The ontology will be used to semantically match the diffeent tems of Qo equiements and guaantees declaed by end uses and the cloud sevices. o example, a sevice cloud offe is Picing by cent in hou but a use equest is submitted as a Cost by dolla in a month. A moe complicated scenaio is when a povide claims the MTT and MTTR values but the use equests the Availability value. Afte esolving the syntactic matching poblem, the integated WRL model will calculate the endto-end values fo the Qo equiements. The availability at and DBaa levels is calculated by adding the measued (claimed) values of MTT and MTTR of the sevice and dividing the esult by MTT. Then the two values ae aggegated using the poduct opeation to get the end-to-end availability offeed to end uses. o the esponse time equiement, the end-to-end value is obtained by summing up the values at each cloud level. Table I and Table II show an example fo a solution (i.e. Tinyoft + + DBaa ) fom the set of the 24 available solutions. We calculate its end-to-end availability and esponse time values based on the claimed values of the associated metics by the povides (the Cost calculation is not shown fo space limitation). The units used fo the availability and esponse time metics ae hous and seconds espectively. TABLE I. THE CLAIMED VALUE O THE AOCIATED METRIC AND THE OBTAINED END-TO-END VALUE O THE AVAILABILITY. Potential Cloud olution Tinyoft + + DBaa Availability (AV) Tinyoft DBaa N/A imtt imttr dbmtt dbmttr End-to- End AV TABLE II. THE CLAIMED VALUE O THE AOCIATED METRIC AND THE OBTAINED END-TO-END VALUE O THE REPONE TIME. Potential Cloud olution Tinyoft + + DBaa Response Time (RT) Tinyoft DBaa aa- - - DBaa- DBaa- PROC PROC TRAN PROC TRAN IV. EXPERIMENT End-to- End RT The pupose of conducting ou expeiment is to answe the following question: Does the Qo mapping and aggegation pocess poposed in this pape incu much computational ovehead when integated to any sevice selection system? We pefom an efficiency test to answe the question. A. Desciption of the Qo Datasets In ou expeiments, we ve used fou souces of Qo data: i) two public eal wold Qo datasets fo web sevices, ii) a netwok pefomance data collected fom a netwok monitoing tool, iii) a claimed database pefomance data. The fist dataset was collected by E. Almasy et al [6]. The public dataset contains 2507 web sevices with a set of 9 Qo popeties. The dataset can be found in [6]. The second dataset was collected by Zheng et al. [25] which is pat of thei W-DREAM poject. It is geneated fom 4532 web sevices when invoked by 42 uses. In each invocation, values of two Qo popeties wee collected: esponse time and thoughput. The dataset can be found in [26]. These two datasets epesent aa s Qo data. The thid dataset is collected fom an Amazon EC2 instance located in Noth 4 520

7 Time(ms) Time(ms) Viginia. It is the monitoing esult of the CPU time (i.e. the EC2 instance esponse time). This dataset epesents the s Qo data. Moe infomation about this dataset can be found in [4]. o the DBaa s Qo data, we used the claimed esponse time by the povide (i.e. Clustix) which is 3-5 milliseconds [5]. B. Expeiments etup and Details In the expeiment, we evaluate the pefomance of the Qo mapping and aggegation pocess using the collected Qo datasets. In ou implementation, we used Java and Potégé API fo Java envionment to access ou Qo knowledge base. We an ou tests on a desktop machine unning unde Windows 7 with 64 bit, 3.6GHz Intel Duo2 CPU and 4 GB RAM. In the fist pat of the expeiment, we used the fist dataset combined with the Qo data of the and DBaa sevices to esemble a cloud solution. We inceased the numbe of sevices by 250 and we measued the time it takes to map and calculate the end-to-end Qo values. In the same way, we pefomed the second pat using the second dataset. We inceased the numbe of sevices by 500. igue 6 shows the esult of ou expeiment. om the esults we could see tt the execution time of the Qo mapping and aggegation pocess is in the anges of [-2.5] and [2-5] milliseconds in both pats (a) and (b) espectively. This amount of time s a minimal effect on the whole pocess of the sevice selection and anking Qo mapping execution time based on the fist dataset Numbe of sevices (in tens) (a) Qo mapping execution time based on the second dataset Numbe of sevices (in hundeds) (b) igue 6. Qo mapping pefomance tests. V. RELATED WORK Thee ae seveal poposals in the liteatue tt define the web sevices Qo popeties. Ontology-based methods ae widely adopted fo modeling Qo popeties. In [24], the poposed cloud ontology coves both functional sevice desciptions and non-functional sevice configuations of laye. It defines detailed concepts of popeties and thei measuement units such as compute, stoage and netwok. The aim is to select an appopiate sevice fo uses. In [7], an ontology based Qo model is poposed tt consides two aspects of the Qo in the cloud: Qo concens and Qo popeties. The latte descibes the cloud esouces and pice. The fome descibes the Qo constaints, influence and Qo weights. They futhe evaluate the model fom the use s pespective using the Analytical Hieachy Pocess (AHP) method. In [7], seveal ontologies ae developed to addess the business aspect of sevice offeings. It helps povides to bette define thei offes based on the chosen business stategy. Consequently, uses can ve a bette cnce to define thei equiements based on thei business demands. Othe appoaches use optimization methods to model Qo constaints and eventually select the cloud sevices. In [9], an impotant wok is pesented to model the CMI Qo popeties and calculate thei values based on sevices offes. A famewok is poposed to ndle the Qo management, monitoing and sevices anking. The AHP method is used fo optimizing the Qo citeia and ank cloud sevices. In [0], uses Qo constaints ae consideed in a multi-tenant aa envionment. The goal is to satisfy multiple uses equests while optimizing the sevice utilization. The optimization poblem is solved using the Constaint Optimization Poblem technique. In [8] a model fo selecting sevice based on pice and poximity aspects is poposed. The model is fomulated as a multi-objective optimization poblem. In [20], a cloud-based LA model is poposed tt descibes the LA between cloud sevices and thei uses by defining Qo guaantees. A LA model fo each cloud sevices is intoduced and a new LA language is developed fo this pupose. Reseaches in othe eseach domains (e.g., multimedia and netwok systems) popose to use Qo mapping to map netwok elated Qo popeties acoss multiple netwok components. In [6], a model is poposed fo vetically mapping Qo between uppe and lowe layes of a wieless netwok. In ode to povide an end-to-end Qo guaantee ove the heteogeneous netwoks to uses, the concept of abstact queue is used to model the Qo mapping by decomposing it into diffeent poblems at each netwok laye. In [9] a Qo mapping fo multi-use sessions in netwok systems is poposed. It consides multiple uses who submit thei equiements, and map them onto the most suitable netwok sevice class. Thee mapping methods ae poposed: pefect, sub-pefect and hybid match. 52

8 Ou appoach focuses on defining the mapping elationship between use s Qo equiements and guaantees of multiple clouds which paticipate in composing cloud solutions fo end uses. The goal is to compute the end-to-end Qo values of web sevices in cloud envionment fo the selection and ecommendation pocess. Most of the cuent cloud-based Qo models deal with single cloud sevice levels independently when modeling uses Qo equiements. They do not conside the collaboation of diffeent types of cloud sevices towads satisfying end uses equiements. In ou appoach, we conside the collaboation between the softwae sevice level and othe utility sevices to povide uses with complete solutions tt satisfy thei non-functional as well as the functional equiements. o this pupose, we map the use s Qo equiements onto the softwae sevice and the equied utility sevices. We use aggegation models to calculate the end-to-end Qo values which can be used fo the utility selection pocess. Thee ae moe aspects could be consideed to extend ou wok. One impotant aspect is to educe the candidate solutions space by using skylinebased technique such as the one pesented in [23]. VI. NCLUION In this pape, we popose to map the uses Qo equiements to the softwae level and to othe utility sevices in cloud envionment. oftwae sevices usually collaboate with othe cloud sevices to povide a complete solution to end uses. The goal of this eseach is to compute the end-to-end Qo values fo the sevice matchmaking and selection pocess. Ou mapping tool can help aa povides identify the pope Qo equiements they should ve on othe cloud utility sevices accoding to the equiements submitted by end uses. The expeiment esults depict tt using the mapping tool would not incu much computational ovehead when integated into a cloud sevice selection system. The ange of the execution time fo the mapping pocess is [ - 5ms] using the diffeent combinations of the afoementioned datasets. Cuently we ae impoving the expeiment by measuing the accuacy of the mapping pocess. In this wok, we only focus on cloud sevices collaboation to meet uses goals. Othe (non cloud) systems might be equied which is out of this eseach scope. In ou futue wok, we define the selection pocedue including adding objective functions and Qo pioities. ACKNOWLEDGMENT This wok is patially sponsoed by Natual cience and Engineeing Reseach Council of Canada (gant ) fo the second autho. REERENCE [] evice Management Index Vesion..0, 203, f40e- 47ad-b9a6-4f246cf7e34f. [2] WRL: A emantic Web Rule Language Combining OWL and RuleML, 203, [3] Potégé, 203, [4] PRTG Netwok Monito, Retieved in Januay 204. [5] Clustix, Retieved in Januay 204. [6] E. Almasy, Q. Mahmoud, Discoveing the best web sevice: a neual netwok-based solution, Poc. IEEE. Conf. ystems, Man, and Cybenetics, pp , [7] G. Chen, X. Bai, X. Huang, M. Li, L. 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