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

Size: px
Start display at page:

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

Transcription

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. 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, https://ptg.paessle.com/, 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. Zhou, Evaluating sevices on the cloud using ontology Qo model, Poc. the 6 th IEEE Inte. ymp. on evice Oiented ystem Engineeing, pp.32-37, 20. [8]. W. Choi, J.. He,. D. Kim, Qo metics fo evaluating sevices fom the pespective of sevice povides, Poc. IEEE Inte. Conf. on e-business Engineeing, pp , [9]. K. Geg, R. Vesteeg, R. Buyya, MICloud: a famewok fo computing and anking cloud sevices, Poc.of the 4 th IEEE Inte. Conf. on Utility and Cloud Computing, pp , 20. [0] Q. He, J. Han, Yang Y., J. Gundy, H. Jin, Qo-Diven sevice selection fo multi-tenant aa, Poc. 5 th IEEE Inte. Conf. on Cloud Computing, pp , 202. [] R. Kaim, C. Ding, An ennced PROMETHEE model fo Qobased web sevice selection, Poc. the 8 th IEEE Inte. Conf. on evice Computing, pp , 20. [2] K. Kitikos, D. Plexousakis, Mixed-Intege pogamming fo qosbased web sevice matchmaking, IEEE Tans. on evices Computing, vol.2, no.2, pp.22-39, [3] K. Kitikos, D. Plexousakis, emantic Qo metic matching, Poc. the 4 th Euopean Conf. on Web evices, pp , [4] X. Li, J. Wu,. Lu, Qo-awae sevice selection in geogaphically distibuted clouds, Poc. the IEEE Intenational Confeence on Compute Communications and Netwoks, pp. -5, 203. [5] Q. Ma, H. Wang, Y. Li, G. Xie, and. Liu, A semantic Qo-awae discovey famewok fo web sevices, Poc. IEEE Intenational Confeence on Web evices, pp.29-36, [6] M. Machese, M. Mongeli, Vetical Qo Mapping ove Wieless inteface,ieee Wieless Communications, vol. 6, no.2,apil [7] G. D. Modica, G. Petalia, O. Tomachio, A Business Ontology to Enable emantic Matchmaking in Open Cloud makets, Poc. the 8 th Inte. Conf. on emantics, Knowledge, and Gids, 202, pp [8] H. Qian, H. Zu, C. Cao, Q. Wang, C: facilitate the cloud sevice selection in platfoms, Poc. The Intenational Confeence on Collaboation technologies and ystems, pp , 203. [9]. B. Rakas, M. D. tojanovic, An efficient Qo mapping algoithm in multi-povide netwoks, Poc. the 34 th Inte. Conf. on Telecommunications and ignal Pocessing, pp , 20. [20] D. eano,. Bouchenak, Y. Kouki, T. Ledoux, J. Lejeune, J. opena, Towads Qo-Oiented LA guaantees fo online cloud sevices, Poc. The 3 th IEEE/ACM Intenational ymposium on Cluste, Cloud and Gid Conputing, pp , 203. [2]. Wagne,. Ishikawa,. Honiden, Applying Qo-awae sevice selection on functionally divese sevices, IC 20, pp. 00-3, 20. [22] X. Wang, Z. Wang, X. Xu., An impoved atificial bee colony appoach to qos-awae sevice selection, Poc.the IEEE 20 th Intenational Confeence on Web evices, pp , 203. [23] Q. Yu, A. Bouguettaya, Efficient sevice skyline computation fo composite sevice selection, IEEE Tan. On Knowledge And Data Engineeing, vol. 25, no.4, pp , Apil 203. [24] M. Zng, R. Ranjan, A. Halle, D. Geogakopoulos, M. Menzel,. Nepal, An ontology-based system fo cloud infastuctue sevices discovey, Poc. the 8 th Inte. Conf. on Collaboative Computing: Netwoking, Applications and Woksing, pp , 202. [25] Z. Zng, W-Deam, Retieved Januay, 204, [26] Z. Zheng, Y. Zng, and M. R. Lyu, Distibuted Qo evaluation fo eal-wold web sevices, ICW, July 5-0, pp.83-90,

HEALTHCARE INTEGRATION BASED ON CLOUD COMPUTING

HEALTHCARE INTEGRATION BASED ON CLOUD COMPUTING U.P.B. Sci. Bull., Seies C, Vol. 77, Iss. 2, 2015 ISSN 2286-3540 HEALTHCARE INTEGRATION BASED ON CLOUD COMPUTING Roxana MARCU 1, Dan POPESCU 2, Iulian DANILĂ 3 A high numbe of infomation systems ae available

More information

Software Engineering and Development

Software Engineering and Development I T H E A 67 Softwae Engineeing and Development SOFTWARE DEVELOPMENT PROCESS DYNAMICS MODELING AS STATE MACHINE Leonid Lyubchyk, Vasyl Soloshchuk Abstact: Softwae development pocess modeling is gaining

More information

An Approach to Optimized Resource Allocation for Cloud Simulation Platform

An Approach to Optimized Resource Allocation for Cloud Simulation Platform An Appoach to Optimized Resouce Allocation fo Cloud Simulation Platfom Haitao Yuan 1, Jing Bi 2, Bo Hu Li 1,3, Xudong Chai 3 1 School of Automation Science and Electical Engineeing, Beihang Univesity,

More information

A framework for the selection of enterprise resource planning (ERP) system based on fuzzy decision making methods

A framework for the selection of enterprise resource planning (ERP) system based on fuzzy decision making methods A famewok fo the selection of entepise esouce planning (ERP) system based on fuzzy decision making methods Omid Golshan Tafti M.s student in Industial Management, Univesity of Yazd Omidgolshan87@yahoo.com

More information

The transport performance evaluation system building of logistics enterprises

The transport performance evaluation system building of logistics enterprises Jounal of Industial Engineeing and Management JIEM, 213 6(4): 194-114 Online ISSN: 213-953 Pint ISSN: 213-8423 http://dx.doi.og/1.3926/jiem.784 The tanspot pefomance evaluation system building of logistics

More information

9:6.4 Sample Questions/Requests for Managing Underwriter Candidates

9:6.4 Sample Questions/Requests for Managing Underwriter Candidates 9:6.4 INITIAL PUBLIC OFFERINGS 9:6.4 Sample Questions/Requests fo Managing Undewite Candidates Recent IPO Expeience Please povide a list of all completed o withdawn IPOs in which you fim has paticipated

More information

INITIAL MARGIN CALCULATION ON DERIVATIVE MARKETS OPTION VALUATION FORMULAS

INITIAL MARGIN CALCULATION ON DERIVATIVE MARKETS OPTION VALUATION FORMULAS INITIAL MARGIN CALCULATION ON DERIVATIVE MARKETS OPTION VALUATION FORMULAS Vesion:.0 Date: June 0 Disclaime This document is solely intended as infomation fo cleaing membes and othes who ae inteested in

More information

A formalism of ontology to support a software maintenance knowledge-based system

A formalism of ontology to support a software maintenance knowledge-based system A fomalism of ontology to suppot a softwae maintenance knowledge-based system Alain Apil 1, Jean-Mac Deshanais 1, and Reine Dumke 2 1 École de Technologie Supéieue, 1100 Note-Dame West, Monteal, Canada

More information

Questions & Answers Chapter 10 Software Reliability Prediction, Allocation and Demonstration Testing

Questions & Answers Chapter 10 Software Reliability Prediction, Allocation and Demonstration Testing M13914 Questions & Answes Chapte 10 Softwae Reliability Pediction, Allocation and Demonstation Testing 1. Homewok: How to deive the fomula of failue ate estimate. λ = χ α,+ t When the failue times follow

More information

Automatic Testing of Neighbor Discovery Protocol Based on FSM and TTCN*

Automatic Testing of Neighbor Discovery Protocol Based on FSM and TTCN* Automatic Testing of Neighbo Discovey Potocol Based on FSM and TTCN* Zhiliang Wang, Xia Yin, Haibin Wang, and Jianping Wu Depatment of Compute Science, Tsinghua Univesity Beijing, P. R. China, 100084 Email:

More information

THE DISTRIBUTED LOCATION RESOLUTION PROBLEM AND ITS EFFICIENT SOLUTION

THE DISTRIBUTED LOCATION RESOLUTION PROBLEM AND ITS EFFICIENT SOLUTION IADIS Intenational Confeence Applied Computing 2006 THE DISTRIBUTED LOCATION RESOLUTION PROBLEM AND ITS EFFICIENT SOLUTION Jög Roth Univesity of Hagen 58084 Hagen, Gemany Joeg.Roth@Fenuni-hagen.de ABSTRACT

More information

Cloud Service Reliability: Modeling and Analysis

Cloud Service Reliability: Modeling and Analysis Cloud Sevice eliability: Modeling and Analysis Yuan-Shun Dai * a c, Bo Yang b, Jack Dongaa a, Gewei Zhang c a Innovative Computing Laboatoy, Depatment of Electical Engineeing & Compute Science, Univesity

More information

An application of stochastic programming in solving capacity allocation and migration planning problem under uncertainty

An application of stochastic programming in solving capacity allocation and migration planning problem under uncertainty An application of stochastic pogamming in solving capacity allocation and migation planning poblem unde uncetainty Yin-Yann Chen * and Hsiao-Yao Fan Depatment of Industial Management, National Fomosa Univesity,

More information

Scheduling Hadoop Jobs to Meet Deadlines

Scheduling Hadoop Jobs to Meet Deadlines Scheduling Hadoop Jobs to Meet Deadlines Kamal Kc, Kemafo Anyanwu Depatment of Compute Science Noth Caolina State Univesity {kkc,kogan}@ncsu.edu Abstact Use constaints such as deadlines ae impotant equiements

More information

Evaluating the impact of Blade Server and Virtualization Software Technologies on the RIT Datacenter

Evaluating the impact of Blade Server and Virtualization Software Technologies on the RIT Datacenter Evaluating the impact of and Vitualization Softwae Technologies on the RIT Datacente Chistophe M Butle Vitual Infastuctue Administato Rocheste Institute of Technology s Datacente Contact: chis.butle@it.edu

More information

IBM Research Smarter Transportation Analytics

IBM Research Smarter Transportation Analytics IBM Reseach Smate Tanspotation Analytics Laua Wynte PhD, Senio Reseach Scientist, IBM Watson Reseach Cente lwynte@us.ibm.com INSTRUMENTED We now have the ability to measue, sense and see the exact condition

More information

Database Management Systems

Database Management Systems Contents Database Management Systems (COP 5725) D. Makus Schneide Depatment of Compute & Infomation Science & Engineeing (CISE) Database Systems Reseach & Development Cente Couse Syllabus 1 Sping 2012

More information

883 Brochure A5 GENE ss vernis.indd 1-2

883 Brochure A5 GENE ss vernis.indd 1-2 ess x a eu / u e a. p o.eu c e / :/ http EURAXESS Reseaches in Motion is the gateway to attactive eseach caees in Euope and to a pool of wold-class eseach talent. By suppoting the mobility of eseaches,

More information

STUDENT RESPONSE TO ANNUITY FORMULA DERIVATION

STUDENT RESPONSE TO ANNUITY FORMULA DERIVATION Page 1 STUDENT RESPONSE TO ANNUITY FORMULA DERIVATION C. Alan Blaylock, Hendeson State Univesity ABSTRACT This pape pesents an intuitive appoach to deiving annuity fomulas fo classoom use and attempts

More information

Over-encryption: Management of Access Control Evolution on Outsourced Data

Over-encryption: Management of Access Control Evolution on Outsourced Data Ove-encyption: Management of Access Contol Evolution on Outsouced Data Sabina De Capitani di Vimecati DTI - Univesità di Milano 26013 Cema - Italy decapita@dti.unimi.it Stefano Paaboschi DIIMM - Univesità

More information

Modeling and Verifying a Price Model for Congestion Control in Computer Networks Using PROMELA/SPIN

Modeling and Verifying a Price Model for Congestion Control in Computer Networks Using PROMELA/SPIN Modeling and Veifying a Pice Model fo Congestion Contol in Compute Netwoks Using PROMELA/SPIN Clement Yuen and Wei Tjioe Depatment of Compute Science Univesity of Toonto 1 King s College Road, Toonto,

More information

Research on Risk Assessment of the Transformer Based on Life Cycle Cost

Research on Risk Assessment of the Transformer Based on Life Cycle Cost ntenational Jounal of Smat Gid and lean Enegy eseach on isk Assessment of the Tansfome Based on Life ycle ost Hui Zhou a, Guowei Wu a, Weiwei Pan a, Yunhe Hou b, hong Wang b * a Zhejiang Electic Powe opoation,

More information

Comparing Availability of Various Rack Power Redundancy Configurations

Comparing Availability of Various Rack Power Redundancy Configurations Compaing Availability of Vaious Rack Powe Redundancy Configuations By Victo Avela White Pape #48 Executive Summay Tansfe switches and dual-path powe distibution to IT equipment ae used to enhance the availability

More information

An Analysis of Manufacturer Benefits under Vendor Managed Systems

An Analysis of Manufacturer Benefits under Vendor Managed Systems An Analysis of Manufactue Benefits unde Vendo Managed Systems Seçil Savaşaneil Depatment of Industial Engineeing, Middle East Technical Univesity, 06531, Ankaa, TURKEY secil@ie.metu.edu.t Nesim Ekip 1

More information

Chapter 3 Savings, Present Value and Ricardian Equivalence

Chapter 3 Savings, Present Value and Ricardian Equivalence Chapte 3 Savings, Pesent Value and Ricadian Equivalence Chapte Oveview In the pevious chapte we studied the decision of households to supply hous to the labo maket. This decision was a static decision,

More information

Towards Automatic Update of Access Control Policy

Towards Automatic Update of Access Control Policy Towads Automatic Update of Access Contol Policy Jinwei Hu, Yan Zhang, and Ruixuan Li Intelligent Systems Laboatoy, School of Computing and Mathematics Univesity of Westen Sydney, Sydney 1797, Austalia

More information

ENABLING INFORMATION GATHERING PATTERNS FOR EMERGENCY RESPONSE WITH THE OPENKNOWLEDGE SYSTEM

ENABLING INFORMATION GATHERING PATTERNS FOR EMERGENCY RESPONSE WITH THE OPENKNOWLEDGE SYSTEM Computing and Infomatics, Vol. 29, 2010, 537 555 ENABLING INFORMATION GATHERING PATTERNS FOR EMERGENCY RESPONSE WITH THE OPENKNOWLEDGE SYSTEM Gaia Tecaichi, Veonica Rizzi, Mauizio Machese Depatment of

More information

An Efficient Group Key Agreement Protocol for Ad hoc Networks

An Efficient Group Key Agreement Protocol for Ad hoc Networks An Efficient Goup Key Ageement Potocol fo Ad hoc Netwoks Daniel Augot, Raghav haska, Valéie Issany and Daniele Sacchetti INRIA Rocquencout 78153 Le Chesnay Fance {Daniel.Augot, Raghav.haska, Valéie.Issany,

More information

AN IMPLEMENTATION OF BINARY AND FLOATING POINT CHROMOSOME REPRESENTATION IN GENETIC ALGORITHM

AN IMPLEMENTATION OF BINARY AND FLOATING POINT CHROMOSOME REPRESENTATION IN GENETIC ALGORITHM AN IMPLEMENTATION OF BINARY AND FLOATING POINT CHROMOSOME REPRESENTATION IN GENETIC ALGORITHM Main Golub Faculty of Electical Engineeing and Computing, Univesity of Zageb Depatment of Electonics, Micoelectonics,

More information

Concept and Experiences on using a Wiki-based System for Software-related Seminar Papers

Concept and Experiences on using a Wiki-based System for Software-related Seminar Papers Concept and Expeiences on using a Wiki-based System fo Softwae-elated Semina Papes Dominik Fanke and Stefan Kowalewski RWTH Aachen Univesity, 52074 Aachen, Gemany, {fanke, kowalewski}@embedded.wth-aachen.de,

More information

Reduced Pattern Training Based on Task Decomposition Using Pattern Distributor

Reduced Pattern Training Based on Task Decomposition Using Pattern Distributor > PNN05-P762 < Reduced Patten Taining Based on Task Decomposition Using Patten Distibuto Sheng-Uei Guan, Chunyu Bao, and TseNgee Neo Abstact Task Decomposition with Patten Distibuto (PD) is a new task

More information

Firstmark Credit Union Commercial Loan Department

Firstmark Credit Union Commercial Loan Department Fistmak Cedit Union Commecial Loan Depatment Thank you fo consideing Fistmak Cedit Union as a tusted souce to meet the needs of you business. Fistmak Cedit Union offes a wide aay of business loans and

More information

Memory-Aware Sizing for In-Memory Databases

Memory-Aware Sizing for In-Memory Databases Memoy-Awae Sizing fo In-Memoy Databases Kasten Molka, Giuliano Casale, Thomas Molka, Laua Mooe Depatment of Computing, Impeial College London, United Kingdom {k.molka3, g.casale}@impeial.ac.uk SAP HANA

More information

ON THE (Q, R) POLICY IN PRODUCTION-INVENTORY SYSTEMS

ON THE (Q, R) POLICY IN PRODUCTION-INVENTORY SYSTEMS ON THE R POLICY IN PRODUCTION-INVENTORY SYSTEMS Saifallah Benjaafa and Joon-Seok Kim Depatment of Mechanical Engineeing Univesity of Minnesota Minneapolis MN 55455 Abstact We conside a poduction-inventoy

More information

Tracking/Fusion and Deghosting with Doppler Frequency from Two Passive Acoustic Sensors

Tracking/Fusion and Deghosting with Doppler Frequency from Two Passive Acoustic Sensors Tacking/Fusion and Deghosting with Dopple Fequency fom Two Passive Acoustic Sensos Rong Yang, Gee Wah Ng DSO National Laboatoies 2 Science Pak Dive Singapoe 11823 Emails: yong@dso.og.sg, ngeewah@dso.og.sg

More information

An Epidemic Model of Mobile Phone Virus

An Epidemic Model of Mobile Phone Virus An Epidemic Model of Mobile Phone Vius Hui Zheng, Dong Li, Zhuo Gao 3 Netwok Reseach Cente, Tsinghua Univesity, P. R. China zh@tsinghua.edu.cn School of Compute Science and Technology, Huazhong Univesity

More information

High Availability Replication Strategy for Deduplication Storage System

High Availability Replication Strategy for Deduplication Storage System Zhengda Zhou, Jingli Zhou College of Compute Science and Technology, Huazhong Univesity of Science and Technology, *, zhouzd@smail.hust.edu.cn jlzhou@mail.hust.edu.cn Abstact As the amount of digital data

More information

Data Center Demand Response: Avoiding the Coincident Peak via Workload Shifting and Local Generation

Data Center Demand Response: Avoiding the Coincident Peak via Workload Shifting and Local Generation (213) 1 28 Data Cente Demand Response: Avoiding the Coincident Peak via Wokload Shifting and Local Geneation Zhenhua Liu 1, Adam Wieman 1, Yuan Chen 2, Benjamin Razon 1, Niangjun Chen 1 1 Califonia Institute

More information

Comparing Availability of Various Rack Power Redundancy Configurations

Comparing Availability of Various Rack Power Redundancy Configurations Compaing Availability of Vaious Rack Powe Redundancy Configuations White Pape 48 Revision by Victo Avela > Executive summay Tansfe switches and dual-path powe distibution to IT equipment ae used to enhance

More information

METHODOLOGICAL APPROACH TO STRATEGIC PERFORMANCE OPTIMIZATION

METHODOLOGICAL APPROACH TO STRATEGIC PERFORMANCE OPTIMIZATION ETHODOOGICA APPOACH TO STATEGIC PEFOANCE OPTIIZATION ao Hell * Stjepan Vidačić ** Željo Gaača *** eceived: 4. 07. 2009 Peliminay communication Accepted: 5. 0. 2009 UDC 65.02.4 This pape pesents a matix

More information

Effect of Contention Window on the Performance of IEEE 802.11 WLANs

Effect of Contention Window on the Performance of IEEE 802.11 WLANs Effect of Contention Window on the Pefomance of IEEE 82.11 WLANs Yunli Chen and Dhama P. Agawal Cente fo Distibuted and Mobile Computing, Depatment of ECECS Univesity of Cincinnati, OH 45221-3 {ychen,

More information

Office of Family Assistance. Evaluation Resource Guide for Responsible Fatherhood Programs

Office of Family Assistance. Evaluation Resource Guide for Responsible Fatherhood Programs Office of Family Assistance Evaluation Resouce Guide fo Responsible Fathehood Pogams Contents Intoduction........................................................ 4 Backgound..........................................................

More information

Efficient Redundancy Techniques for Latency Reduction in Cloud Systems

Efficient Redundancy Techniques for Latency Reduction in Cloud Systems Efficient Redundancy Techniques fo Latency Reduction in Cloud Systems 1 Gaui Joshi, Emina Soljanin, and Gegoy Wonell Abstact In cloud computing systems, assigning a task to multiple seves and waiting fo

More information

est using the formula I = Prt, where I is the interest earned, P is the principal, r is the interest rate, and t is the time in years.

est using the formula I = Prt, where I is the interest earned, P is the principal, r is the interest rate, and t is the time in years. 9.2 Inteest Objectives 1. Undestand the simple inteest fomula. 2. Use the compound inteest fomula to find futue value. 3. Solve the compound inteest fomula fo diffeent unknowns, such as the pesent value,

More information

Things to Remember. r Complete all of the sections on the Retirement Benefit Options form that apply to your request.

Things to Remember. r Complete all of the sections on the Retirement Benefit Options form that apply to your request. Retiement Benefit 1 Things to Remembe Complete all of the sections on the Retiement Benefit fom that apply to you equest. If this is an initial equest, and not a change in a cuent distibution, emembe to

More information

Promised Lead-Time Contracts Under Asymmetric Information

Promised Lead-Time Contracts Under Asymmetric Information OPERATIONS RESEARCH Vol. 56, No. 4, July August 28, pp. 898 915 issn 3-364X eissn 1526-5463 8 564 898 infoms doi 1.1287/ope.18.514 28 INFORMS Pomised Lead-Time Contacts Unde Asymmetic Infomation Holly

More information

Analyzing Ballistic Missile Defense System Effectiveness Based on Functional Dependency Network Analysis

Analyzing Ballistic Missile Defense System Effectiveness Based on Functional Dependency Network Analysis Send Odes fo Repints to epints@benthamscience.ae 678 The Open Cybenetics & Systemics Jounal, 2015, 9, 678-682 Open Access Analyzing Ballistic Missile Defense System Effectiveness Based on Functional Dependency

More information

Multiband Microstrip Patch Antenna for Microwave Applications

Multiband Microstrip Patch Antenna for Microwave Applications IOSR Jounal of Electonics and Communication Engineeing (IOSR-JECE) ISSN: 2278-2834, ISBN: 2278-8735. Volume 3, Issue 5 (Sep. - Oct. 2012), PP 43-48 Multiband Micostip Patch Antenna fo Micowave Applications

More information

Model-Driven Engineering of Adaptation Engines for Self-Adaptive Software: Executable Runtime Megamodels

Model-Driven Engineering of Adaptation Engines for Self-Adaptive Software: Executable Runtime Megamodels Model-Diven Engineeing of Adaptation Engines fo Self-Adaptive Softwae: Executable Runtime Megamodels Thomas Vogel, Holge Giese Technische Beichte N. 66 des Hasso-Plattne-Instituts fü Softwaesystemtechnik

More information

Channel selection in e-commerce age: A strategic analysis of co-op advertising models

Channel selection in e-commerce age: A strategic analysis of co-op advertising models Jounal of Industial Engineeing and Management JIEM, 013 6(1):89-103 Online ISSN: 013-0953 Pint ISSN: 013-843 http://dx.doi.og/10.396/jiem.664 Channel selection in e-commece age: A stategic analysis of

More information

Self-Adaptive and Resource-Efficient SLA Enactment for Cloud Computing Infrastructures

Self-Adaptive and Resource-Efficient SLA Enactment for Cloud Computing Infrastructures 2012 IEEE Fifth Intenational Confeence on Cloud Computing Self-Adaptive and Resouce-Efficient SLA Enactment fo Cloud Computing Infastuctues Michael Maue, Ivona Bandic Distibuted Systems Goup Vienna Univesity

More information

Electricity transmission network optimization model of supply and demand the case in Taiwan electricity transmission system

Electricity transmission network optimization model of supply and demand the case in Taiwan electricity transmission system Electicity tansmission netwok optimization model of supply and demand the case in Taiwan electicity tansmission system Miao-Sheng Chen a Chien-Liang Wang b,c, Sheng-Chuan Wang d,e a Taichung Banch Gaduate

More information

Financial Planning and Risk-return profiles

Financial Planning and Risk-return profiles Financial Planning and Risk-etun pofiles Stefan Gaf, Alexande Kling und Jochen Russ Pepint Seies: 2010-16 Fakultät fü Mathematik und Witschaftswissenschaften UNIERSITÄT ULM Financial Planning and Risk-etun

More information

Converting knowledge Into Practice

Converting knowledge Into Practice Conveting knowledge Into Pactice Boke Nightmae srs Tend Ride By Vladimi Ribakov Ceato of Pips Caie 20 of June 2010 2 0 1 0 C o p y i g h t s V l a d i m i R i b a k o v 1 Disclaime and Risk Wanings Tading

More information

Give me all I pay for Execution Guarantees in Electronic Commerce Payment Processes

Give me all I pay for Execution Guarantees in Electronic Commerce Payment Processes Give me all I pay fo Execution Guaantees in Electonic Commece Payment Pocesses Heiko Schuldt Andei Popovici Hans-Jög Schek Email: Database Reseach Goup Institute of Infomation Systems ETH Zentum, 8092

More information

Review Graph based Online Store Review Spammer Detection

Review Graph based Online Store Review Spammer Detection Review Gaph based Online Stoe Review Spamme Detection Guan Wang, Sihong Xie, Bing Liu, Philip S. Yu Univesity of Illinois at Chicago Chicago, USA gwang26@uic.edu sxie6@uic.edu liub@uic.edu psyu@uic.edu

More information

Financial Derivatives for Computer Network Capacity Markets with Quality-of-Service Guarantees

Financial Derivatives for Computer Network Capacity Markets with Quality-of-Service Guarantees Financial Deivatives fo Compute Netwok Capacity Makets with Quality-of-Sevice Guaantees Pette Pettesson pp@kth.se Febuay 2003 SICS Technical Repot T2003:03 Keywods Netwoking and Intenet Achitectue. Abstact

More information

SUPPORT VECTOR MACHINE FOR BANDWIDTH ANALYSIS OF SLOTTED MICROSTRIP ANTENNA

SUPPORT VECTOR MACHINE FOR BANDWIDTH ANALYSIS OF SLOTTED MICROSTRIP ANTENNA Intenational Jounal of Compute Science, Systems Engineeing and Infomation Technology, 4(), 20, pp. 67-7 SUPPORT VECTOR MACHIE FOR BADWIDTH AALYSIS OF SLOTTED MICROSTRIP ATEA Venmathi A.R. & Vanitha L.

More information

Optimal Peer Selection in a Free-Market Peer-Resource Economy

Optimal Peer Selection in a Free-Market Peer-Resource Economy Optimal Pee Selection in a Fee-Maket Pee-Resouce Economy Micah Adle, Rakesh Kuma, Keith Ross, Dan Rubenstein, David Tune and David D Yao Dept of Compute Science Univesity of Massachusetts Amhest, MA; Email:

More information

A Comparative Analysis of Data Center Network Architectures

A Comparative Analysis of Data Center Network Architectures A Compaative Analysis of Data Cente Netwok Achitectues Fan Yao, Jingxin Wu, Guu Venkataamani, Suesh Subamaniam Depatment of Electical and Compute Engineeing, The Geoge Washington Univesity, Washington,

More information

Uncertain Version Control in Open Collaborative Editing of Tree-Structured Documents

Uncertain Version Control in Open Collaborative Editing of Tree-Structured Documents Uncetain Vesion Contol in Open Collaboative Editing of Tee-Stuctued Documents M. Lamine Ba Institut Mines Télécom; Télécom PaisTech; LTCI Pais, Fance mouhamadou.ba@ telecom-paistech.f Talel Abdessalem

More information

An Infrastructure Cost Evaluation of Single- and Multi-Access Networks with Heterogeneous Traffic Density

An Infrastructure Cost Evaluation of Single- and Multi-Access Networks with Heterogeneous Traffic Density An Infastuctue Cost Evaluation of Single- and Multi-Access Netwoks with Heteogeneous Taffic Density Andes Fuuskä and Magnus Almgen Wieless Access Netwoks Eicsson Reseach Kista, Sweden [andes.fuuska, magnus.almgen]@eicsson.com

More information

Mining Relatedness Graphs for Data Integration

Mining Relatedness Graphs for Data Integration Mining Relatedness Gaphs fo Data Integation Jeemy T. Engle (jtengle@indiana.edu) Ying Feng (yingfeng@indiana.edu) Robet L. Goldstone (goldsto@indiana.edu) Indiana Univesity Bloomington, IN. 47405 USA Abstact

More information

EXPERIENCE OF USING A CFD CODE FOR ESTIMATING THE NOISE GENERATED BY GUSTS ALONG THE SUN- ROOF OF A CAR

EXPERIENCE OF USING A CFD CODE FOR ESTIMATING THE NOISE GENERATED BY GUSTS ALONG THE SUN- ROOF OF A CAR EXPERIENCE OF USING A CFD CODE FOR ESTIMATING THE NOISE GENERATED BY GUSTS ALONG THE SUN- ROOF OF A CAR Liang S. Lai* 1, Geogi S. Djambazov 1, Choi -H. Lai 1, Koulis A. Peicleous 1, and Fédéic Magoulès

More information

Ilona V. Tregub, ScD., Professor

Ilona V. Tregub, ScD., Professor Investment Potfolio Fomation fo the Pension Fund of Russia Ilona V. egub, ScD., Pofesso Mathematical Modeling of Economic Pocesses Depatment he Financial Univesity unde the Govenment of the Russian Fedeation

More information

Adaptive Queue Management with Restraint on Non-Responsive Flows

Adaptive Queue Management with Restraint on Non-Responsive Flows Adaptive Queue Management wi Restaint on Non-Responsive Flows Lan Li and Gyungho Lee Depatment of Electical and Compute Engineeing Univesity of Illinois at Chicago 85 S. Mogan Steet Chicago, IL 667 {lli,

More information

Chris J. Skinner The probability of identification: applying ideas from forensic statistics to disclosure risk assessment

Chris J. Skinner The probability of identification: applying ideas from forensic statistics to disclosure risk assessment Chis J. Skinne The pobability of identification: applying ideas fom foensic statistics to disclosue isk assessment Aticle (Accepted vesion) (Refeeed) Oiginal citation: Skinne, Chis J. (2007) The pobability

More information

who supply the system vectors for their JVM products. 1 HBench:Java will work best with support from JVM vendors

who supply the system vectors for their JVM products. 1 HBench:Java will work best with support from JVM vendors Appeaed in the ACM Java Gande 2000 Confeence, San Fancisco, Califonia, June 3-5, 2000 HBench:Java: An Application-Specific Benchmaking Famewok fo Java Vitual Machines Xiaolan Zhang Mago Seltze Division

More information

Instructions to help you complete your enrollment form for HPHC's Medicare Supplemental Plan

Instructions to help you complete your enrollment form for HPHC's Medicare Supplemental Plan Instuctions to help you complete you enollment fom fo HPHC's Medicae Supplemental Plan Thank you fo applying fo membeship to HPHC s Medicae Supplement plan. Pio to submitting you enollment fom fo pocessing,

More information

Distributed Computing and Big Data: Hadoop and MapReduce

Distributed Computing and Big Data: Hadoop and MapReduce Distibuted Computing and Big Data: Hadoop and Map Bill Keenan, Diecto Tey Heinze, Achitect Thomson Reutes Reseach & Development Agenda R&D Oveview Hadoop and Map Oveview Use Case: Clusteing Legal Documents

More information

A Two-Step Tabu Search Heuristic for Multi-Period Multi-Site Assignment Problem with Joint Requirement of Multiple Resource Types

A Two-Step Tabu Search Heuristic for Multi-Period Multi-Site Assignment Problem with Joint Requirement of Multiple Resource Types Aticle A Two-Step Tabu Seach Heuistic fo Multi-Peiod Multi-Site Assignment Poblem with Joint Requiement of Multiple Resouce Types Siavit Swangnop and Paveena Chaovalitwongse* Depatment of Industial Engineeing,

More information

Peer-to-Peer File Sharing Game using Correlated Equilibrium

Peer-to-Peer File Sharing Game using Correlated Equilibrium Pee-to-Pee File Shaing Game using Coelated Equilibium Beibei Wang, Zhu Han, and K. J. Ray Liu Depatment of Electical and Compute Engineeing and Institute fo Systems Reseach, Univesity of Mayland, College

More information

Referral service and customer incentive in online retail supply Chain

Referral service and customer incentive in online retail supply Chain Refeal sevice and custome incentive in online etail supply Chain Y. G. Chen 1, W. Y. Zhang, S. Q. Yang 3, Z. J. Wang 4 and S. F. Chen 5 1,,3,4 School of Infomation Zhejiang Univesity of Finance and Economics

More information

Transmittal 198 Date: DECEMBER 9, 2005. SUBJECT: Termination of the Existing Eligibility-File Based Crossover Process at All Medicare Contractors

Transmittal 198 Date: DECEMBER 9, 2005. SUBJECT: Termination of the Existing Eligibility-File Based Crossover Process at All Medicare Contractors anual ystem Depatment of ealth & uman evices (D) entes fo edicae & Pub 100-20 One-Time Notification edicaid evices () Tansmittal 198 Date: DEEBE 9, 2005 hange equest 4231 UBJET: Temination of the Existing

More information

The LCOE is defined as the energy price ($ per unit of energy output) for which the Net Present Value of the investment is zero.

The LCOE is defined as the energy price ($ per unit of energy output) for which the Net Present Value of the investment is zero. Poject Decision Metics: Levelized Cost of Enegy (LCOE) Let s etun to ou wind powe and natual gas powe plant example fom ealie in this lesson. Suppose that both powe plants wee selling electicity into the

More information

Power Monitoring and Control for Electric Home Appliances Based on Power Line Communication

Power Monitoring and Control for Electric Home Appliances Based on Power Line Communication I²MTC 2008 IEEE Intenational Instumentation and Measuement Technology Confeence Victoia, Vancouve Island, Canada, May 12 15, 2008 Powe Monitoing and Contol fo Electic Home Appliances Based on Powe Line

More information

Modal Characteristics study of CEM-1 Single-Layer Printed Circuit Board Using Experimental Modal Analysis

Modal Characteristics study of CEM-1 Single-Layer Printed Circuit Board Using Experimental Modal Analysis Available online at www.sciencediect.com Pocedia Engineeing 41 (2012 ) 1360 1366 Intenational Symposium on Robotics and Intelligent Sensos 2012 (IRIS 2012) Modal Chaacteistics study of CEM-1 Single-Laye

More information

867 Product Transfer and Resale Report

867 Product Transfer and Resale Report 867 Poduct Tansfe and Resale Repot Functional Goup ID=PT Intoduction: This X12 Tansaction Set contains the fomat and establishes the data contents of the Poduct Tansfe and Resale Repot Tansaction Set (867)

More information

Statistics and Data Analysis

Statistics and Data Analysis Pape 274-25 An Extension to SAS/OR fo Decision System Suppot Ali Emouznead Highe Education Funding Council fo England, Nothavon house, Coldhabou Lane, Bistol, BS16 1QD U.K. ABSTRACT This pape exploes the

More information

The impact of migration on the provision. of UK public services (SRG.10.039.4) Final Report. December 2011

The impact of migration on the provision. of UK public services (SRG.10.039.4) Final Report. December 2011 The impact of migation on the povision of UK public sevices (SRG.10.039.4) Final Repot Decembe 2011 The obustness The obustness of the analysis of the is analysis the esponsibility is the esponsibility

More information

Loyalty Rewards and Gift Card Programs: Basic Actuarial Estimation Techniques

Loyalty Rewards and Gift Card Programs: Basic Actuarial Estimation Techniques Loyalty Rewads and Gift Cad Pogams: Basic Actuaial Estimation Techniques Tim A. Gault, ACAS, MAAA, Len Llaguno, FCAS, MAAA and Matin Ménad, FCAS, MAAA Abstact In this pape we establish an actuaial famewok

More information

MATHEMATICAL SIMULATION OF MASS SPECTRUM

MATHEMATICAL SIMULATION OF MASS SPECTRUM MATHEMATICA SIMUATION OF MASS SPECTUM.Beánek, J.Knížek, Z. Pulpán 3, M. Hubálek 4, V. Novák Univesity of South Bohemia, Ceske Budejovice, Chales Univesity, Hadec Kalove, 3 Univesity of Hadec Kalove, Hadec

More information

Financing Terms in the EOQ Model

Financing Terms in the EOQ Model Financing Tems in the EOQ Model Habone W. Stuat, J. Columbia Business School New Yok, NY 1007 hws7@columbia.edu August 6, 004 1 Intoduction This note discusses two tems that ae often omitted fom the standad

More information

Approximation Algorithms for Data Management in Networks

Approximation Algorithms for Data Management in Networks Appoximation Algoithms fo Data Management in Netwoks Chistof Kick Heinz Nixdof Institute and Depatment of Mathematics & Compute Science adebon Univesity Gemany kueke@upb.de Haald Räcke Heinz Nixdof Institute

More information

Development of Mathematical Model for Market-Oriented Cloud Computing

Development of Mathematical Model for Market-Oriented Cloud Computing Intenational Jounal of Compute Applications (0975 8887) Volume 9 No.11, Novembe 2010 Development of Mathematical Model fo Maket-Oiented Cloud Computing K.Mukhejee Depatment of Compute Science & Engineeing.

More information

Exam #1 Review Answers

Exam #1 Review Answers xam #1 Review Answes 1. Given the following pobability distibution, calculate the expected etun, vaiance and standad deviation fo Secuity J. State Pob (R) 1 0.2 10% 2 0.6 15 3 0.2 20 xpected etun = 0.2*10%

More information

COMPLYING WITH THE DRUG-FREE SCHOOLS AND CAMPUSES REGULATIONS

COMPLYING WITH THE DRUG-FREE SCHOOLS AND CAMPUSES REGULATIONS Highe Education Cente fo Alcohol and Othe Dug Abuse and Violence Pevention Education Development Cente, Inc. 55 Chapel Steet Newton, MA 02458-1060 COMPLYING WITH THE DRUG-FREE SCHOOLS AND CAMPUSES REGULATIONS

More information

Supporting Efficient Top-k Queries in Type-Ahead Search

Supporting Efficient Top-k Queries in Type-Ahead Search Suppoting Efficient Top-k Queies in Type-Ahead Seach Guoliang Li Jiannan Wang Chen Li Jianhua Feng Depatment of Compute Science, Tsinghua National Laboatoy fo Infomation Science and Technology (TNList),

More information

Real Time Tracking of High Speed Movements in the Context of a Table Tennis Application

Real Time Tracking of High Speed Movements in the Context of a Table Tennis Application Real Time Tacking of High Speed Movements in the Context of a Table Tennis Application Stephan Rusdof Chemnitz Univesity of Technology D-09107, Chemnitz, Gemany +49 371 531 1533 stephan.usdof@infomatik.tu-chemnitz.de

More information

Manual ultrasonic inspection of thin metal welds

Manual ultrasonic inspection of thin metal welds Manual ultasonic inspection of thin metal welds Capucine Capentie and John Rudlin TWI Cambidge CB1 6AL, UK Telephone 01223 899000 Fax 01223 890689 E-mail capucine.capentie@twi.co.uk Abstact BS EN ISO 17640

More information

Timing Synchronization in High Mobility OFDM Systems

Timing Synchronization in High Mobility OFDM Systems Timing Synchonization in High Mobility OFDM Systems Yasamin Mostofi Depatment of Electical Engineeing Stanfod Univesity Stanfod, CA 94305, USA Email: yasi@wieless.stanfod.edu Donald C. Cox Depatment of

More information

DMIF based QoS Management for MPEG-4 Multimedia Streaming: ATM and RSVP/IP Case Studies

DMIF based QoS Management for MPEG-4 Multimedia Streaming: ATM and RSVP/IP Case Studies DMIF based QoS Management fo MPEG-4 Multimedia Steaming: ATM and RSVP/IP Case Studies Victo Maques 1, Ricado Cadime 2, Amao de Sousa 1, A. M. Oliveia Duate 1 1 Instituto de Telecomunicações, Univesidade

More information

Avoided emissions kgco 2eq /m 2 Reflecting surfaces 130

Avoided emissions kgco 2eq /m 2 Reflecting surfaces 130 ANALYSIS OF GLOBAL WARMING MITIGATION BY WHITE REFLECTING SURFACES Fedeico Rossi, Andea Nicolini Univesity of Peugia, CIRIAF Via G.Duanti 67 0615 Peugia, Italy T: +9-075-585846; F: +9-075-5848470; E: fossi@unipg.it

More information

Strength Analysis and Optimization Design about the key parts of the Robot

Strength Analysis and Optimization Design about the key parts of the Robot Intenational Jounal of Reseach in Engineeing and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Pint): 2320-9356 www.ijes.og Volume 3 Issue 3 ǁ Mach 2015 ǁ PP.25-29 Stength Analysis and Optimization Design

More information

Optimizing Content Retrieval Delay for LT-based Distributed Cloud Storage Systems

Optimizing Content Retrieval Delay for LT-based Distributed Cloud Storage Systems Optimizing Content Retieval Delay fo LT-based Distibuted Cloud Stoage Systems Haifeng Lu, Chuan Heng Foh, Yonggang Wen, and Jianfei Cai School of Compute Engineeing, Nanyang Technological Univesity, Singapoe

More information

Controlling the Money Supply: Bond Purchases in the Open Market

Controlling the Money Supply: Bond Purchases in the Open Market Money Supply By the Bank of Canada and Inteest Rate Detemination Open Opeations and Monetay Tansmission Mechanism The Cental Bank conducts monetay policy Bank of Canada is Canada's cental bank supevises

More information

An Introduction to Omega

An Introduction to Omega An Intoduction to Omega Con Keating and William F. Shadwick These distibutions have the same mean and vaiance. Ae you indiffeent to thei isk-ewad chaacteistics? The Finance Development Cente 2002 1 Fom

More information

California s Duals Demonstration: A Transparent. Process. Margaret Tatar Chief, Medi-Cal Managed Care Division. CA Coo 8/21/12

California s Duals Demonstration: A Transparent. Process. Margaret Tatar Chief, Medi-Cal Managed Care Division. CA Coo 8/21/12 Califonia s Duals Demonstation: A Tanspaent and Inclusive Stakeholde Pocess Magaet Tata Chief, Medi-Cal Managed Cae Division Depatment of Health Cae Sevices 1 Stakeholde Engagement 1. 2. Inclusive Building

More information

Questions for Review. By buying bonds This period you save s, next period you get s(1+r)

Questions for Review. By buying bonds This period you save s, next period you get s(1+r) MACROECONOMICS 2006 Week 5 Semina Questions Questions fo Review 1. How do consumes save in the two-peiod model? By buying bonds This peiod you save s, next peiod you get s() 2. What is the slope of a consume

More information