Dynamic Provisioning Modeling for Virtualized Multi-tier Applications in Cloud Data Center

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1 200 IEEE 3rd Iteratoal Coferece o Cloud Computg Dyamc Provsog Modelg for Vrtualzed Mult-ter Applcatos Cloud Data Ceter Jg B 3 Zhlag Zhu 2 Ruxog Ta 3 Qgbo Wag 3 School of Iformato Scece ad Egeerg College of Software 2 Northeaster Uversty Sheyag 0004 P.R. Cha IBM Cha Research Lab 3 Bejg P.R. Cha E-mal: eubjg@gmal.com zzl@mal.eu.edu.c {tarux wagqbo}@c.bm.com Abstract Dyamc provsog s a useful techque for hadlg the vrtualzed mult-ter applcatos cloud evromet. Uderstadg the performace of vrtualzed mult-ter applcatos s crucal for effcet cloud frastructure maagemet. I ths paper we preset a ovel dyamc provsog techque for a cluster-based vrtualzed mult-ter applcato that employ a flexble hybrd queueg model to determe the umber of vrtual maches at each ter a vrtualzed applcato. We preset a cloud data ceter based o vrtual mache to optmze resources provsog. Usg smulato expermets of three-ter applcato we adopt a optmzato model to mmze the total umber of vrtual maches whle satsfyg the customer average respose tme costrat ad the request arrval rate costrat. Our expermets show that cloud data ceter resources ca be allocated accurately wth these techques ad the extra cost ca be effectvely reduced. Keywords-cloud computg; resource provsog; vrtualzed applcato; performace modelg I. INTRODUCTION Vrtualzato techologes have facltated the realzato of cloud computg servces []. Cloud computg [2 3] cludes three kds of computg capactes as a servce dfferet abstracto levels for dfferet busess purposes such as Ifrastructure as a Servce (IaaS) Platform as a Servce (PaaS) ad Software as a Servce (SaaS). Here we cosder oly IaaS whch ams to provde computg resources or storage as a servce to customers. Oe major player cloud computg s Amazo s Elastc Compute Cloud (EC2) whch comprses several data ceters worldwde. Amazo EC2 lets customers deploy vrtual maches (VMs) o-demad o Amazo s frastructure ad pay oly for the computg storage ad etwork resources they use. Whle a umber of recet papers address vrtualzato of eterprse applcatos such as resource vrtualzato [4 5] o-demad resource provsog maagemet based o vrtual maches [6 7] ad QoS maagemet of vrtual mache [8]. These works lead to mprovemets the performace of vrtualzato ad resource utlzatos. Sce IT frastructure customer requremets for cloud frastructure servces are vared frastructure provders have to esure that they ca be flexble ther servce delvery whle keepg the frastructure servce customers effcetly creasg ablty of commodty hardware to ru applcatos wth VMs. VMs allow both the solato of applcatos from the uderlyg hardware ad other VMs ad the customzato of the frastructure resource to meet the requremets of the IT frastructure customer. However the applcato of vrtualzato techologes shows ts advatages for further challeges such as the tellget allocato of VM resources for maagg computg resource demads of the frastructure customers. I addto eterprse IT frastructure customers wth vrtualzed applcatos requre lesser resource cost ad thus save resource by dstrbutg workload requests to vrtualzed mult-ter applcatos cloud evromet. Ths creates the eed for establshg a computg atmosphere for dyamcally provsog cloud resources from mult-ter domas wth ad across eterprses. Furthermore there are may ope challeges volved o-demad resources dyamc provsog for cloud data ceters such as the CPU memory dsk ad etwork badwdth to be parttoed amog the resdet VMs ad optmal cofgurato for VMs. Therefore ths paper cosders the curret treds the evromet of cloud computg ad presets relatoshps betwee performace ad resources provsog of vrtualzed applcatos. I order to address the above challege ths paper proposes a hybrd model for dyamc resource provsog VM-based cloud data ceter whch ca be based o combato of a M/M/c model ad multple M/M/ queueg models methods. Such model s mportat for the followg reasos: () Resource provsog a umber of cluster VMs are dedcated to vrtualzed mult-ter applcato ad the model must determe how may VMs are allocated to vrtualzed mult-ter applcato to satsfy the requremet of gve respose tme for customers that s the model ca ot oly meet the eeds of customers but also cause lttle waste of resources. (2) VM cofgurato whch eables varous cofgurato parameters of the VM to be determed for a certa performace goal. Thus we ca determe the most effectve resource utlzato by VM optmal cofgurato. The ma cotrbutos of the paper clude: () We develop a hybrd model composed of a M/M/c model ad multple M/M/ models to provso computg resources for vrtualzed applcato; (2) Based o the proposed hybrd model a optmzato model to mmze the total /0 $ IEEE DOI 0.09/CLOUD

2 umber of vrtual maches for computg resources s developed ad the proposed optmzato model s verfed wth the three-ter vrtualzed applcato of hadlg dyamc workloads through smulato. The rest of ths paper s orgazed as follows. I Secto 2 we descrbe the frastructure maagemet of a cloud evromet for vrtualzed mult-ter applcatos. Secto 3 presets the aalytc models used to solve the defed problem. Secto 4 demostrates the results of prototype expermets. I Secto 5 we revew some related work the area of dyamc ad scalable resources provsog. Cocludg remarks ad dscusso about future work are gve Secto 6. II. THE DYNAMIC VIRTUAL MACHINES IN CLOUD DATA CENTER I order to dyamcally provsog resources for vrtualzed mult-ter applcato executo evromets (VAEEs) of dfferet customers the most commo approaches are based o self-maagg techques [9] such as Motor Aalyze Pla ad Execute (MAPE) cotrol loops archtecture s eeded. The goal s to meet the vrtualzed applcato requremets whle adaptg IT archtecture to workload varatos. Usually each request requres the executo of vrtualzed applcato allocated o the VM of each physcal ter. A cloud data ceter eables multple vrtualzed applcatos may be creased whe workload creases ad reduced whe workload reduces. Ths dyamc resource provso allows flexble respose tme a VAEE where peak workload s much greater tha the ormal steady state. Fgure provdes a hgh-level dyamc resource provso archtecture for cloud data ceter whch shows relatoshps betwee computatoal resources pool ad self-maagemet commuty. Computatoal Resources Pool cotas physcal resources ad vrtualzed resources. Plety of VMs hold several VAEEs sharg the capacty of physcal resources ad ca solate multple applcatos from the uderlyg hardware. VMs of each ter of a vrtualzed applcato may correspod to a physcal mache. Computatoal resources pool delegates self-maagemet commuty for satsfyg the requremet goal of the customer to automatcally allocate suffcet resources to the each ter of vrtualzed applcato. Self-maagemet commuty meas mechasms to automate the VMs of cofgurg ad tug the vrtualzed mult-ter applcato so as to mata the respose tme requremets of the dfferet customers. It geerates result of ru-tme provsog for cloud data ceter. It cludes four compoets as follows: Motor: collects the workload ad the performace metrc of all rug VAEEs such as the request arrval rate the average servce tme ad the CPU utlzato etc. Aalyzer: receves ad aalyzes the measuremets from the motor to estmate the future workload. It also receves the respose tmes of dfferet customers. Resource Scheduler: sets up performace aalytc models for each ter of the VAEE ad uses ts optmzer wth the optmzato model to determe resource provsog accordg to these workload estmates ad respose tme costras of dfferet customer such that the resource requremets of the overall VAEE s mmzed. Vrtualzed applcato Executor: assgs the VM cofgurato ad the rus the VAEEs to satsfy the resource requremets of the dfferet customers accordg to the optmzed decso. Fgure. The dyamc resource provsog of cloud data ceter Ths paper focuses o the desg of resource scheduler for vrtualzed mult-ter applcatos. The goal s to mmze the usg of resources uder a workload whle satsfyg dfferet customer for the costrats of average respose tme. III. VIRTUALIZED MULTI-TIER APPLICATION QUEUEING MODEL I ths secto we preset a hybrd queueg model for a vrtualzed mult-ter applcato ad the defe a olear costraed optmzato problem for dyamc resource provsog. Moreover the optmal model of relatoshps betwee performace ad resources provsog s used to maxmze resources utlzato accordg to the respose tme of customer requremet. A. Aalytc Performace Model A vrtualzed mult-ter applcato cloud computg evromet s deployed o multple vrtual maches (VMs) ad each ter provdes certa fuctoalty to ts precedg ter. Here we cosder a ole e-commerce applcato that cossts of ters deoted by T T 2... T. We assume that there are c parallel detcal VMs the each ter of VAEE ad the requests of all the arrvg sessos eter to a commo queue mataed by the odemad scheduler (ODS) watg for avalable resources the frst ter. The ODS schedules these requests of each sesso. Note that our system scheduler decsos are made oly for the frst ter of the vrtualzed applcato. Oce scheduled a request s decded processg at the VMs of the frst ter wth the vrtualzed applcato. 37

3 Dspatcher of other ters s used for collectg requests processed the pre-ter ad dstrbutg them to multple parallel VMs queueg models of that ter to execute. Multple VMs queueg models of other ters are resposble for dyamc resource provsog by the requests of that ter. Each ter s assumed to employ a perfect load-balacg elemet for a vrtualzed applcato that s resposble for processg requests at that ter ad each request s forwarded to ts succeedg ter for further processg. Oce the result s processed by the fal ter T the results are set back by each ter the reverse order utl t reaches T whch the seds the results to the customer. I more complex processg scearos each request at ter T ca trgger zero or multple requests to ter T +. For example a statc web page request s processed by the Web ter etrely ad wll ot be forwarded to the followg ters. O the other had a keyword search at a Web ter may trgger multple requests to the ext ter. We also assume that database ter wth a shared-everythg archtecture [0] whch ca be clustered ad replcated o-demad. Through modelg all the ters ad ther teractos our mult-ter model allows us to tegrate decsos for the frst ter to scheduler ad other ters to each VM. Therefore the amout of cocurrecy VMs may be determed by the umber of cocurret requests that ter supports accordg to our model. I order to capture the vrtualzed mult-ter applcato for dyamc resources provsog we defe that our model s a hybrd aalytcal model whch accords wth real evromet. Ths ot oly saves the etwork trasmsso tme but also mproves the processg effcecy of the request. B. Ope Queueg Model of Vrtualzed mult-ter applcato The workload o the vrtualzed mult-ter applcato s typcally sesso-based customer where a customer sesso cossts of a successo of requests. At a tme multple cocurret customer requests teract wth the vrtualzed mult-ter applcato. I order to capture the multple cocurret requests of customer sessos we model the servg system as a M/M/c queueg system to the frst ter ad other ters ca be modeled as multple M/M/ queueg systems. The requests of each ter are servced a frst-come-frst-served (FCFS) order as show fgure 2. λ s μ μ 2 μ c λ s2 λ s22 λ sc 2 2 μ 2 μ c 2 2 λ s3 λ s23 λ s 2 μ 22 μ 23 μ2 λ sc 3 3 μ 3 μ c 3 3 λ s λ s c Fgure. 2 Ope queueg model for vrtualzed mult-ter applcato Fgure 2 shows the ope vrtualzed mult-ter applcato queueg models. The arrval of requests at the each ter for a vrtualzed applcato s assumed to be descrbed by a Posso dstrbuto [ 2].e. ter-arrval tme μ μ c betwee requests subjects to the egatve expoetal dstrbuto. Ths assumpto s valdated by aalyzg traces take from a e-commerce mult-ter applcato webste. Besdes the average servce tme of requests s also cosdered the egatve expoetal dstrbuto. The frst step solvg our model s to determe the capacty of multple VMs for each ter terms of the request rate they ca hadle. Gve the capacty of each ter VMs the ext step computes the umber of VMs requred at each ter to satsfy the requremet of customer respose tme. Here let R be the desred ed-to-ed respose tme for a vrtualzed mult-ter applcato. Deote T r () Tr ( 2 )... Tr ( ) as the per-ter ed-to-ed respose tmes such that T () = R these values are obtaed by gve r = aalytcal model. We let λ s be the aggregate request arrval rate to the ter for a customer s such that s [ m] [ ]. Note that a sesso our model correspods to a customer. Assume that requests of forwarded ter wll ot arrval the followg ters such that requests are reduced at the followg ters the probablty of relatve request arrval rate ca be deoted by λs λs( ) = α [ 2 ] such that λ s2 λ s = α λs3 λs2 = α2 λs λs( ) = α. The parameters α α2... α are derved usg ole mea- suremets. Each request brgs wth a certa amout of work for the VM to do. We assume that the per-ter multple VMs have equal processg capablty such that μ = μ2 =... = μc. The sum of the servce rates of per-ter multple VMs c [ ] s μ j j = c c [ ] j = s μ j the servce rate of per-ter sgle VM. The sum of the servce rates of vrtualzed mult-ter applcato s u ( j) where u ( j ) = μ + μ μ. 2 j I our model systems per-ter servce tmes are assumed to be draw from a kow fxed dstrbuto. Assume that the per-ter utlzato of VM for vrtualzed mult-ter applcato s ρ = λs u( j) < 0 < ρ < where ρ correspod to the utlzato of the busest resource (e.g. CPU dsk or etwork) for the ter. Our model ca also express useful system metrcs lke average request arrval rates ad throughputs at multple VMs terms of the dstrbutos of ther per-ter terarrval ad servce tmes. Therefore our model eables us to capture the behavor of varous ters such as HTTP J2EE ad database VMs. Here the behavor of frst ter ca be modeled a M/M/c system usg Lttle s Law [3] whch derves the ed-to-ed average respod tme for the frst ter as follows: = 372

4 c ( λs ) ( ρ+ c cρ) c u ( j)( ρ) T () = p + k p λ c ( ) r 0 k 2 s k = 0 j = where ρ λ u ( j) s () = < ( = s [m]) s the frst ter utlzato of VMs for vrtualzed mult-ter applcato. p 0 s system state probablty that a request leaves ter for the vrtualzed applcato just after completg servce. The models of other ters dvde the comg requests to multple M/M/ models by some rules ad sed them to dfferet VMs respectvely for respose. It s assumed that the requests of customer s arrve at VM j for the ter wth arrval rate λ s j (2 j c ) ad the ed-toed average respod tme for the other ters ca be derved as follows: T r () = = =... = μ λ μ λ μ λ s 2 s2 c s c where these rules mea that the same respose tme ca be esured o matter whch VM s allocated for the ext c request. λs j = λs ( [2] s [m]) s request j = arrval rate for the ter whch s equally dstrbuted to each VM for that ter. μ = μ2 =... = μc = μ s equal capacty of each VM for ter. Observe that our model ca hadle vrtualzed mult-ter applcatos wth a arbtrary umber of ters sce the complex task of modelg a vrtualzed mult-ter applcato s reduced to modelg each ter. Assume that VMs each ter are homogeeous ad load-balaced. Every VAEE has VMs umber c whch s a fucto of the performace metrcs for each ter of that vrtualzed multter applcato ad thus ( ) c = f λ μ... μ s c The global fucto C g s a self-optmzato fucto of each vrtualzed mult-ter applcato. Thus our model of resource optmzato would the be to mmze total weghted VMs of the system whch ca be formulated as follows: { Cg f ( λs μ μ ) } c λ s μ μc λs μ μc m =... ;...;... ;...;... (3) r () 0 s = c μj λs j = (2) s.. t T R (4) [ ] s [ ] > m (5) Gve the request rate servce rate ad ed-to-ed respose tme for a vrtualzed mult-ter applcato our objectve s to determe how may VMs to allocate such that vrtualzed mult-ter applcato ca servce all comg requests wth a gve respose tme R 0s. The output of the model would be to mmze the total umber of VMs for a vrtualzed mult-ter applcato deoted by C g such that meet to hadle a request rate λ s. Note that the frst costrat gve by (4) requres that the average respose tme for each ter caot be greater tha a certa respose tme (R 0s such as 0.5 secod). Respose tme of customer s requremet R 0s s specfed by eterprse IT customer s cotract. The secod costrat descrbes a codto μ j > λs c j = (5) ecessary for average utl- zato the VMs whch ca ot occur the state of fte queue. For satsfy wth the costrats ad the we adjust the capacty of all ters to these values resultg a mmedate crease for effectve capacty. I order to compute the umber of VMs the model requres several put parameters. I practce these parameters ca be estmated through ole motorg vrtualzed applcato. Therefore we aalyze the geeral treds at each VM ode for each ter our cloud evromet such as the Apache Web server wth VMs. The target applcato used our expermets s that aucto system commoly used a bechmark for mult-ter eterprse applcatos. The ma otatos used throughout ths paper are summarzed Table for clarty. TABLE I. SUMMARY OF NOTATIONS Symbol Descrpto Number of vrtualzed applcato ters m Number of customers c Number of VMs for ter ( ) R Ed-to-ed respose tme for a vrtualzed multter applcato (sec) R Respose tme (sec) of customer s requremet for 0s vrtualzed applcato α Relatve probablty of request arrval rate Request arrval rate (req/s) of customer s for ter ( s m ) μ j Servce rate (req/s) of server j for ter ( j c ) λ s Tr () Ed-to-ed respose tme for ter (sec) C g Mmzed umber of VMs for a vrtualzed multter applcato IV. EXPERIMENTAL EVALUATION I ths secto we preset our expermetal results o the effcecy of our autoomc resource provsog techque for optmzg the umber of VMs the cloud evromet. The results show that uder fe-graed resource provsog the provder s acheved reveues ca be maxmzed whle the customer s operatoal cost s reduced as much as possble. The followg expermets are for the valdato of the model. 373

5 A. Expermetal Setup We establsh a prototype system of cloud evromet such that each of the server odes was ru o two Itel Petum GHz processors wth 2GB RAM. Processg capacty of each server s equal cloud data ceter. The o-demad scheduler was ru o a mache wth 4 Iter Xeo 3.00GHz processor wth 3GB RAM. VAEE Host ra the ope-source verso of the Xe to buld the vrtualzato evromet. All maches were coducted o a Lux kerel cluster tercoected by a Ggabt Etheret (GgE) swtch. Each Lux was stalled as a guest OS each doma of Xe. Note that because Xe places devce drvers for physcal devces to a separate guest vrtual mache called doma 0 all comg ad outgog etwork commucato passes through a extra ode ad curs addtoal latecy. Moreover sce ths ode potetally shares the CPU wth the other VMs ths latecy depeds o both the utlzato of the ode ad the umber of messages. Therefore we explctly model ad measure parameters for ths VM motor delay. We preset proflg result o oe ope-source multter applcato servce based o Eterprse Java Beas (EJB) our expermetal study: the RUBS ole aucto bechmark [4] rug o VMs hosted o dfferet servers. RUBS mplemets the core fuctoalty of a aucto ste smlar to ebay cludg 26 teractos that ca be performed from a clet s Web browser. It follows the three-ter applcato. The frot ter was based o the Apache 2.2 Web server. The mddle ter was based o Java servlets that mplemet the applcato logc wth a embedded Tomcat as the servlets cotaer. Fally the database ter was based o MySQL 4.0. To solate performace terferece we restrct the maagemet doma (doma 0) to use oe CPU ad VMs to use the other CPU. Table 2 shows the values for varous parameters our smulato expermets. TABLE II. WORKLOAD CHARACTERISTICS FOR RUBIS Parameter Web Ter App Ter DB Ter T 0.08 sec 0.4 sec 0.32 sec r () μ j 250 req/s 50 req/s 00 req/s α R 0s 0.8 sec Because our tested applcato s CPU-tesve the oly resource type we curretly cosder s CPU capacty ad we assume that all resources are detcal. We do ot show the memory ad dsk I/O proflg results for brevty. The memory ad dsk I/O cosumpto for the vrtualzed applcato s relatvely sgfcat ad they ever become the bottleeck resource our test settgs. B. Effectveess of Mult-Ter Model I the followg expermets we evaluate our dyamc resource provsog techque for vrtualzed mult-ter applaces. We bult a tme-drve optmzer that models the system as a hybrd queue wth two dfferet queueg models ad s fed wth workload traces from vrtualzed applcatos. For the self-optmzato strateges the optmzer s coupled to a optmzato model solver whch s called at the each ter terval to calculate the umber c of resource provsog accordg to the ed-toed respose tme Tr () for ter for the ext terval. Durg each terval per-request respose tme as well as per-ter throughput ad CPU utlzato are collected ad used to compute the mmzed umber of VMs for each ter of vrtualzed applcatos wth dfferet workloads. Moreover our optmzer employs a far admsso mechasm whch accepts a request wth probablty λ s λ s( ). Thus the assumpto of Posso arrvals holds for the accepted requests order to descrbe vrtualzed applcatos. Frst the RUBS applcato s provded for each server wth embedded VMs. Here each ter employs ts ow provsog techque. System parameter values are show Table 2. Our techque s aware of the demads at each ter ad ca take dosycrases such as optmzato model to accout as show Fgure 3 where arrval rates vary from 0 to 2000 requests per secod. The customer s gve respose tme s 0.8 secods for the RUBS applcato ad servce rate s ad 00 requests per secod for Web App ad DB ters respectvely. For App ad DB ters the request probablty s 0.8 ad 0.8 respectvely for the hybrd aalytc model. I Fg.3 the mmzed VM umber for each ter by the respose tme costrat s preseted whch s computed wth our model ad optmal approach. Number of VMs Web ter App ter DB ter Request arrval rate (reqs/sec) Fgure. 3 Valdato results o the umber of VMs at varous request arrval rates Next we repeat ths expermet to predct the throughput of vrtualzed applcato as show Fgure 4. It shows valdato results o the overall system throughput for RUBS the applcato throughput cotues to crease wth the creasg workload. We measure the rate of successfully completed requests at dfferet requests rate. I our expermet a request s couted successfully oly f t resposes wth 0.8 secods for the customer requremet. The request probablty s 0.8 for App ter ad 374

6 for DB ter. The result shows that the system throughput ca be accurately predcted wth our model. Throughput (reqs/sec) Web ter (Model) Web ter (Measuremet) App ter (Model) App ter (Measuremet) DB ter (Model) DB ter (Measuremet) Request arrval rate (reqs/sec) Fgure. 4 Valdato results o system throughput Sce the vrtualzed applcato throughput our model s derved from resource usage at each ter we further exame the accuracy of per-ter resource usage predcto usg the same parameter values Table 2. Fgure 5 presets valdato results o the CPU utlzato at Web App ad DB ters respectvely. They compare the predcted CPU utlzato to the measured CPU utlzato for the three ters wth the workload creasg. Web App ad DB ters were rug o ther ow vrtual mache wth average 53.82% 62.25% ad 62.5% CPU utlzato respectvely whch s close to the optmal soluto 60%. CPU utlzato of doma 0 was average 22% for each ter of vrtualzed applcato. Overall these fgures demostrate that the model s reasoably accurate ad ca effectvely use CPU resource. CPU Utlzato Web ter (Model) Web ter (Measuremet) Doma 0 for Web ter App ter (Model) App ter (Measuremet) Doma 0 for App ter DB ter (Model) DB ter (Measuremet) Doma 0 for DB ter Request arrval rate (reqs/sec) Fgure. 5 Comparso of models vs. expermetal results Therefore the mmzed umber of VMs as well as the maxmzed CPU resource utlzato ca be acheved wth our method by dyamc resource provsog techque ad the we ca keep the hgh global utlty. V. RELATED WORK Prevous lterature o ssues related to maagg resources mult-ter applcatos of data ceters. I ths secto we descrbe some pror work related to ths paper as follows. Some papers have cosdered the provsog of resources at fer graularty of resources. Urgaokar et al. [5] preseted a aalytcal model for mult-ter Iteret applcatos whch s geeral to capture varous characterstcs of a arbtrary umber of heterogeeous ters. The the model was appled to dyamc resource provsog. Ardaga et al. [6] proposed a provsog cotroller for mult-ter data ceter whch maxmze profts usg a cost model ad developed a heurstc soluto. The lmtato s that they dd ot dstgush servers dfferet ters but allocated physcal resources stead of vrtual maches. At the same tme they adopted a closed queueg etwork performace model for the autoomc system. Overall the above approaches are commoly based o the provsog of detcal servers as ut whle our work s dfferet that we adopt full vrtual maches based o a ope queueg etwork model whch supports fe-graed sharg of the physcal frastructure as well as guaratees the performace solato of dfferet vrtualzed applcato evromets by deployg them o separate vrtual maches. Other research efforts have focused o the modelg of mult-ter applcato evromets. Urgaokar et al. [7] proposed a dyamc capacty provsog model for multter Iteret applcatos whch determe how much of the resources to provsog to each ter of the applcato ad a combato of predctve ad reactve methods that determe whe to provso these resources both at large ad small tme scales. Che et al. [8] proposed a closedsystem model of mult-ter busess applcatos ad based o mea value aalyss (MVA) algorthm to predcate performace of mult-ter applcatos. Kamra et al. [9] preseted a sgle queue model for all ters ad based o cotrol-theoretc approach for admsso cotrol multter Web stes that both preveted overload ad eforced absolute clet respose tmes whle stll matag hgh throughput uder load. Jug et al. [20] proposed a geeratg adaptato for mult-ter applcatos vrtualzed cosoldated server evromets. It provdes dyamc maagemet method ad optmzes offle resources to geerate sutable cofguratos by evaluatg a model cosstg of mult-ter M/M/ queues. However the prmary dfferece s that we have establshed sophstcated models dfferet from tradtoal aalytc models whch adopt accurate MVA or sgle queueg model. Here we ca coclude that a hybrd model whch s a M/M/c queueg model combed wth multple M/M/ queueg models ca be adopted ths paper whch ca be acheved more accurate provsog for vrtualzed mult-ter applcatos tha other models. Aother area of related researches has focused o optmzato problems arsg mult-ter applcatos. For example Zhag et al. [2] preseted a olear teger optmzato model for determg the umber of maches at each ter a mult-ter server etwork. Smlar to ours they profle the computg resource of data ceter physcal servers evromet whle we profle the computg resource of cloud evromet vrtual 375

7 maches evromet. As a result our approach ca support a more fe graed resource provsog ad maagemet for a vrtualzed applcato cloud evromet. Addtoally ther approach uses a smply ope queueg etwork model at each server whch s less accurate tha hybrd queueg models we used. Cuha et al. [22] preseted a ew self-adaptve capacty maagemet framework for mult-ter vrtualzed evromets. It executes perodcally ad reassgs resources by evaluatg a model cosstg of mult-ter M/M/ queues ad solves a optmzato problem. Istead our work we cosder that the doma 0 of Xe potetally shares the CPU wth the other VMs ad ths latecy depeds o both the utlzato of the ode ad the umber of messages. Moreover we propose a optmal method for VMs whch ca compute effectve utlzato for CPU of each ter of vrtualzed applcato. Therefore our work has preseted models close to realty. VI. CONCLUSION AND FUTURE WORK I ths paper t s argued that dyamc provsog of vrtualzed mult-ter applcatos rases ew challeges ot addressed by pror work o provsog techque for cloud evromet. We preseted a optmal autoomc vrtual mache provsog archtecture for cloud data ceter. We proposed a ovel dyamc provsog techque whch was a hybrd model for a vrtualzed mult-ter applcato cloud data ceter. A costraed o-lear optmzato model s employed to mmze the total umber of VMs for the requremet of customer. Hece the effcecy ad flexblty for resource provsog were mproved cloud evromet. We evaluated ad cotrasted the performace of three ter vrtualzed applcatos through smulato expermets. Results have show that uder fe-graed resource provsog computg resources are optmzed utlzato. Moreover our techque s also demostrated that by optmzg provsog the overall performace could be further ehaced whle matag average respose tme targets. Our work ca be mproved a umber of ways. Frst we further tegrate load predcto method techque to ft our workload characterstcs. Secod we wll focus o expadg the utlty aalytc model to ft cloud evromets wth heterogeeous servers produced by dfferet maufacturers. Thrd we adopt Servce Level Agreemet (SLA) based egotato of prortzed applcatos to determe the costs ad pealtes by the acheved performace level. If the etre request caot be satsfed some vrtualzed applcatos wll be affected by ther creased executo tme creased watg tme or creased rejecto rate. ACKNOWLEDGMENT Ths work was supported part by the IBM Ph.D. Fellowshp ad the Natoal Natural Scece Foudato of Cha uder Grat REFERENCES [] D. Reed I. Pratt ad P. Meage et al Xeoservers: Accoutable executo of utrusted programs The Seveth Workshop o Hot Topcs Operatg Systems Ro Rco Arzoa 999. [2] M. Armbrust A. Fox ad R. Grffth et al Above the clouds: A Berkeley vew of cloud computg Techcal Report No. UCB/EECS Uversty of Calfora Berkley USA Feb [3] R. Buyya C.S. Yeo ad S. Veugopal et al Cloud computg ad emergg IT platforms: Vso hype ad realty for delverg computg as the 5th utlty Future geerato computer systems Elsever scece Amsterdam the Netherlads (6) pp [4] D. Gupta S. Lee ad M. Vrable et al Dfferece ege: haressg memory redudacy vrtual maches The 8th USENIX Symposum o Operatg Systems Desg ad Implemetato 2008 pp [5] P. Barham B. Dragovc ad K. Fraser et al Xe ad the art of vrtualzato Proceedgs of the 9th ACM Symposum o Operatg Systems Prcples Bolto Ladg NY USA 2003 pp [6] Y. Sog Y. L ad H. Wag et al A servce-oreted prortybased resource schedulg scheme for vrtualzed utlty computg Proceedgs of the 9th IEEE Iteratoal Symposum o Cluster Computg ad the Grd 2009 pp [7] J. Zhag M. Yousf ad R. Carpeter et al Applcato resource demad phase aalyss ad predcto support of dyamc resource provsog Proceedgs of the 4th Iteratoal Coferece o Autoomc Computg [8] X.Y. Wag Z.H. Du ad Y.N. Che et al Vrtualzato based autoomc resource maagemet for mult-ter Web applcatos shared data ceter The Joural of Systems ad Software (9) pp [9] S.R. Whte J.E. Haso ad I. Whalley et al A archtectural approach to autoomc computg Proceedgs of the Iteratoal Coferece o Autoomc Computg [0] Oracle [] D.A. Meascé M.N. Bea Autoomc vrtualzed evromets Proceedgs of IEEE Iteratoal Coferece o Autoomc ad Autoomous Systems 2006 pp [2] R.P. Doyle J.S. Chase ad O.M. Asad et al Model-based resource provsog a web servce utlty Proceedgs of the 4th coferece o USENIX Symposum o Iteret Techologes ad Systems [3] J. McKea A Geeralzato of Lttle's Law to momets of queue legths ad watg tmes closed product form queueg etworks Joural of Appled Probablty pp [4] E. Cecchet J. Marguerte ad W. Zwaeepoel Performace ad scalablty of EJB applcatos Proceedgs of the 7th ACM SIGPLAN coferece o Object-oreted programmg systems laguages ad applcatos 2002 pp [5] B. Urgaokar G. Pacfc ad P. Sheoy et al A aalytcal model for mult-ter Iteret servces ad ts applcatos Proceedgs of the 2005 ACM SIGMETRICS teratoal coferece o Measuremet ad modelg of computer systems 2005 pp [6] D. Ardaga M. Truba ad L. Zhag SLA based proft optmzato mult-ter systems Proceedgs of the 4th IEEE Iteratoal Symposum o Network Computg ad Applcatos 2005 pp [7] B. Urgaokar P. Sheoy ad A. Chadra et al Agle dyamc provsog of mult-ter Iteret applcato ACM Trasactos o Autoomous ad Adaptve Systems () pp [8] Y. Che S. Iyer ad X. Lu et al SLA decomposto: Traslatg servce level objectves to system level thresholds Proceedgs of the 4th Iteratoal Coferece o Autoomc Computg

8 [9] A. Kamra V. Msra ad E. Nahum Yaksha: A self-tug cotroller for maagg the performace of 3-tered web stes Proceedgs of Iteratoal Workshop o Qualty of Servce 2004 pp [20] G. Jug K.R. Josh ad M.A. Hltue et al Geeratg adaptato polces for mult-ter applcatos cosoldated server evromets Proceedgs of the 5th Iteratoal Coferece o Autoomc Computg 2008 pp [2] A. Zhag P. Satos ad D. Beyer et al Optmal server resource allocato usg a ope queueg etwork model of respose tme HP Labs Techcal Report HPL [22] I. Cuha J. Almeda ad V. Almeda et al Self-adaptve capacty maagemet for mult-ter vrtualzed evromets Proceedgs of the 0th Iteratoal Symposum o Itegrated Network Maagemet2007pp