Cos Opimizaion Model for Business Applicaions in Virualized Grid Environmens Jörg Srebel Universiä Karlsruhe (TU), 76131 Karlsruhe, Germany srebel@iism.uni-karlsruhe.de Absrac. The adven of Grid compuing gives enerprises an ever increasing choice of compuing opions, ye research has so far hardly addressed he problem of mixing he differen compuing opions in a cos-minimal fashion. The following paper presens a comprehensive cos model and a mixed ineger opimizaion model which can be used o minimize he IT expendiures of an enerprise and help in decision-making when o ousource cerain business sofware applicaions. A sample scenario is analyzed and promising cos savings are demonsraed. Possible applicaions of he model o fuure research quesions are oulined. 1 Inroducion 1.1 Moivaion Today s corporae IT deparmens are ypically being plagued by a muliude of challenges. Among hose is he increasing complexiy of IT infrasrucure caused by a large number of dedicaed, heerogeneous IT resources. This siuaion enails oal operaions expenses of complex sofware landscapes o rise coninuously. The Grid compuing model, which allows he sharing of compuing resources promises o remedy he complexiy of IT infrasrucure by enabling more efficien infrasrucure uilizaion. Bu his promise has no ye caugh on in he corporae world: while Grid-based soluions enjoy grea populariy in he scienific communiy (e. g. Grid-based daa processing for he LHC a CERN), hey hihero have found lile accepance wih corporae users and are hus hardly found in he business IT landscape - if i all (c.f. [1, p. 4]). In he Business in he Grid (BIG) projec [1], he auhors conduced numerous inerviews wih relevan findings. All companies see Grid compuing as a way o reduce coss in cerain areas... [1, p. 5], bu a good cos model is sill missing, so ha companies currenly canno quanify he poenial, Grid-relaed savings. In our opinion, a weakness of Grids for business exis a he momen, because Grids are no profiable [1, p. 8]. This observaion raises doubs wheher he promises associaed wih Grid compuing really exis. A solid analysis of he business cases of Grid soluions is herefore required. In his research paper, a cos-opimizaion model is presened, ha will be helpful in answering he quesion, wha he cos-saving poenial of Grid compuing is and how a company can realize his poenial. The paper was moivaed by he research quesions arising from he curren
Biz2Grid research projec 1. Is goal is o clarify under which condiions business applicaions can be moved o he Grid; he BMW Group acs as an indusry parner o he projec. 1.2 Relaed Work The research quesion menioned above has already araced a fair body of research; especially research from he following hree perspecives has insighs o offer: research on Grid compuing cos, research on decision suppor ools and research on resource managemen. As a researcher in Grid compuing cos, Opiz e al. [2] ries o quanify he oal cos of ownership (TCO) for grid compuing resource providers in absolue cos figures from real-world scenarios and comes up wih an esimae for he oal cos of a CPU-h offered by a commercial resource provider; he model in Opiz e al. does no include any sorage coss, which are of major imporance in business seings. So, he necessary cos calculaions for compuing resource-consuming enerprises have o look differen from he model in Opiz e al. for resource providers. Risch e al. [3] analyzed a number of Grid compuing scenarios using a cos-based approach; hey showed ha Grid compuing is beneficial in scenarios, where eiher shor and infrequen peaks have o be covered or where daa backups have o be conduced or where lighly used resources have o be replaced. However, hey recommend ha each company performs is own cos analysis as he benefis are depending on he cos level of he in-house resources. Gray [4] specifically deals wih he decision when o ousource given he price raios beween he differen compuing resources. Generally, he business model behind Grid compuing remains case-specific; he mainains ha business benefis are only realized for very CPU-inensive sofware applicaions. Gray s model fails o include he ofen cosly commercial sofware licenses required by corporae users and compleely omis cos facors like ransacion coss. I has long been known from research on IT ousourcing, ha hose facors play an imporan role in he ousourcing decision (e.g. Smih e al. [5]). As a conclusion, i can be saed ha he cos models for Grid compuing found in he curren research lieraure are raher incomplee, scenario-specific and no focused on he corporae decision-maker. The decision-suppor perspecive is he second imporan perspecive on he poenial of Grid compuing. Kenyon and Cheliois [6] addressed he area of Grid resource commercializaion, which is frequenly called uiliy compuing. They conceive Grid resources as commodiies and apply financial insrumens for convenional commodiies like gas or elecriciy o hose resources. Wihin he scope of heir analysis, hey idenified he necessiy for decision suppor, when Grid users buy or sell Grid resources on a Grid markeplace. However, he need for such elaboraed decision suppor models will only arise if a working Grid resource marke similar o he exising markes of convenional commodiies should ever exis, which is currenly - despie research iniiaives such as SORMA [7] and GridEcon [8], no he case. Even wihou he exisence of Grid markeplaces, 1 hp://www.biz2grid.de
Grid resource consumers sill need decision suppor oday when planning heir Grid usage. The research lieraure has only limied insighs o offer for oday s scenarios. In he area of resource managemen, Rolia e al. (e.g. [9]) sugges a resourcemanagemen framework for auomaic sofware applicaion placemen in he daa cener using Grid-compuing principles like resource allocaion and scheduling. Their main focus lies on he opimizaion of in-house daa-cener resources, hey do no address he quesion under which condiions o use exernal resources. Their opimizaion approach minimizes he number of CPUs and does no consider acual cos facors from an enerprise IT environmen. Bichler e al. [10], Wimmer e al. [11] and Almeida e al.[12] pursue he same goal and suffer from he same drawbacks. Bagchi [13] uses simulaion o analyze financial merics (e.g. ROI) for Enerprise Grid sysems, bu does no feaure opimizaion. The decision-suppor problem may be found in he Ousourcing research lieraure as well (Grid compuing can be seen as on-demand ousourcing of IT infrasrucure). However, he lieraure review of Gonzalez e al.[14] and he review of Dibbern e al.[15] show ha he quesion of wha o ousource has mosly been analyzed concepually or in a posiivis fashion so far, bu no hrough mahemaical modeling, even hough cos is universally recognized as an imporan reason for Ousourcing. The use of linear programming in ousourcing decisions was suggesed by Knolmayer [16], who also presened a model for deciding which IT service asks o ousource. However, he never acually implemened or evaluaed his model in real life, so his work remains raher concepual. I seems he curren research lieraure on Grid compuing does offer some help when opimizing he enerprise daa cener, bu i fails o help wih he quesion of when o uilize exernal resource pools in an economically beneficial way. This paper suggess a novel model for opimizing he overall cos of inernal and exernal IT resource usage for enerprises. In he following secion, he opimizaion model and he cos model are developed and heir underlying assumpions are saed. In Sec. 3, he model is insaniaed and solved for a ypical enerprise scenario; he soluion is hen discussed and fuure research direcions are given in he las secion. 2 Mehods The following chaper will describe a novel model for opimizing inernal and exernal resource usage. Firs, he assumed IT archiecure on which he cos model is based, will be defined; second, he acual opimizaion model ha operaes on his IT archiecure will be explained. 2.1 Enerprise IT Archiecure Srong [17] gives examples of how a ypical enerprise IT archiecure looks like. Fig. 1 is based on his archiecure; as an exension, a second resource pool (e.g. a Grid service provider) and he Grid middleware (for resource managemen)
are added. The necessary economic merics needed for he cos evaluaion are also displayed. A uiliy compuing model is assumed for all exernal resources. A number of differen business sofware applicaions can poenially be run on boh inernal and exernal resources. A ypical business applicaion consiss a leas of he applicaion iself (feauring he business logic), a daabase sysem for persisen daa sorage and he Grid middleware. The following definiions correspond o he eniies in Fig. 1; hey are used hroughou he res of he paper. Le T be he ime inerval under analysis in he opimizaion model. Le T be divided up in s T = {1, 2,... l}( T ) of equal lengh. Le J be a se of sofware applicaions (J = {App 1, App 2,... App N }, j J); each applicaion has processor requiremens p j R + 0 measured in number of CPU cores, inbound neworking requiremens n I j R+ 0 measured in GB (Gigabyes), oubound neworking requiremens n O j R+ 0 and sorage requiremens so j R + 0 measured in GB. Each applicaion shall run in is own virualized environmen on a separae sofware sack (OS, daabase). The virualizaion overhead is facored ino he load daa p j. The CPU, neworking and sorage requiremens can be esimaed from hisorical real-world sysem races coming from performance monioring sysems (c.f. Rolia e al.[9]). Le I be a se of inernal servers (I = {InernalServer 1,... InernalServer M }, i I) wih each inernal server having a capaciy of s i R + 0 measured in number of CPU cores. Le E be a se of exernal servers (E = {ExernalServer 1,... ExernalServer R }, e E) wih each exernal server having a capaciy of s e R + 0 measured in number of CPU cores. There is only one class of service for all servers; his class of service shall correspond o he resource provider s class of service. Fig. 1. Enerprise Grid Archiecure Inernal Pool Inernal Server Inernal Server Sorage LAN App. 1 DB OS Virual Machine App. 2 DB OS Virual Machine Grid Middleware App. 3 DB OS Virual Machine Inernal cos: -HW lease -Licenses -Sorage - WAN Exernal Pool Exernal Server Inerne Grid Sorage App. x DB OS (Grid) Uiliy model: - /CPU-h - /GB-Monh -
2.2 Opimizaion Model The ask of he opimizaion model consiss of assigning each sofware applicaion in each o a resource from eiher he inernal or he exernal pool. The model is essenially a mixed-ineger programming problem (MIP) where he cos funcion is composed of cos facors ypically found in business sofware applicaions. The following lis shows he decision variables of he opimizaion model. binary variable x ij for assigning applicaion j o inernal server i in binary variable x ej for assigning applicaion j o exernal servers e in binary variable z i for recording he use of inernal server i in T binary variable y e for recording he use of exernal server e in binary variable w j for one-ime migraion se-up aciviies of applicaion j binary variable o for he one-ime overhead of using he exernal resource pool binary variable a for he ic overhead of using he exernal resource pool in ineger variable v for he required number of sofware licenses raional variable u for he required size of he inernal sorage infrasrucure z i records if he inernal server i was used a all in T (e.g. z 1 = 1 means ha InernalServer 1 was used a leas in one ). This informaion is required o accuraely calculae he hardware coss. y e records if he exernal server e was used in ; if ha is he case, hen he exernal compue fees for ha server in ha are facored in he oal cos. w j is a binary decision variable which is se o 1 if applicaion j is moved from he inernal resource pool o he exernal resource pool. This migraion requires a number of se-up aciviies (inerface implemenaion, nework configuraion, possible reconfiguraions of exising sysems ec.) whose one-ime cos c impl j (see Table 1) will be added o he oal cos if he migraion akes place. Those se-up aciviies only occur once in he lifeime of an applicaion; subsequen migraions do no resul in addiional expenses. The decision variable o is se o 1 if any resources from he exernal pool are used a all. Then he overhead cos facor c o comes ino play. This cos facor represens he one-ime ransacion coss incurred when choosing an exernal provider and when seing up he conracual relaionship. The effor required for vendor informaion rerieval, vendor selecion, conrac negoiaion ec. deermines he level of he one-ime ransacion coss. The decision variable a is se if any resources from he exernal resource pool are used in. Then he overhead facor c a is added o he oal cos. c a models ic ransacion coss resuling from aciviies like vendor managemen, service level monioring or conrac changes. Decision variable v comprises he maximum number of sofware licenses required;
each applicaion requires one daabase license and one Grid middleware license per core used. Licenses have o be purchased no maer if an applicaion resides on inernal or exernal resources. For simpliciy s sake, i is assumed ha he differen sofware applicaions only use one ype of daabase sysem. Table 1 describes he cos facors used hroughou he model. The cos facor represens expendiures for inernal server hardware. The regular paymens per for hardware could eiher be he server ren or server depreciaion coss. The server expendiures do no depend on he acual server uilizaion; if he server is used wihin T, is expendiures have o be paid for he complee ime inerval T. Poenial hardware replacemens and oher repairs are included in he hardware cos. (The hardware cos also includes cos facors like elecrical power, server operaions and daa cener faciliies.) The cos model for sofware licenses is simple: c v is he price for one daabase license and one Grid middleware license (licenses are priced per core). The sofware license mainenance fee c hw i is a fixed amoun per per license (usually a percenage of he iniial license cos). I is assumed ha he exernal infrasrucure provider only provides OS (operaing sysem) licenses for he exernal servers; he res of he sofware licenses has o be purchased by he Grid user. LAN coss are negleced as LAN ranspors ypically cos 10000 imes less han WAN ranspors[4]. Moreover, hey are no helpful in disinguishing he cos beween inernal and exernal resources, as all daa has o pass hrough he LAN, no maer if i is desined for inernal or exernal resources. WAN connecions are assumed o be sized large enough so ha he inernal deploymen of all applicaions does no creae any bandwidh bolenecks. The cos model srucure and he cos facors were found in Sekazek s work on he merics of corporae SAP sysems [18, p. 135]. Table 2 combines he cos facors and he decision variables of he model and shows he cos funcion componens. The complee cos funcion is he sum of all cos funcion componens lised in Table 2. The cos funcion is subjec o he following consrains. Equaion (1) mandaes ha one applicaion is assigned o exacly one server per. I is neiher possible o run one applicaion on several servers nor o run several applicaion insances during he same. Inequaliy 2 ensures ha he load per placed on each exernal server is a mos he maximum capaciy per of ha server. Inequaliy 3 applies o inernal servers analog o (2). Inequaliy (4) makes sure ha z i is se as soon as server i is used a leas once in T. Inequaliy (5) ses he overhead decision variable whenever here is a leas one exernal server. Inequaliy (6) ses he decision variable for ousourcing se-up aciviies, whenever applicaion j uses an exernal server a leas once. Inequaliy (7) ses he ic ransacion cos decision variable if exernal servers are used. Inequaliy (8) makes sure ha he minimal necessary amoun of sofware licenses are purchased; (9) models he inernal sorage requiremens and ses u o he minimal amoun of inernal sorage required across all s. Consrain 10 limis he decision variables o binary values. c vm R M x ej + x ij = 1 j J, T (1) e=1 i=1
Time focus One-ime expenses Running expenses Table 1. Cos facors Cos ype Cos facor Varian Uni coss Cos Coeff. Implemenaion Inerfaces, Nework configuraion se-up App core Sofware Licenses (Daabase,Middleware) Overhead Transacion cos c o Infras- Inernal rucure Sofware Daa ransfer over- Periodic head Inernal HW cos (Server, Racks) Inernal Sorage cos WAN usage Sofware mainenance ransfer from he exernal pool o he enerprise ransfer from he enerprise o he exernal pool infras- Exernal Compue fees Exernal rucure Exernal sorage fees Transacion cos, Suppor cos 4core 8core 16core 24core 1core 4core 8core per size per I/O requess GB GB license GB GB GB reques c impl j c v c hw i c u c wan cvm c dou c din c f e c esors cesorr c a
Table 2. Cos funcion componens Cos facor Cos coeffic. Cos componen Inerfaces, Nework configuraion se-up c impl N j j=1 cimpl j w j Sofware license purchases c v c v v One-ime Overhead c o c o o HW cos (Server, Racks) c hw M l i i=1 zi =1 chw i l Inernal Sorage =1 cu u WAN usage c wan c wan l Sofware mainenance c u c vm l =1 cvm =1 N j=1 (ni j + n O j) R e=1 xej Daa ransfer o enerprise c dou c dou l N R =1 j=1 no j e=1 xej Daa ransfer o he exernal c din c din l N R =1 j=1 ni j e=1 xej pool Exernal Compue fees Exernal sorage fees (size) Exernal sorage fees (requess) c f e c esors c esorr Periodic overhead c a c a l =1 a v l R =1 e=1 cf ey e l =1 cesors l N R =1 cesorr j=1 e=1 xej N j=1 soj R e=1 xej N p j x ej s e y e 0 e E, T (2) j=1 N p j x ij s i 0 i I, T (3) j=1 N M v + p j ( x ij + j=1 i=1 e=1 i=1 j=1 x ij z i 0 T, i I, j J (4) x ej o 0 e E, T, j J (5) x ej w j 0 e E, T, j J (6) y e a 0 e E, T (7) R x ej ) 0 T (8) M N u + so j x ij 0 T (9) x ej, x ij, y e, z i, o, w j, a {0.1} e E, T, j J, i I (10) v N + 0 (11) u R + 0 (12)
3 Resuls The following secion shows an exemplary insaniaion of he opimizaion model; firs, sample cos figures from he lieraure are presened; hen he opimizaion model is applied o a sample scenario using hese cos figures. 3.1 Cos figures All cos calculaions are based on acual cash-flows; no depreciaion rules are used, as he cos accouning of Grid compuing expenses is no in he focus of his research paper. As a general rule, 200 working days per year are assumed for a full-ime employee; an employer has o calculae wih around 100000$ (ca. 75000 ) for salaries and indirec labor coss per year for a full-ime employee[19]. The cos for seing up he inerface beween he enerprise and he exernal resource pool for applicaion j c impl j depends on he inerface implemenaion effor (measured in working days). I is assumed ha an inerface of medium complexiy requires 5 FTE. This esimaion is based on sofware indusry bespracices for EAI inerfaces. The one-ime ransacion cos c o incurred when choosing an exernal resource provider is exremely hard o esimae. In his insance, he effor required by he vendor selecion process is used as a proxy for he ransacion cos. In he indusry, an RFP (reques-for-proposal) is launched whenever here is ousourcing work o be done and whenever here are several poenial vendors. In his case, an aciviy-based cosing approach is chosen o assess he asks involved and esimae he complexiy in FTE (Full-Time Equivalens). The oal effor in FTE required by he vendor selecion process can be capured in he following equaion: c o = 3 + 0.6 vendors + 0.125 vendors evaluaors (13) The daa for (13) has been colleced hrough an inerview wih a BMW IT projec manager wih several years of professional experience. For he cos model in Table 3, i is assumed ha here are 3 poenial vendors and 3 employees acing as evaluaors in he enerprise, which resuls in an effor of ca. 6 FTE (= 2250 ). The ic ransacion cos level c a is similarly hard o esimae as he one-ime ransacion cos level. The enerprise requires personnel o conrol he ousourcing provider. The coordinaion effor is mainly depending on he number of differen conracs and no so much on he size of each conrac. The lieraure saes ha a single employee can handle conrac sizes of 5-10 Mil. per year[20, p. 135]. The conrac sizes for ad-hoc uiliy compuing should range considerably below his level; i is assumed ha a 0.1 FTE per year (= 7500 ) can handle he vendor managemen required by he usage of exernal resources. The hardware coss c hw i have been esimaed wih an IBM Sysem x3850 M2 server in mind using he IBM online configuraor 2. Cos facors like 2 hp://www-03.ibm.com/sysems/de/x/hardware/enerprise/x3850m2/index. hml
server operaions, daa cener faciliies and operaing sysem licenses add ca. 350 o he acual price of he hardware (based on McKinsey s daa[19]). Table 3 summarizes realisic cos figures for he opimizaion model (he cos figures have no relaionship o BMW IT cos figures). 3.2 Opimizaion resuls In he scenario under sudy, wo sofware applicaions are analyzed over he course of 6 monhs (12 s), wih each lasing 2 weeks. The firs applicaion is a business sofware which requires 2 CPU cores in he firs half of he monh and 6 CPU cores in he second half of he monh for billing runs and oher bach processing. This applicaion needs 600GB of sorage in each ; i receives 0.1 GB and sends 1 GB of daa over he nework in he firs wo weeks of he monh and receives 0.5 GB and sends 5 GB of daa in he second wo weeks of he monh. The second applicaion is a simulaion sofware and consanly requires 2 CPU cores and has only minimal sorage and nework requiremens. The inernal resource pool feaures 4 servers wih 4, 4, 8 and 16 cores; he exernal resource pool feaures 3 servers wih 1, 4 and 8 cores. Three resource managemen policies are esed: Policy 1 pus each applicaion on a separae inernal server and uses a maximum sizing approach for hardware and sofware (i.e. he firs applicaion would run on an 8 core server using 8 sofware licenses, he second applicaion would run on a 4 core server using 4 sofware licenses). This approach is commonly used oday in he indusry. Policy 2 ries o consolidae he applicaions on inernal and exernal servers of opimal size. Policy 3 ries o consolidae he applicaions on exernal resources of opimal size. Table 4 shows he resuls of scenario 1; he savings are calculaed in comparison o policy 1. 4 Discussion The resuls in Table 4 leads o he following conclusions: Policy 2 leads o consolidaion of he wo applicaions on one inernal server, which is he cos-opimal soluion for his scenario. Even hough exernal resources were available, he opimizaion model picked he more cos-efficien inernal servers. Hence, an Enerprise Grid, where he Grid middleware opimizes he managemen of inernal resources, would be he bes archiecural decision in his scenario. Furher research mus show if a longer ime inerval under sudy (>6 monhs) migh lead o a differen resul, as he overhead coss of using exernal resources would hen be disribued over more s. A forced usage of exernal resources is slighly more cosly (ca. 9%) han pure inernal operaions in his scenario.
Table 3. Cos figures Cos coeffic. Varian Value Commen c impl j ca. 1875 inerface se-up c v ca. 6600 Oracle license a c o ca. 2250 see (13) c hw i 4core ca. 522 monh based on [19], IBM b 8core ca. 553 monh 8Gb RAM 16core ca. 604 monh 16Gb RAM 24core ca. 833 monh 24Gb RAM c u ca. 1.60 c wan c vm x86 ca. 123 c din c dou GB monh based on [21] ca. 0.28 GB based on [22] ca. 0.07 GB ca. 0.13 GB c f e 1core ca. 60 monh c esors 4core 8core core monh ca. 240 monh ca. 480 monh ca. 0.42 c esor c a ca. 21 monh GB monh ca. 926 monh Oracle Sofware Updae License and Suppor, Sun Grid engine subscripion (4core CPU) c based on Amazon Web Services (AWS) d based on Amazon Web Services based on AWS on-demand Linux insances based on AWS on-demand Linux insances based on AWS on-demand Linux insances Amazon EBS and S3 snapsho Amazon EBS, 100 I/O reques per second vendor and conrac managemen, AWS suppor a hp://www.oracle.com/corporae/pricing/echnology-price-lis.pdf b hp://www-03.ibm.com/financing/de/ifinancing/ools/ezrae/de500.hml c hp://globalspecials.sun.com/drhm/servle/conrollerservle?acion= DisplayProducDeailsPage&SieID=sunsor&Locale=en_US&producID= 107684700 d Pricing for a European company hp://aws.amazon.com/ec2/#pricing Table 4. Resuls of he scenario Policy Toal cos Savings separae scheduling on wo inernal machines 100275 n.a. combined scheduling on inernal and exernal resources 67791 32% combined scheduling on exernal resources 74202 26%
Consolidaion leads o a much improved server uilizaion and o a much improved sofware license uilizaion. A major par of he savings (81%) in policy 1 comes from he lower number of required sofware licenses (12 for policy 1 compared o 8 for policy 2). As a nex sep, he model will be evaluaed using load races (processor loads, nework hroughpu) from a number of BMW SAP sysems from differen funcional areas (producion, finance, engineering human resources) over he of 6 monhs (he corresponding load daa is being colleced a he momen). From a scienific perspecive, his novel mehod enables he reamen of an array of relevan research quesions. Using exernal resources o offload peak resource requiremens is ofen cied as one of he mos promising applicaions of Grid compuing. The opimizaion model will be helpful in analyzing wha resource demand paerns p j, so j, n I j, no j can economically be shifed o he Grid. Anoher racable research quesion goes ino he direcion of analyzing he effecs of dynamic resource pricing. If he prices of exernal resources are no longer saic over ime, as hey are now, hen how will dynamic resource pricing affec he ousourcing business case? A sensiiviy analysis of cos facors like or c f e in he model will help answer his quesion. (Please noice ha hose cos facors are ime-dependen; herefore, he model can already accommodae changing exernal resource prices.) c esors,c esor 4.1 Limiaions on he Research Design and Maerial MIP models like he one suggesed in his paper are NP-complee and solving imes usually grow exponenially wih he model size; cerain large scenarios canno be solved o opimaliy. This limiaion however is a minor one: firs, he MIP solver will give an esimae of how close he curren soluion is o he opimal one, so he qualiy of he non-opimal soluion can be assessed; second, even non-opimal model soluions can give valuable insighs for he research quesions menioned above. The ransacion cos model needs beer scienific suppor, i.e. a beer undersanding of he processes involved when searching for an ousourcing parner is required. Aciviy-based cosing approaches and ousourcing process analyses can be helpful here. If he exisence of Grid compuing markes is assumed, anoher way o esimae ransacion coss would be o use ransacion cos figures from exising commodiy markes such as elecriciy. The ineracions beween wo applicaions are no modeled: if wo applicaions boh running on exernal resources are exchanging daa, he daa exchange cos facor will be differen from he WAN cos facor. I is hard o deermine wha par of he nework raffic goes o oher applicaions running on exernal resources and wha par goes o he enerprise. So he nework coss migh be slighly exaggeraed wih he curren model. The curren model does no include qualiy measures for compuing resources. Grading compue resources according o benchmarks like he SAP Applicaion
Performance Sandard (SAPS) or SPECin will give a more accurae price vs. performance picure. The cos model does no include any cos facors for eiher applicaion licenses, applicaion mainenance or applicaion operaions. I is assumed ha hose cos facors are comparable no maer where he applicaion runs. Sofware-as-a- Service scenarios are no in he focus of his paper, however hey migh be a fuure model exension. 4.2 Conclusion This paper suggess a novel model of opimizing he cos of IT resource usage for enerprises. The resuling model is helpful for boh opimizing he inernal and he exernal deploymen of an applicaion; i can be se up using daa ha is readily available in he enerprise (sysem races, inernal cos figures); i can be solved using sandard PC hardware and herefore faciliaes he exploraion of research quesions relevan o enerprises pondering he use of Grid compuing. A sample scenario demonsraes he usefulness of he model. However, as he scenario shows, using exernal resources is no beneficial for every siuaion; a careful analysis of a larger number of business scenarios has o be conduced using he opimizaion model o reveal where he promises of Grid compuing hold rue. References 1. Schikua, E., Donno, F., Sockinger, H., Vinek, E., Wanek, H., Weishäupl, T., Wizany, C.: Business in he grid: Projec resuls. hp://www.pri.univie.ac.a/ Publicaions/2005/Schikua_ausriangrid_bigresuls.pdf [Accessed on Oc. 05 2008] (2005) 2. Opiz, A., König, H., Szamlewska, S.: Wha does grid compuing cos? Journal of Grid Compuing (Jan 2008) n.a. 3. Risch, M., Almann, J.: Cos analysis of curren grids and is implicaions for fuure grid markes. In Almann, J., Neumann, D., Fahringer, T., eds.: Grid Economics and Business Models 5h Inernaional Workshop, GECON 2008. Volume 5206 of LNCS., Berlin Heidelberg, Springer-Verlag (2008) 13 27 4. Gray, J.: Disribued compuing economics. hp://research.microsof.com/ research/pubs/view.aspx?r_id=655[accessed on Oc. 05 2008] (Mar 2003) 5. Smih, A.D., Rupp, W.T.: Applicaion service providers: an applicaion of he ransacion cos model. Informaion Managemen and Compuer Securiy 11 (2003) 11 18 6. Kenyon, C., Cheliois, G.: Grid resource commercializaion: Economic engineering and delivery scenarios. In Nabrzyski, J., Schopf, J.M., Weglarz, J., eds.: Grid Resource Managemen: Sae of he Ar and Fuure Trends. Inernaional Series in Operaions Research & Managemen Science. 1s ediion edn. Kluwer Academic Publishers (2004) 465 478 7. Neumann, D., Sößer, J., Anandasivam, A., Borissov, N.: SORMA Building an Open Grid Marke for Grid Resource Allocaion. In Vei, D.J., Almann, J., eds.:
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