GRID COMPUTING IN EXTREME SITUATIONS: REDUCING COSTS AND CREATING RESILIENCE FOR IT INFRASTRUCTURES

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1 GRID COMPUTING IN EXTREME SITUATIONS: REDUCING COSTS AND CREATING RESILIENCE FOR IT INFRASTRUCTURES Olver Hnz, Char of Busness Admnstraton, esp. Electronc Commerce, J.W. Goethe Unversty Frankfurt, Mertonstrasse 7, Frankfurt, Mchael Schwnd, Char of Busness Informaton Systems and Operatons Research, Techncal Unversty Kaserslautern, Erwn Schrödnger Strasse, Geb. 42, Kaserslautern, Roman Beck, Char of Busness Admnstraton, esp. Informaton Systems, J.W. Goethe Unversty Frankfurt, Mertonstrasse 7, Frankfurt, Abstract Recent turbulences n fnancal markets are not only a challenge for the actors n the front offces of the related nsttutons, but also represent a serous challenge for the IT departments n the back offces of banks etc. We present a smulaton model that shows how Grd computng ncreases the reslence and qualty-of-servce of IT nfrastructure n departmentalzed enterprses n the presence of shocks. Grd computng also reduces the costs dervng from the cancellaton of jobs n tmes wth a hgh volatlty of computatonal load. The model can be used to fnd the approprate type of IT nfrastructure for dfferent fnancal servce nsttutons. Our smulatons fndngs are also lkely to encourage the ntroducton of Grd computng for related busness branches and applcatons. Keywords: Grd computng, reslence, IT nfrastructure, exogenous shocks, qualty-of-servce

2 INTRODUCTION Increasng competton on global markets leads to hgh pressure on enterprses and consequently requres further restructurng and automaton of IT-related busness processes such as n the fnancal servces ndustry. In addton to the stffenng competton, banks have to cope wth new legal regulatons such as Basel II and customer needs that are changng n the drecton of hghly customzed on-demand fnancal products. Fnally, the closely woven and nterconnected nternatonal fnancal markets react extremely senstvely to any relevant nformaton such as natonal fnancal turbulences, market crashes or bubble bursts that lead to hghly volatle markets and hgh tradng volumes that are dffcult to forecast. Such extreme events n fnancal markets not only cause turbulences for the related actors n the market nsttutons but also have a sgnfcant mpact on the crtcal IT nfra-structure of these nsttutons. If the tradng volume of the markets rses to a tenfold (or even more) of the regular level n extreme market stuatons, t s very lkely that IT nfrastructure s not capable of dealng wth ths load. A falure or slow down of computng servces can cause serous damage to nsttutons n the fnance ndustry especally n crtcal stuatons, e.g. f trades, transactons, and settlements of fnancal nstruments and products cannot be guaranteed just n tme. However, mantanng an nfrastructure that can deal wth such rare events s also very expensve for the fnancal nsttutons. Our scenaro deals wth an enterprse of the fnancal ndustry (e.g. a bank) wth a separate computng department for each organzatonal unt. In ths paper we show for the case of such a departmentalzed bank that the ntroducton of Grd computng can sgnfcantly ncrease IT archtecture reslence and thereby qualty-of-servce (QoS) wth lower costs for the cancellaton of requested computng jobs even n extreme stuatons, e.g., those caused by fnancal crashes etc. In order to understand the vulnerablty to exogenous shocks of a wdespread IT organzaton structure better, we desgned a smulaton system that s able to model a very common organzaton scheme of IT nfrastructure n bg European fnancal nsttutons. By feedng load profles that are typcal for extreme stuatons nto the departmentalzed IT nfrastructure, we compare a Grd and a Non-Grd soluton wth respect to ther reslence and qualty-of-servce (QoS). Ths s only the frst step n our research process; tests for real world load-profles wll follow to substantate our smulaton fndngs. The remander of the paper s structured as follows: Secton 2 provdes a categorzaton of Grd computng n the lght of management mplcatons, followed by a detaled dscusson of the mportance of reslence for ndustres whch depend on the avalablty of up-to-date nformaton n secton 3. Secton 4 wll ntroduce the developed and appled smulaton model for Grd computng n extreme cases, whle we wll dscuss the fndngs and conclude the paper n secton 5. 2 MANAGEMENT PERSPECTIVES OF GRID COMPUTING To our knowledge the management perspectves of Grd computng are not wdely dscussed n recent lterature yet. Especally for aspects of system reslence, there s a research gap. For ths reason, we try to classfy the few exstng papers usng a classcal scheme of manageral lterature. We dentfy three major management perspectves for Grd computng technology followng the classcal dfferentaton scheme wth respect to the short, mddle, and long term plannng horzon of an enterprse (Ansoff, 965):

3 2. Strategc management perspectve of Grd computng Globalzaton of computng: The concept of computng around the world s ganng ncreasngly n mportance. The expanson of the enterprses core busness nto new geographcal regons (e.g. Chna) plays an mportant role n the busness strategy of many nternatonal companes. and Global Grd computng can reduce nvestments nto IT nfrastructure n the case of such an expanson strategy, because IT resources located n other countres can be co-used (2002). Ths strategy s often called follow the sun because the tme zones n the dfferent geographcal regons lead to resource load profles that follow the rhythm of day and nght. Sustanable resource management: Ecologcal factors are ganng ncreasngly n mportance for the IT ndustry and companes wth bg IT departments. Green computng ams at the effectve use of computng resources to preserve natural resources. The trend has attracted the nterest of governmental nsttutons as well as ndustral companes (GreenerComputng, 2007; GreenGrd, 2007). Several experments have shown that a sutable organzaton of Grd computng nfrastructure can lead to a sgnfcant reducton n power consumpton (Patel et al., 2002). Reslence of computng: Another strategc vew of Grd computng ncludes reacton to exogenous shocks. The extreme ncrease n IT resource load due to an unexpected event lke a crash n the fnancal markets that leads to a huge demand temporary for computatonal power s an example of such an exogenous shock (Huang & Bhatt, 2004). Most recent lterature ncludes some aspects of network reslence (Castan & Squyres, 2007) and robust allocaton mechansms for computatonal tasks on Grd systems (Choon Lee & Zomaya, 2007) Reslence to exogenous shocks n an entre Grd nfrastructure has not been dscussed n much detal yet. 2.2 Tactcal management perspectves of Grd computng Cost reducton: A major target that s expected to be achevable by ntroducng Grd computng technology n the IT servce nfrastructure of companes whch have an ncreased demand for computatonal power s the sgnfcant reducton of resource costs (Skllcorn, 2002). The reorgansaton of IT systems by ntroducng Grd technology n order to mprove the usage of the exstng resources can be consdered as a tactcal varant of cost reducton. However, exact fgures about the true dmenson of possble cost reducton are not known n the lterature yet (Chelots et al., 2004). 2.3 Operatve management perspectves of Grd computng: Falure rsk reducton and qualty of servce: The applcaton of parallel processng n dstrbuted computer systems, as s the case n Grd system archtectures, makes t possble to reduce the rsk of falure of the entre system and helps to ncrease the relablty and robustness of servce provson (Baker et al., 2002; Czajkowsk et al., 200). Consequently Grd systems are usually able to offer a hgher level of QoS for IT servce provsonng processes whle employng fewer resource capactes as a result of the ncreased system relablty and robustness (Schwnd et al., 2007). Ths rse n QoS s manly of nterest from the operatve pont of vew. However, as for the case of IT cost reducton by the applcaton of Grd systems, exact fgures about the achevable level of mprovement are not yet known for ndustral real world applcatons.

4 Schedulng and load balancng: Another advantage of Grd systems from the operatve pont of vew s ther ablty to schedule ncomng computng tasks on the pooled IT resources and to acheve a sutable load balancng. Economcally-orented Grd schedulng systems n partcular have turned out to be a promsng approach for load balancng n dstrbuted computer systems (Eymann et al., 2003; Schwnd et al., 2006). Whle regardng the postve mpacts of Grd computng, one should not forget about some economc downsde rsk of Grd computng. Though there s a legtmate opportunty of cost reducton, there can also be addtonal hdden costs of Grd computng. These costs mght result from deployment, update, and verson management effort, resultng from the dstrbuted hardware nfrastructure of a Grd system (Afgan & Bangalore, 2007). Addtonally, suffcent network capacty s requred for a relable workng Grd nfrastructure (Huang & Bhatt, 2004). 3 RESILIENCE IN GRID COMPUTING Grd archtectures can be mplemented not only to optmze exstng busness processes by faster calculaton or avalablty of data but also to mprove the reslence of the enterprse s IT nfrastructure to hardware falure or unforeseen peak loads. As mentoned already n the prevous secton, computng reslence s an mportant factor from a strategc IT management perspectve. For nstance, a German headquarters can use the offce nfrastructure of ther subsdary company n the US to augment processng power and to run applcatons durng the Amercan off-peak hours. There s sgnfcant potental for peak demand clppng wthn organzatons that straddle multple tme zones (Skllcorn, 2002). Ths flexblty and scalablty of capacty allows t to provde computng capacty to meet average demand, takng advantage of vrtualzed resources, to meet unexpected surges of resource requrements, and mprove the utlzaton of exstng IT assets. In addton, departmentalzed enterprses can take full advantage of ths to use underutlzed computng resources to serve as backup and recovery systems for mproved operatonal reslence and reduced nfrastructure nvestment requrements. Accordng to Xe et al. (2005), reslence s the ablty of IT nfrastructure to guarantee a certan level of servce n the presence of sudden mponderabltes such as natural dsasters, falures due to operatonal errors, attacks on the IT, or unpredctably long delay paths. From a management perspectve, erratc but extreme volatltes of usage can be added to the potental threats an IT nfrastructure has to cope wth. Consequently, reslence n ths paper s defned as the IT system s ablty to provde a certan predefned QoS even f an unusual hgh but legtmate traffc load occurs (Menasce & Casalccho, 2004a; Menasce, 2004). In ths context the ablty to measure the QoS n Grd systems plays a role of hgh mportance (Collng et al., 2007; Menasce & Casalccho, 2004b). More tradtonal approaches for pooled computer resources address the topc of reslence under the term hgh avalablty HA especally n connecton wth earler cluster computng applcatons (Gray & Seworek, 99). The HA concept defnes classes for the tme rato of a computer system s uptme to ts uptme plus downtme and such provdes a defnton for QoS standard. In contrast to our approach whch ensures QoS by the poolng of computer resources (Grd) that may be heterogeneous or not, HA s manly concerned wth the desgn of fault tolerant (reslent) hardware archtectures by creatng redundant structures (backup solutons) n homogeneous computer envronment. IT reslence s especally mportant f the crtcal nfrastructure s supportng complex adaptve systems (Holland, 968) such as captal and stock markets wth ther low-probablty/hghconsequence events. In mllsecond ndustres such as the fnancal servces ndustry the IT Uptme s the tme the computer system s avalable for provdng servces requested by the users; downtme defnes the tme the system s not avalable.

5 nfrastructure requres sgnfcant attenton to reslence. Operatng at hgh speed, nformaton-based ndustres requre Grd archtecture-lke IT nfrastructure to strengthen reslence, dversty, and redundancy. Thus acknowledgng the role of reslence n crtcal nfrastructure can reduce operatonal rsk n extreme stuatons. 4 MODEL AND SIMULATION FOR GRID COMPUTING IN EXTREME SITUATIONS 4. Smulaton Model To assess the mpact of exogenous shocks lke poltcal or fnancal crses on the IT nfrastructure of hghly departmentalzed enterprses, we conduct a smulaton study and compare two dfferent IT nfrastructures: Frstly, we look at an IT nfrastructure where every department has exclusve access to dedcated servers. Ths s the tradtonal way access to IT resources s organzed n the fnancal servces ndustry today. In the second case, the IT resources are pooled by means of vrtualzaton technque. We call ths IT nfrastructure a Grd. In both cases a sngle IT controller gathers the demand requests of the consumng departments and optmzes the number of servers needed. The enterprse thus conssts of two types of players: Frst, the enterprse conssts of n departments that demand IT resources. For the sake of smplcty, all departments demand d of an unform IT resource gven by the normal dstrbuton N(µ, σ ²). Besde the demand d, these IT consumng departments vary n ther preferences by facng cancellaton costs c. Departments wth urgent or mportant jobs suffer from cancellaton more than departments wth low prorty jobs and therefore demand a hgher level of QoS. The requred level of QoS s thus a functon of c and wll be explaned later. Every department submts ts demand functon to the second type of agent, the IT controllng agent. There s only one nstance of IT controllng aggregatng the demand. The IT controllng agent calculates the amount of computatonal power that s needed to ensure the QoS requred for all departments. Every addtonal server unt costs s and s assumed to be constant and exogenously gven, snce a sngle enterprse does not have an nfluence on the market prce of servers. Every server delvers m unts of unform IT resource. The process of a smulated perod can be descrbed as follows: Smulaton tme s dscrete (e.g. every tme perod equals month) and a fxed number of processes are executed for all agents n every smulated perod. At the begnnng of the smulaton, every department calculates the optmal level of qualty of servces gven by the followng ndfference equaton: s d μ s cdf, ( d ) c = d + erfμ, σ c = d m σ 2 m ( μ σ ) 2 where d s the demand at whch department becomes ndfferent between payng the expected cancellaton costs and payng for d unts of unform IT resource whch cost the server costs s dvded by the computatonal power m per server. The ratonale behnd ths equaton s that a rsk-neutral enterprse s ndfferent between payng the cancellaton costs and payng for the server costs requred for dong the job. By solvng ths ndfference equaton, the department can calculate the cut-off demand d and can then calculate the optmal level of qualty of servce by settng d nto the cumulatve dstrbuton functon:

6 QoS = cdf ( d ) = + erf μ, σ 2 μ, σ d μ σ 2 After the calculaton of the optmal servce level, each department submts ts demand functon and the level of QoS demanded to the IT controllng agent. The IT controllng agent thereafter determnes the optmal number of servers gven the demand and requred QoS by the followng decson rules: For the Non-Grd-Archtecture, the IT controllng agent treats all requests separately and extrapolates the cost of the servce for every sngle department. Ths s the tradtonal busness practce: Department needs a servce and therefore orders dedcated servers for the fulflment of ths servce. The monthly costs are kept constant over tme. The IT controllng agent calculates the optmal numbers of dedcated servers for every sngle department based on ts demand request (demand functon and level of QoS). Ths can easly be done by calculatng the nverse cumulatve dstrbuton functon of the normal dstrbuton and roundng up, snce the number of servers has to be whole-number. The optmal number of servers s for a sngle department s then gven by: ( ( ), ) s = cdfμ, ( QoS ) 2 2 σ = μ + σ erf QoS μ σ m m Thereby, the optmal number of servers for the entre bank s gven by: ( μ σ μ, ( )) σ n sˆ = + 2 erf 2QoS = m For Grd archtectures the IT controllng agent aggregates the demand and allocates enough computatonal power for the pool of departments as a whole. By aggregatng the demand, the varaton of demand may even out. In probablty theory, f X and Y are ndependent random varables that are normally dstrbuted, then X + Y are also normally dstrbuted. Therefore, the total demand TD s gven by: n n n 2 TD = D ~ N μ, σ = = = The optmal number of servers for the bank as system s then gven by the sum of servers for dstnct classes of servce levels. Snce the demanded QoS s heterogeneous, the optmal number of servers should be calculated by buldng classes of homogeneous QoS-classes, summng up the number of servers for these classes: s? sclass = 0 = wth ( ( )) sˆ class = cdfa, b( QoSclass ) = a+ b 2 erfa, b 2QoSclass = m m n n 2 μ + σ 2 erfa, b( 2QoSclass ) m = =

7 wth n a= μ; b= n = = σ 2 After the calculaton of the optmal number of servers requred and ther acquston, the smulaton moves nto the second phase where n every tme step the actual demand s determned. The actual demand s rolled from the gven normal dstrbuton for every department. If the demand can not be fulflled due to a lack of computatonal power, the department suffers as a result of ts cancellaton costs. We calculate the total costs for the entre enterprse, whch are gven by cancellaton costs per department and costs for server mantenance. Overall, the enterprse needs to fnd the optmal number of servers whch should be as low as possble but should also avod cancellaton costs. However, the predcted demand can vary due to some unexpected shock: In economcs a shock s defned as an unexpected or unpredctable event that affects an economy, ether postvely or negatvely. We borrow these constructs from economcs and model extreme stuatons by applyng exogenous shocks, meanng events that affect the IT nfrastructure negatvely, occur rarely and can thus not be taken nto account ex ante. In the fnancal servces ndustry ths mght happen due to a fnancal crss or severe poltcal crses. We model a shock as an unexpected ncrease n demand for IT resources. Ths s a common practce n macroeconomcs (e.g. Hckman & Klen, 984). The rsk of a shock can be expressed as the probablty densty of the consequences. In economcs rsk has therefore two dmensons: The occurrence probablty p and the weght of the consequences w (Zwefel & Esen, 2002, p. 34). Though the departments assess ther demand as d ~N(µ, σ ²), ther actual demand s gven by d ~N(µ *x, σ ²) wth x representng addtonal demand excted by the shock. As suggested by economc theory, the shock s not expected and should thus not be taken nto account when departments report ther expected demand to the IT-controllng agent. The varable x conssts of two parts, x=w*z: Frstly, w models the weght of the consequences and due to the multplcatve composton the consequences n terms of addtonal demand are relatve to µ. If a shock occurs, the demand s then e.g. ncreased by 50% for the department. Secondly, z represents the stochastc part and depends on the occurrence probablty o of the shock. z s a bnomal varable gven by the followng rule: z=, f random number o and z=0 otherwse. Normally, one would expect dfferent departments to be affected to dfferent extents by the shock. However, we assume for the sake of smplcty that a shock nfluences all departments n the same manner. Nevertheless, the absolute nfluence of the shock vares due to the relatve combnaton wth the demand µ. Note that the actual demand functon s not normally dstrbuted anymore snce t s based wth ths demand shock. As dependent varable we observe the qualty of servce and the total costs and compare two dfferent IT-nfrastructures: Frstly, we run the smulaton for a departmentalzed enterprse wth dedcated servers as benchmark, and secondly we evaluate the same scenaro for an enterprse that pools ts resources and apples a Grd IT-nfrastructure by means of resource vrtualzaton. Schwnd et al. (2007) show that Grd technology can drve down costs and ncrease the QoS delvered n the absence of shocks. We also expect ths advantage to be robust n the presence of shocks and thus hypothesze: Ha: Grd technology ncreases the QoS delvered even n the presence of exogenous shocks. Hb: Grd technology drves down the total costs even n the presence of exogenous shocks.

8 These two hypotheses are not very surprsng but we expect that a grdfed IT nfrastructure also ncreases reslence. We therefore ntroduce the followng two hypotheses that have not been tested quanttatvely as far as we know: H2a: Grd technology ncreases reslence n terms of QoS n the presence of exogenous shocks. H2b: Grd technology ncreases reslence n terms of mantenance and cancellaton costs n the presence of exogenous shocks. Our smulaton s based on the followng assumptons: All departments demand a homogeneous, arbtrary, and unform IT resource. The demand d s exogenous. Addtonal IT resources can easly be suppled by acquston of addtonal servers. The prce s for addtonal servers s constant and exogenously gven. Departments can evaluate ther demand a pror accurately, except the addtonal demand that can be excted by some exogenous shock. A shock has the same relatve mpact on all departments n the enterprse. Snce we are not usng unts, results can not be nterpreted on an absolute bass. As we are only nterested n the mpact of the IT nfrastructure type total costs, QoS and the reslence of the system, we can use these fgures to compare on a relatve bass. 4.2 Smulaton Parameters and Results We developed the smulaton based on the second model descrbed from scratch n c# under.net and use the followng ntal parameters: We look at an enterprse wth 25 departments and thus set n=25. The costs for a server are set to s=0,000 and every server produces m=00 unts of unform IT resources. The average demand for each department s drawn from N~(000,250) and the standard devaton for ths demand s drawn from N~(00,25). Overall, we look at 365 tme steps per scenaro. 75,00% 70,00% 70,00%-75,00% 65,00%-70,00% 60,00%-65,00% 55,00%-60,00% Delvered Qualty of Servce 65,00% 60,00% 55,00% 9,25% 7,75% 6,25% Probablty of Shock o 4,75% 3,25%,75% 0,25% 50,00% 225,00% 400,00% Weght of Consequences w Fgure. Delvered Qualty of Servce as Functon of o and w for a Non-Grd Archtecture

9 95,00% 90,00% 90,00%-95,00% 85,00%-90,00% 80,00%-85,00% Delvered Qualty of Servce 85,00% 80,00% 9,25% 7,75% 6,25% Probablty of Shock o 4,75% 3,25%,75% 0,25% 50,00% 225,00% 400,00% Weght of Consequences w Fgure 2. Delvered Qualty of Servce as Functon of o and w for a Grd-Archtecture We vary the probablty of occurrence o from 0.25% to 0% and the weght of consequences w from 50% to 400%, w=200% would hence mean that the demand has doubled due to the shock. We also vary the cancellaton costs c for every department whch s drawn from N~(µ cancellatoncosts, σ² cancellatoncosts ). µ cancellatoncosts runs from 00,000 to 500,000 and σ cancellatoncosts from to 200,000. As depcted n Fgure the QoS delvered n Non-Grd envronments drops from around 75% wth ncreasng probablty for shock occurrence o and ncreasng weght of consequences w snce the addtonal demand was not taken nto account. It s also obvous that the decrease n QoS s very steep even wth small ncreases n w. Fgure 2 shows the same settng for an enterprse that pools ts resources. The QoS s on average 9.02% whereas the non-grd-archtecture delvers only an average QoS of 67.69%. We conduct an ANOVA (Analyss of Varance) to test hypothess Ha and fnd support on the %-level (p<0.0). Therefore we conclude that the vrtualzaton enabled by Grd technology ncreases the absolute level of QoS even when the system suffers from stochastc shocks. Surprsngly, ths ncrease n QoS due to vrtualzaton comes wth lower costs: we use the total costs, whch are the sum of mantenance costs and cancellaton costs, as dependent varable, normalze t to and test Hb. The ANOVA shows that the total costs usng the Grd nfrastructure are only 67.02% of the total costs of the equvalent non-grd-nfrastructure and s hence sgnfcantly (p < 0.0) lower. We thus fnd support for Hb and conclude that Grd technology leads to hgher QoS and smultaneously to lower total costs. Fgure 2 also demonstrates that the loss n QoS s not as steep wth ncreasng w and/or o as t s n Fgure. To make ths effect more vsble we pck a typcal scenaro and fx w=2.25, µ cancellatoncosts =500,000 and σ cancellatoncosts =200,000 and calculate the margnal loss of QoS wth ncreasng o. Ths scenaro s depcted n Fgure 3. The loss of qualty of servce per addtonal pont of shock probablty s sgnfcantly (p<0.05) hgher wth non-grd-archtectures than wth Grd archtecture. To test hypotheses 2a and 2b rgorously, we run a lnear regresson for both dependent varables and compare the coeffcents of o and w across the two scenaros (non-grd vs. Grd).

10 0,00% -,00% 0,25%,00%,75% 2,50% 3,25% 4,00% 4,75% 5,50% 6,25% 7,00% 7,75% 8,50% 9,25% 0,00% Loss of Qualty of Servce -2,00% -3,00% -4,00% -5,00% -6,00% Non-Grd Grd -7,00% -8,00% Shock Probablty Fgure 3. Margnal Loss of QoS wth ncreasng o Table reveals some nterestng nsghts: Frstly, QoS n Grds s typcally hgher whch s depcted by the hgher constant. Secondly, hgher cancellaton costs ncrease QoS for both archtectures, snce t s cheaper to buy new servers than to drop jobs unfnshed when cancellaton costs are hgh. Thus, µ cancellatoncosts has a postve sgn. However, the nfluence of the mean cancellaton costs s sgnfcantly hgher wth non-grd-archtecture than wth Grd archtecture. Scenaro Dependent Varable Non-Grd QoS Grd QoS Non-Grd Total Costs Grd Total Costs Constant *** *** *** *** µ CancellatonCosts.39e-006 *** e-006 *** 5.78e-007 *** 2.556e-007 *** σ CancellatonCosts -3.63e-007 *** -.336e-007 *** -0.72e-006 ***.534e-008 *** Shock Probablty o Weght of Consequences w *** *** 0. *** 0.07 *** *** *** 0.23 *** *** R² 79.7% 55.5% 67.% 75.3% Table. Regresson Model wth QoS and TotalCosts as Dependent Var. (n=9,000 each), *** p<0.0

11 Hgher varance n cancellaton costs gven by σ cancellatoncosts usually leads to a loss n QoS. Ths s qute ntutve and not surprsng. Agan, we recognze that the Grd archtecture s more robust to a hgher varance than the non-grd-archtecture. Both parameters descrbng extreme stuatons, o and w, have a negatve nfluence on the QoS delvered, as expected. However, we agan recognze by the smaller coeffcents that the Grd- Archtecture s more reslent aganst these shocks. Grd s especally robust n scenaros where the weght of the consequences s hgh. The defect of departmentalzed IT archtectures wth dedcated servers already became evdent n the chart depcted n Fgure. Hypothess 2a cannot thus be rejected, and we conclude that Grds are more reslent n terms of QoS than equvalent tradtonal non-grdarchtectures wthout vrtualzaton. All these fndngs equally hold for total costs as dependent varable. Grd technology drves down costs sgnfcantly, the archtecture s more robust aganst changes n cancellaton costs and fnally shocks do not have such a severe nfluence on costs. Hypothess 2b cannot be rejected. 5 CONCLUSION We presented a smulaton model that allows us to nvestgate the consequences of extreme events (e.g. market crashes) for the IT nfrastructure of an enterprse n the fnancal ndustry that s organzed n a departmentalzed structure. In a frst step our model calculates the optmal sze of IT nfrastructure (servers) n the enterprse s departments, both for a Grd and a non-grd-archtecture. After ths step, the mpact of extreme events (e.g. load peaks n the case of a fnancal crash) on ths type of resource allocaton s smulated for the two approaches by varyng the probablty of exogenous shocks and the weght of ther consequences. As a result, t turned out that the Grd archtecture s not only able to mantan a hgher qualty-of-servce level n the presence of such exogenous shocks compared wth a non-grd-soluton, but s also able to reduce the cost for jobs that are cancelled due to the hgh load stuaton n the computng system. We were also able to demonstrate that a Grd-soluton s far less senstve to the mpact of extreme events than a non-grd-system, both n terms of the shocks probablty and ther consequences. Our smulaton results suggest the ntroducton of Grd computng nto busness applcatons due to a sgnfcant cost reducton and ncreased system reslence. If we are able to prove the relevance of our smulaton results n real world settngs, together wth our ndustral partners n the fnancal servces ndustry, ths wll be the frst step nto the drecton of tradng Grd compute resources as a flexble ntra-enterprse utlty. The next step should be the nter-enterprse exchange of computatonal resources. Ths could help to further ncrease reslence to extreme events, especally f the exchange of compute resources s establshed between enterprses that belong to dfferent ndustry sectors. At the moment, however, securty concerns prevent such a globalzaton of Grd computng, especally n the fnancal ndustry sector. References Ansoff, I. (965) Corporate strategy. McGraw-Hll, New York. Afgan, E.; Bangalore, P., (2007) Computaton Cost n Grd Computng Envronments; In Proceedngs of the Frst Int. Workshop on the Economcs of Software and Computaton (ESC '07), Mnneapols, Mnnesota, USA, pp Baker, M., Buyya, R. and Laforenza, D. (2002) Grds and grd technologes for wde-area dstrbuted computng. Software: Practce and Experence 32, Castan, R. and Squyres, J. (2007) Creatng a transparent, dstrbuted, and reslent computng envronment: The openrte project. Journal of Supercomputng 42, Chelots, G., Kenyon, C. and Buyya, R. (2004) Grd economcs: 0 lessons from fnance. In Peer-topeer computng: Evoluton of a dsruptve technology (Subramanan, R. and Goodman, B., Eds.), Idea Group Publsher, Hershey, PA.

12 Choon Lee, Y. and Zomaya, A. Y. (2007) Practcal schedulng of bag-of-tasks applcatons on grds wth dynamc reslence. IEEE Transactons on Computers 56 (6), Collng, D., Ferrar, T., Hassoun, Y., Huang, C., Kotsokals, C., Mcgough, S., Patel, Y., Roncher, E. and Tsanakas, P. (2007) On qualty of servce support for grd computng. In Proceedngs of the 2nd Internatonal Workshop on Dstrbuted Cooperatve Laboratores and Instrumentng the GRID (INGRID 2007). Czajkowsk, K., Ftzgerald, S., Foster, I. and Kesselman, C. (200) Grd nformaton servces for dstrbuted resource sharng. In Tenth IEEE Internatonal Symposum on Hgh-Performance Dstrbuted Computng (HPDC-0), p 8, IEEE Press, Los Alamtos, CA, USA. Eymann, T., Rencke, M., Ardaz, O., Artgas, P., Fretag, F., Messeguer, R., Navarro, L. and Royo, D. (2003) Decentralzed vs. Centralzed economc coordnaton of resource allocaton n grds. In. European Across Grds Conference. Gray, J. and Seworek, D. P. (99) Hgh avalablty computer systems. Computer 24 (9), Greenercomputng (2007) Greener computng. Greengrd (2007) The green grd. Hckman, B. G. and Klen, L. R. (984) Wage-prce behavor n the natonal models of project. Amercan Economc Revew, Holland, J. H. (968) Herarchcal descrptons, unversal spaces and adaptve systems. Engneerng, College of, Ann Arbor. Huang, Y. and Bhatt, S. N. (2004) Decentralzed reslent grd resource management overlay networks. In IEEE Internatonal Conference on Servces Computng pp , IEEE Computer Socety, Shangha, Chna. Menasce, D. A. (2004) Mappng servce-level agreements n dstrbuted applcatons. IEEE Internet Computng 8 (4), Menasce, D. A. and Casalccho, E. (2004a) Qos n grd computng. IEEE Internet Computng 8 (4), Menasce, D. A. and Casalccho, E. (2004b) Qualty of servce aspects and metrcs n grd computng. In Computer Measurement Group Conference 2004, Las Vegas, NV. Patel, C., Sharma, R., Bash, C. and Graupner, S. (2002) Energy aware grd: Global workload placement based on energy effcency. HP Laboratores, Palo Alto. Schwnd, M., Gujo, O. and Stockhem, T. (2006) Dynamc resource prces n a combnatoral grd system. In Conference on E-Commerce Technology (CEC'06), IEEE Press, San Francsco, US. Schwnd, M., Hnz, O. and Beck, R. (2007) A cost-based mult-unt resource aucton for servceorented grd computng. In 8th IEEE/ACM Internatonal Conference on Grd Computng (Grd 2007) Austn, Texas. Skllcorn, D. B. (2002) Motvatng computatonal grds. In 2nd IEEE Internatonal Symposum on Cluster Computng and the Grd (CCGrd 2002), pp , IEEE Computer Socety. Xe, L., Smth, P., Banfeld, M., Leopold, H., Sterbenz, J. P. G. and Hutchson, D. (2005) Towards reslent networks usng programmable networkng technologes. In IFIP IWAN 2005, Sopha- Antpols, France. Zwefel, P. and Esen, R. (2002) Verscherungsökonome. Sprnger, Hedelberg.