An AnyLogic Simulation Model for Power and Performance Analysis of Data Centres

Transcription

1 An AnyLogic Simulation Modl fo Pow and Pfomanc Analysis of Data Cnts Bjön F. Postma and Boudwijn R. Havkot Cnt fo Tlmatics and Infomation Tchnology, Univsity of Twnt, th Nthlands {b.f.postma, Abstact. In this pap w popos a simulation famwok that allows fo th analysis of pow and pfomanc tad-offs fo data cnts that sav ngy via pow managmnt. Th modls a coopating disct-vnt and agnt-basd modls, which nabl a vaity of data cnt configuations, including vaious infastuctual choics, wokload modls, (htognous) svs and pow managmnt statgis. Th capabilitis of ou modlling and simulation appoach is shown with an xampl of a 200-sv clust. A validation that compas ou sults, fo a stictd modl with a pviously publishd numical modl is also povidd. Kywods: Data cnts, simulation, disct-vnt modls, agnt-basd modls, pow managmnt, pfomanc analysis, pow-pfomanc tad-off, cascading ffct, tansint analysis, stady-stat analysis 1 Intoduction In , th global pow consumption of data cnts (DCs) was appoximatly 40 GW; this numb is still incasing [7]. Hnc, bing abl to valuat th ffct of ngy-savings masus is valuabl. On such ngy-savings masu is pow managmnt (PM), which tis to low th pow stat of svs, whil pfomanc is kpt intact. Moov, th so-calld cascad ffct (to b discussd lat; cf. [8]) on ngy consumption in infastuctu, stngthns th ffcts of PM statgis. This pap aims to obtain insight in pow usag and systm pfomanc (masud in tms of thoughput and spons tims) in aly DC dsign phass. It psnts high-lvl modls to stimat DC pow consumption and pfomanc. W will psnt and simulat coopating modls fo (a) IT quipmnt, (b) th cascad ffct, (c) th systm wokload, and (d) pow managmnt. Th valu of ou modls is shown though th analysis and simulation Th wok in this pap has bn suppotd by th Dutch national STW pojct Coopativ Ntwokd Systms (CNS), as pat of th pogam Robust Dsign of Cyb- Physical Systms (CPS). Th wok in this pap has bn suppotd by th EU FP7 pojct Slf Engysuppoting Autonomous Computions (SENSATION; gant no ).

2 2 Bjön F. Postma, Boudwijn R. Havkot of an xampl DC. Ou modls combin disct-vnt modls and agnt-basd modls. Simulating ths modls shds light on th abov-mntiond powpfomanc tad-off. Fo th constuction of ou modls, th multi-mthod simulation tool AnyLogic [1] is usd. AnyLogic suppots a mixtu of th common mthodologis to build simulation modls: (a) systm dynamics, (b) pocss-cntic/disct-vnt modlling, and (c) agnt-basd modlling. In this pap, w do not us systm dynamics. Disct-vnt modlling is a suitabl appoach fo th analysis of systms that ncompass a continuous pocss, that can b dividd into disct pats. Each pat is chaactisd by tigging an vnt. As [15, p.6] stats about disct-vnt simulation: Disct-vnt simulation concns th modling of a systm as it volvs ov tim by a psntation in which th stat vaiabls chang instantanously at spaat points in tim. Ths points in tim a th ons at which an vnt occus, wh an vnt is dfind as an instantanous occunc that may chang th stat of th systm. Agnt-basd modlling allows to modl individual bhaviou to obtain global bhaviou with so-calld communicating agnts. It allows to asily spcify htognous populations. As [15, p. 694] stats about agnt-basd simulation: W dfin an agnt-basd simulation to b a DES wh ntitis (agnts) do, in fact, intact with oth ntitis and thi nvionmnt in a majo way. This pap contibuts by taking th fist stps towads accuat insight in both pow and pfomanc by psnting simpl quuing modls of IT quipmnt that a asy to xtnd and allow htognity. Also, a modl fo th cascading ffct is takn into account, and wokloads can b basd on gnal pobability distibutions o on masumnt data. Moov, th insight in pow and pfomanc has stong visual suppot fo tansint and stady-stat analysis. Nxt stps that follow fom this sach involv fining and validation of modls fo mo alistic cas studis basd on masumnts and knowldg obtaind fom coopation with th pojct patn Tagt Holding that allocatd thi IT quipmnt in th Cntum voo Infomati Tchnologi (CIT) data cnt in Goningn, th Nthlands. Ov th last fw yas, vaious authos hav poposd modls fo th analysis of th pow-pfomanc tad-off in data cnts. Numical solutions to comput pow and pfomanc fo DCs basd on Makov modls hav bn poposd in [14], [9], [11], fluid analysis has bn poposd in [17] and stochastic Pti nts in [16], [5], [12]. All ths numical appoachs allow fo th apid computation of tad-offs, but a oftn limitd in thi modlling capabilitis, thus laving thm usful fo only fw mtics und limiting assumptions. Simulation using AnyLogic, as w popos h, might b slow, howv, it can handl a wid vaity of DCs than numical analysis and scals wll to lag systms (as w will s).

3 Simulation Modl fo Pow and Pfomanc Analysis of Data Cnts 3 Th pap is futh oganisd as follows. Fist, th DC and its contxt a dscibd in Sction 2. Sction 3 continus fom this systm dsciption by intoducing all modls, mtics and visualisation. A cas study with a 200-sv xampl and modl validation a psntd in Sction 4, followd by Sction 5 with th conclusions and futu wok. 2 Systm Dsciption In [2], impotant custom dmands fo DCs a distinguishd, that dict choics on th systm achitctu, namly: availability, scalability, flxibility, scuity and pfomanc. Th minimum quimnts fo a sv a location, spac, pow supply, ntwok accssibility and halthy nvionmnt conditions. Th dmands fom th custom and sv quimnts div th choic of th most lvant componnts in a typical DC. Thfo, a data cnt consist of vaious componnts, as dscibd in [3], which a typically: Automatic Tansf Switchs (ATSs), Unintuptibl Pow Supplis (UPSs), Pow Distibution Units (PDUs), svs, chills, cools, ntwok quipmnt and dvics fo monitoing and contol. Though th ntwok th DC bcoms accssibl fom th outsid wold. Th wokload of a DC is th amount of wok that is xpctd to b don by th DC. Th wokload of a DC is an impotant indication fo functionality and fficincy. An indication of th wokload in a DC is th numb of jobs p tim unit that aiv via th ntwok, togth with th lngth (distibution) of th jobs. Jobs snt though th ntwok aiv in a buff of a load balanc, that schduls th jobs. W assum that stoag and ntwok quipmnt guaant ngligibl job losss in this buff. Engy consumption can b ducd in DCs in sval ways [8]. On way is pow managmnt (PM), that aims to switch svs into a low pow stat to duc pow consumption, whil pfomanc is kpt intact. Th challng is to minimis th numb of idl svs but pvnt unaccptabl pfomanc dgadation. Somtims ngy consumption ducs at th cost of pfomanc, sulting in a tad-off. W will illustat such tad-offs lat in th pap. 3 Data Cnt Modls Sction 3.1 psnts an ovviw of all implmntd agnt-basd modls basd on Sction 2. Ths agnt-basd modls a built fom undlying quuing modls, stat-chat modls and functions fo analysis, which a dtaild in Sctions Finally, pow and pfomanc mtics a psntd in Sction Modl Ovviw All lvant ntitis a modlld as agnts, which nabls asy xtnsion towads htognous ntitis. An ovviw of all agnts is givn in th UML diagam in Figu 1.

4 4 Bjön F. Postma, Boudwijn R. Havkot Fig. 1. All implmntd agnts in on UML diagam. Th MainMnu agnt links to th agnts PowPfomanc, Infastuctu and Configuation with visual psntation of th sults (light gy). Th oth agnts, i.., DataCnt, Cascad, LoadBalanc, EngySuppli, Taffic, Pow Managmnt, Svs and Jobs a th DC modls, including a visual psntation (dak gy). In th upcoming subsctions, th modls insid ths agnts a discussd. Th modls insid th agnt-basd modls a quuing modls, stat-chat modls and functions fo analysis. 3.2 IT Equipmnt Modl Jobs aiv in a quu in a load balanc. Th load balanc dcids to which sv th jobs should b dispatchd dpnding on th stat infomation. Figu 2 shows an G G 1 quu of th load balanc. Jobs aiv in a FIFO buff in th load balanc accoding to a gnal aival pocss (lft-most quu) and a svd (big cicl) in on of th M svs aft injction of th job in on of th sv quus and waiting fo svic th. In od to comput spons tims, th LoadBalanc agnt flags a job with a tim stamp bfo it nts th load balanc quu. Whn a job is finishd it compas th tim stamp with its cunt tim stamp to comput a spons tim sampl. Each Sv agnt compiss a G G 1 quu with FIFO buff. Th jobs fom th load balanc a injctd and aiv at th sv quu. At most on job at a tim is svd with a gnally distibutd svic tim (with man valu 1/µ). If a sv has bn switchd off, thn no jobs a outd to it. Th main ason fo this modlling appoach, instad of dictly using an G G M quu, is that any schduling algoithm basd on th stat infomation of th sv can b implmntd in this famwok, and it also allows fo htognous svs. Th pow stat of a sv indicats how th sv is usd and how much pow is consumd fo that us. Th sv stat can b dscibd with a stat-

5 Simulation Modl fo Pow and Pfomanc Analysis of Data Cnts 5 Fig. 2. Load balanc and svs quuing modls. Fig. 3. Stat-chat modl of sv with slp pow stats. chat modl that switchs btwn th low pow consuming inactiv Aslp stat and th high pow consuming activ stats Idl and Pocssing, that is contolld by xtnal agnts via mssags; as dpictd in Figu 3. Initially, th sv is idl, i.., th initial stat is Idl. Whn th sv is activ, it can switch btwn th pow stat Pocssing (200 W) and Idl (140 W). Whn a sv civs a slp mssag, it fist nds tim to suspnd th systm in

6 6 Bjön F. Postma, Boudwijn R. Havkot pow stat Slping (200 W). Aft a gnally distibutd tim with man 1/α sl, th sv is in pow stat Aslp (14 W). Pow stat Waking (200 W), which taks xta tim bfo th sv stats pocssing th fist job, i.., aft a gnally distibutd tim with man 1/α wk th sv is back on. Th cycl to shut down and boot a sv follows th following squnc of pow stats: Idl (140 W) Shutting Down (200 W) Off (0 W) Booting (200 W) Pocssing (200 W). Th svs lav th pow stat Booting aft a gnally distibutd tim with man 1/α bt and th pow stat Shutting Down aft a gnally distibutd tim with man 1/α sd. Th pow consumption valus as usd h a takn fom [10]. Th usd pow stat modl is highly abstact and could b find,.g., basd on cnt sults fo CPU-intnsiv wokloads [13]. Th cuntly implmntd job schduling dpnds on th pow stat of svs. Initially, a andom idl sv is slctd. If no idl sv is psnt, an off sv is slctd. In cas only activ svs a availabl, a andom sv is slctd. Anoth vaiant of a schduling mchanism is to injct a job in th sv with th shotst quu. In cas th a multipl shotst quus, a andom sv is chosn; such (and oth) vaiants can all b asily implmntd in ou famwok. 3.3 Cascad Modl Th cascad ffct, as laboatd on bfo, occus in many DC infastuctu componnts that consum pow basd on sv pow consumption. Fig. 4. EngyLogic s cascad ffct modl. Th modl fo th cascad ffct in DCs fom [8], as dpictd in Figu 4, is usd in th Cascad agnt. Fo ach unit of pow usd by th svs, oth DC infastuctu componnts,.g., DC-DC, AC-DC, Pow distibution, UPS, cooling, building switchga/tansfom wast pow in a lina lation. Hnc,

7 Simulation Modl fo Pow and Pfomanc Analysis of Data Cnts 7 ngy savings at th lvl of th sv has gat impact on th ovall ngy usag. Th Cascad agnt computs th pow consumption mtics via simpl lina functions. 3.4 Wokload Basd on th dsciption fom Sction 3.2, jobs nt th load balanc in a G G 1 quu following a gnally distibutd int-aival tim. In Any- Logic, th most common pobability distibutions a p-implmntd functions,.g., xponntial, nomal, unifom and Elang. Th agnt Job is addd to th buff aft an int-aival tim basd on a function call that gnats a andom vaiabl fo th spcifid pobability distibution. Additionally, in combination with th Taffic agnt, custom disct and continuous pobability distibutions can b dfind using,.g., fquncy tabls o obsvd sampls. In this pap, w only discuss gnally distibutd tims with tim-constant mans and jobs with fixd man lngths, yt ou simulation dos allow tim-vaying mans in od to suppot alistic tim-vaying wokload with htognous jobs obtaind fom masumnts in data cnts. 3.5 Pow Managmnt Statgis Without application of PM, all svs in th DC a ith pocssing o idl. PM, howv, aims to switch svs into low pow stats to duc pow consumption whn th wokload is low, whil pfomanc is kpt intact. Th PowManagmnt agnt has functions to dcid whn svs nd to b put to slp o vn switchd off, and whn svs nd to b switchd on. In od to dmonstat th capability of implmnting statgis in ou famwok, two of th functions a illustatd h. Customs of DCs oftn dmand a ctain pfomanc with a Svic Lvl Agmnt (SLA),.g., th spons tim in a DC should nv xcd 25 ms (R ths = s). Th thshold statgy tis to stay as clos to this spons tim as possibl by putting svs to slp until it gts too clos to th thshold and svs a again wokn. In mo dtail, th spons tim gts too clos to th thshold whn th latst obsvd sampl xcds 80 % of R ths. Svs a put to slp whn th latst obsvd sampl is low than 60 % of R ths. In futu wok, w will invstigat mo advancd thshold statgis,.g., including hystsis. Th aim of th shut-down statgy is to achiv a wokload of all activ svs that is qual to a p-dfind pcntag,.g., a sv wokload of 20 % mans a sv spnds on avag 20 % of th tim pocssing, whn jobs a qually schduld among all svs. As a consqunc, svs a shut down to achiv that goal. Th only xcption to this ul is whn th a not nough svs in th DC. 3.6 Pow-Pfomanc Mtics Quantitativ mtics a usd to povid insight into pow and pfomanc in DCs.

8 8 Bjön F. Postma, Boudwijn R. Havkot Pow Consumption An infastuctu componnt c has pow consumption P c (t) (in Watt) at tim t (in sconds). Pow consumption P svi (t) of sv i dpnds on th sv s pow stat. Th total pow consumption of K svs P svs (t) at tim t: K P svs (t) = P svi (t). (1) i=1 Th pow consumption of oth systm componnts (lik infastuctu), P oth (t) = j P j(t), wh j sv i fom all oth componnts is computd though th cascad modl. Th total pow consumption thn quals th sum of pow consumption by all componnts, i.., P total (t) = P oth (t) + P svs (t). Th man pow consumption up to tim t is computd as: E[P total (t)] = 1 t t x=0 P total (x)dx. (2) Not that this intgal is not xplicitly computd, but that an fficint disctisation taks plac. This disctisation taks full advantag of th fact that vnts tigg changs in th pow consumption, i.., th is a picwis lina function fo th pow consumption ov tim. Th man pow consumption up to tim t, wh k vnts occu at tim 0, 1,..., k within th intval [0, t] with a fixd fist vnt 0 = 0 and a fixd last vnt k = t, is computd as: 1 E[P total (t)] = k 0 1 = k 0 k i=0 i+1 P total (x)dx (3) x= i k ( i i 1 )P total ( i ) (4) i=0 Rspons Tim This is th dlay R i (in ms) fom th momnt a job i nts until th momnt it lavs th DC. So, ach job will pot its spons tim R i. Givn m obsvations, th man spons tim is computd as: E[R] = 1 m m R i. (5) Pow Stat Utilisation Th pow stat utilisation ρ i (t) is th pcntag of svs in a paticula pow stat i at tim t, with ρ i (t) [0, 1]. Th sum of all pow stat utilisations at tim t is xactly 100 %, i.., i ρ i(t) = 1. Th man pow stat utilisation up to tim t is computd as: E[ρ i (t)] = 1 t t i=1 x=0 ρ i (x)dx. (6) In pactic, th intgal is not xplicitly computd, but an fficint disctisation taks plac, simila as don fo th man pow consumption. Th man pow

9 Simulation Modl fo Pow and Pfomanc Analysis of Data Cnts 9 stat utilisation up to tim t, wh k vnts occu at tim 0, 1,..., k within th intval [0, t] with a fixd fist vnt 0 = 0 and a fixd last vnt k = t, is computd as: k Z 1 X i+1 E[ρi (t)] = ρi (x)dx k 0 i=0 x=i = 3.7 k X 1 (i i 1 )ρi (i ) k 0 i=0 (7) (8) Visualisation Th PowPfomanc and Infastuctu agnts a implmntd to show visuals and liv valus obtaind fom th simulation uns. Fig. 5. Th dashboad fo th IT quipmnt. Figu 5 shows an intuitiv dashboad with sults and configuation paamts of th DC modl. Th top lin shows a mnu ba with (1) links to th modl, visuals and configuation. A cumulativ utilisation plot (2) shows liv how many svs a in ach pow stat. A stack chat blow this plot shows th man cumulativ utilisation, i.., how many svs a in ach pow stat. Futhmo, two tim plots (3) show liv pow consumption (lft) and liv

10 10 Bjön F. Postma, Boudwijn R. Havkot spons tim (ight) of th simulation. Two histogam plots (4) show th distibution of sampls usd to comput th mans of pow consumption (lft) and spons tim (ight). Th valus of th mans a displayd in a small tabl including confidnc intvals (5); th xact way how ths confidnc intvals a computd is not cla (to us) fom th documntation, hnc, ths should b handld with ca. Tabl (6) shows th xact numb of svs in ach pow stat, th total numb of svs in th DC and th total numb of jobs in th quu(s). Configuation options (7) can b usd to chang th bhaviou of th simulation on th fly: adjusting th sv wokload, th PM statgis, st th avags and disabl avags a th main configuation options. Additional configuation options a availabl in th Configuation agnt, lik changing th aival, svic, and booting tim distibutions. 4 Rsults Fist, an xampl of a data cnt with a 200-sv computational clust is laboatd to illustat th capabilitis of th simulation modls in Sction 4.1. Nxt, stps a takn fo modl validation by compaison of th sults obtaind fom simulation to sults obtaind fom modls that a solvd numically in Sction Cas Study: Computational Clust W addss a DC that nds to b installd with 200 svs. A Svic Lvl Agmnt (SLA) pmits a spons tim of at most 25 s. Jobs a svd, and, qui on avag 1 s svic tim. Futhmo, w qui that at most 33 % of all svs a pocssing, which is not unusual [4]. Booting and shutting down of svs qui xactly 100 s and going to slp and waking up nd only 10 s. Th Pow Usag Efficincy (PUE) of th DC is 1.5, i.., 1 W savd at sv lvl cosponds to 1.5 W savd in total; this is in lin with th cascad ffct modl of Sction 3.3. Futhmo, all th oth IT quipmnt (that is, th non-svs) consum 1000 W, in total. Tabl 1 shows an ovviw of wokload (λ), svic tim distibution (µ), IT quipmnt spcifications (man booting tim α bt, man shutting down tim α sd, man slping tim α sl and man waking tim α wk of svs), numb of svs (n), PUE and pow consumption by oth IT quipmnt (P othit ). Figu 6 shows th pow consumption in ach pow stat, combind with a lgnd fo tim-cumulativ utilisation plot fo th shut-down statgy. Fist assum that th xact wokload is known at all tims, and th shutdown statgy (as dscibd in Sction 3.5) is applid. Figu 7 shows tansint bhaviou in a tim-cumulativ utilisation plot. Th x-axis psnts th modl tim t (in s) and th y-axis shows th pcntag of svs in ach of th pow stats. Th wokload without PM is aound 33 %. With PM switchd on, 50 % of all svs is shut down, such that 66 % of all activ svs a pocssing jobs.

11 Simulation Modl fo Pow and Pfomanc Analysis of Data Cnts Tabl 1. DC configuation and wokload. λ αbt αsl n PothIT xp(33.0) µ xp(1.0) dt(100) αsd dt(100) dt(10) αwk dt(10) 200 svs PUE W S v sp oc s s i ng ( 200W) S v sboo ng ( 200W) S v ss l pi ng ( 14W) S v soff ( 0W) 11 S v si dl ( 140W) Fig. 6. Lgnd and pow consumption in pow-stats. Fig. 7. Tim-cumulativ utilisation plot with shutdown statgy. Fig. 8. Tim-spons tim plot with thshold statgy. Fig. 10. Tim-cumulativ utilisation plot with thshold statgy. Fig. 11. Rspons tim sampls distibution with thshold statgy. Fig. 9. Tim-pow consumption plot with thshold statgy. Fig. 12. Pow consumption sampls distibution with thshold statgy. Futhmo, th man pow consumption is 18 kw and th man spons tim is 1 s. In pactic, th futu wokload is not xactly known. If wokload pdiction is inaccuat, lat spons of th PM statgy can damatically incas th numb of jobs in th systm. Such situations hav lad to wos pfomanc, ith by doppd jobs o lag quus. Th thshold statgy (as dscibd in Sction 3.5) is basd on spons tims ath than on th wokload to contol th pow stat of svs. Fo this statgy, th man valus a computd and tim plots a gnatd (as can b sn fom Figu 8 10). Th man spons tim E[R] 23 s and man pow consumption E[Psvs ] 20 kw. Figu 8 shows a tim-spons tim plot with again on th x-axis th modl tim t and on th y-axis a gn lin intpolating btwn th spons tim

12 12 Bjön F. Postma, Boudwijn R. Havkot sampls. A hoizontal d lin is dawn to indicat th spons tim thshold R ths = 25 s. Moov, Figu 9 dpicts a tim-pow consumption plot with modl tim t on th x-axis and a blu lin that intpolats btwn pow consumption P svs (t) sampls on th y-axis. Futhmo, Figu 10 shows a tim-cumulativ utilisation plot. Th x-axis psnts th modl tim t (in s) and th y-axis shows th pcntag of svs in ach of th pow stats. As sn in Figu 8 10, svs wak (fo t [1120, 1140]), bcaus th obsvd spons tims a appoaching th thshold. Thfo, pow consumption incass fom 20 kw to 25 kw and th spons tim dcass fom 24 s to 21 s. Th nxt stp is to put svs to slp again (fo t [1140, 1220]), bcaus th pcivd spons tim is fin. As a consqunc, spons tims incas again fom 21 s to 23 s, but pow consumption dcass fom 25 kw to 15 kw. 4.2 Modl Validation Fo a simpl but vy simila modl, numical solutions using stochastic Pti nt (SPN) modls hav bn psntd in [16], also to comput man spons tim and man pow consumption, again to analys th pow-pfomanc tad-offs causd by PM (but no spons tim and pow consumption distibutions). Tabl 2. DC configuation and wokload. λ xp(1.0) µ xp(1.0) α bt xp(0.01) α sd n.a. n 2-10 svs β xp(0.005) In this pap, w compa th pow-pfomanc mtics obtaind fom ou simulation DC modls to simila mtics found in th numical appoach, that was psntd in [16]. Thfo, th DC modl is configud to xactly th sam ats, pow managmnt statgy, numb of svs and job schduling as with th numical solution. Whil this validation covs only a fw scnaios, this compaison dos show th fasibility of xpssing modls with th xact sam data cnt scnaio that appoach th sam pow and pfomanc valus. Tabl 2 shows th configuation and wokload. Th Poissonian aival at λ = 1.0 jobs/s, α bt = 0.01 svs/s, and µ = 1.0 jobs/s. A spcial PM statgy is implmntd with an xponntially distibutd las tim with at β = svs/s that dtmins th numb of svs shutting down p scond whn idl; not that dtministic tim-outs a not allowd in stochastic Pti nts, which xplains why th tim-out has bn chosn lik this with th numical appoach. Th numb of svs is scald fom 2 to 10. Tim spnd on shutting down a sv is ignod. Figu 13 and Figu 14 show cumulativ pow stat utilisation plots fo th svs with th PM statgy, fo spctivly th SPN-basd numical analysis

13 Simulation Modl fo Pow and Pfomanc Analysis of Data Cnts 13 Fig. 13. Cumulativ utilisation plot whn scaling th numb of svs fo numical analysis. Fig. 14. Svs-cumulativ utilisation plot whn scaling th numb of svs fo simulation. simulation numical simulation numical Man pow consumption (in W) Numb of svs Man spons tim (in s) Numb of svs Fig. 15. Man pow consumption fo vaious numb of svs fo simulation and numical analysis. Fig. 16. Man spons tim fo vaious numb of svs fo simulation and numical analysis. and ou simulation. Th x-axis psnts th numb of svs n and th y-axis shows th pcntag of svs in ach of th pow stats (fom top to bottom: d = off, oang = idl, gn = booting and blu = pocssing). Th plots confim ach oth as th plots appoach simila shap, but diffnt valus; th plots a not compltly th sam, which is patly th cas du to th fact that w un a stochastic simulation, which, in ssnc, is a statistical xpimnt. Anoth ason fo th obsvd diffnc lis in th implmntation of th job schduling: th SPN-basd modls us only on gnal buff, whas ou simulation modls us a spaat buff p sv. Figu 15 shows th man pow consumption fo vaious svs in a DC. Th x-axis psnts th numb of svs n and th y-axis shows man pow consumption (in W). Th cuvs fo simulation and numical analysis hav simila shap, but with diffnt valus, which ang, spctivly, fom 353 W and 342 W fo 2 svs to 982 W and 939 W fo 10 svs. Figu 16 shows th man spons tims fo vaious svs in a DC with th PM statgy. Th shap of both cuvs a again vy simila, but with diffnt valus, which a fo simulation and numical analysis spctivly fom 5.2 s and 1.84 s fo 2 svs to 2.19 s and 1.25 s fo 10 svs. Anoth ason, fo th diffnt valus in th cuvs, is that numical analysis has no load balanc, but an implicit way fo schduling jobs. Fist, jobs a schduld to a andom idl sv with both numical analysis and simulation.

14 14 Bjön F. Postma, Boudwijn R. Havkot Othwis, th jobs a schduld to a andom off sv. If all svs a booting o pocssing, numical analysis kps th jobs in th buff and simulation injct th job in a andom sv. 5 Conclusions & Futu Wok Th contibution of this pap is th psntation of a nw AnyLogic-basd tool with an intuitiv dashboad, ffctiv fo obtaining quick insights in tansint and stady-stat bhaviou of htognous DC with any possibl wokload and PM statgis. Futhmo, th AnyLogic nvionmnt nabls to asily xtnd, fin and adapt DC modls to many oth scnaios. Insight is obtaind in th pow and pfomanc in DCs with vaying numb of svs, PM statgis and wokloads. Rlvant mtics a divd fom th qualitativ DC dmands, including pow consumption, spons tim and pow stat utilisation. Ths mtics a stimatd by gathing sampls fom a mixtu of disct-vnt and agnt-basd modls fo IT quipmnt, PM and wokload, implmntd in AnyLogic. Futhmo, a cascad modl nabls th computation of total pow consumption. Ou appoach is illustatd with a 200-sv cas study. A wll known opn-souc toolkit CloudSim [6] allows to simulat cloud computing scnaios and allows to spcify (txtually) DC modls with vitual machins, applications, uss, schduling and povisioning. This tool obtains utilisation, spons tims, xcution tims and ngy consumption mtics fom simulation uns. It is futu wok to to invstigat th capabilitis of CloudSim in compaison to ou AnyLogic-basd simulation modls. Fosn futu xtnsions to th psntd modls a, among oths, (i) th analyss of oth PM statgis,.g., basd on numb of jobs in th systm and on hystsis basd statgis; (ii) ngy-fficincy masus basd on dynamic voltag and fquncy scaling; (iii) pow consumption of scaling wokload; (iv) lag-scal DC stting with htognous svs, and a mixtu of job sizs and int-aival tims; (v) vitualisation; and (vi) thmal-awa DCs. Oth futu wok includs th validation of th modls with actual masumnts fom a DC. Rfncs 1. AnyLogic: AnyLogic: Multimthod Simulation Softwa (2000), anylogic.com/ 2. Agocs, M., Potolani, M.: Data Cnt Fundamntals. Cisco Pss, Indianapolis (2003) 3. Baoso, L.A., Hölzl, U.: Th Datacnt as a Comput: An Intoduction to th Dsign of Wahous-Scal Machins. Synthsis Lctus on Comput Achitctu 8(3), (2009), /10/WSC\_2.4\_Final-Daft.pdf 4. Bik, R., Chn, L.Y., Smini, E.: Data Cnts in th Wild: A Lag Pfomanc Study. Tch. p., IBM Rsach (2012),

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