HEROS: Energy-Efficient Load Balancing for Heterogeneous Data Centers

Transcription

1 05 IEEE 8th Intnatinal nfnc n lud mputing HEROS: -Efficint Lad Balancing f Htgnus Data nts Matusz Guzk Univsity f Luxmbug 6, u R. udnhv-kalgi Luxmbug, Luxmbug [email protected] Dzmity Kliazvich Univsity f Luxmbug 6, u R. udnhv-kalgi Luxmbug, Luxmbug [email protected] Pascal Buvy Univsity f Luxmbug 6, u R. udnhv-kalgi Luxmbug, Luxmbug [email protected] Abstact Htgnus achitctus hav bcm m ppula and widspad in th cnt yas with th gwing ppulaity f gnal-pups pcssing n gaphics pcssing units, lw-pw systms n a chip, multi- and manyc achitctus, asymmtic cs, cpcsss, and slidstat divs. Th dsign and managmnt f clud cmputing data-cnts must adapt t ths changs whil tagting bjctivs f impving systm pfmanc, ngy fficincy and liability. This pap psnts HEROS, a nvl lad balancing algithm f ngy-fficint suc allcatin in htgnus systms. HEROS taks int accunt th htgnity f a systm duing th dcisin-making pcss and uss a hlistic psntatin f th systm. As a sult, svs that cntain sucs f multipl typs (cmputing, mmy, stag and ntwking) and hav vaying intnal stuctus f thi cmpnnts can b utilizd m fficintly. Kywds-lud mputing; Data nt; Lad Balancing; Efficincy; Schduling I. INTRODUTION Htgnity is a gwing tnd in distibutd systms, including clud cmputing. Th incasing manufactuing capabilitis cmbind with th nd f high pfmanc and high cmputatinal dnsity sult in gwing divsificatin and spcializatin f th hadwa []. Exampls f ths tnds includ th gwing utilizatin f gnal-pups pcssing n gaphics pcssing unitss (GPGPUs), lw-pw systm n a chips (Ss), multi- and many-c achitctus, asymmtic cs, cpcsss, and slid-stat divs. Evn standadizd sttings, such as data cnts cmpsd f cntains a facing htgnity []. Th pw cnsumptin tnd f lctnic hadwa is n f th asns bhind th gwth f th htgnity. Equipmnt spcializatin incass ngy fficincy, which in its tun quis tchnlgis such as Dynamic Shutdwn (DNS) dak-silicn. In ssnc, thy aim t us th mst fficint hadwa ( its cmpnnts) f th shtst pid f tim. Futh dvlpmnts in th fild f sftwa, ntably vitualizatin, nabl wklad cnslidatin and xplitatin f th lw-pw hadwa stats. lud cmputing, which cmpiss lag pls f sucs accssd via cmmn suc managmnt famwk, facilitats such ptimizatin by cating m pptunitis f agggatin. Vitualizatin can add an additinal dimnsin t th htgnity by th intductin f vaius hypviss, and by cntains which may b ncapsulatd in Vitual Machins (VMs). A hypvis has impact nt nly n pfmanc, but als n ngy-fficincy []. Mdn Infmatin Tchnlgy (IT) systms a bcming stuctually cmplx, with labatd sftwa stacks. T gt th mst ut f ths systms, it is ncssay t pfm ptimizatin that is awa f th undlying chaactistics. In this pap, w psnt a highly scalabl lad balanc, which xplits htgnity in data cnts and is basd n a mathmatical mdling f th systm, which nabls quick, lw cmputatinally-cmplx dcisin making. Th sulting schdul, namd Htgnus fficint Rsuc allcatin Optimizing (HEROS), is validatd using th Gnlud [4] simulat, which cnt xtnsins [5] nabl t simulat htgnus data cnts. F th pups f this study, w ppad additinal cnfiguatins and pw mdls [] that pvid alistic tst scnais. Th btaind sults shw that HEROS achivs stat-f-th-at sults in hmgnus data cnts, whil in htgnus sttings it lads t significant ngy savings (up t 46.4 %) in cmpaisn with lad balancs that a nt awa f htgnity. Th st f th pap is ganizd as fllws: Sctin II dscibs th stat-f-th-at appachs f lad balancing in data cnts. Sctin III psnts th ppsd lad balanc. Sctin IV shws th pfmanc f HEROS in cmpaisn with th fnc algithms. Sctin V summaizs th pap and psnts futu wk dictins. II. LOAD BALANING IN DATA ENTERS Th challng f lad balancing in data cnts is vital and alady xtnsivly psntd in litatu. In this sctin, w viw a slctin f th mst pminnt and psntativ wks. As dmnstatd by ADAPT-POLIY [6], an adaptiv slctin fm a lag st f schduls n-th-fly lads.g. cntain-ngin/ /5 $ IEEE DOI 0.09/LOUD

2 t btt pfmanc than any schdul can achiv n its wn. Ou study cntibuts t this appach by psnting a spcializd schdul that xcls in htgnus sttings. In additin, it has a fatu f adaptability, which allws it t b usd als in hmgnus sttings and t dynamically xtnd if nw sv cnfiguatins typs f sucs a addd t a data cnt. Status [7] is an xampl f an appach wh htgnity btwn data cnts is xplitd. Status ds nt accunt f data cnt intnals. Instad it fcuss n high-lvl chaactistics. On th th hand, auxiliay facts such as cabn missins and cling csts a accuntd. Th Status algithm minimizs th wightd sum f th fllwing bjctivs: cabn missins, lcticity csts and spns tim. Gag t al. pps sval gdy huistics [8] f th multi-clud schduling pblm. Th SMA algithm [9] slvs a simila pblm f assigning wklad t data cnts whil cnsiding nwabl ngy sucs and thmal stag. SMA is basd n Lyapunv ptimizatin tchniqu. Lad balancing in data cnt ntwks can b achivd with VM placmnt algithms [0], []. VM migatin may b th bst chic f ptimizatin f sm applicatins and wkflws, but it has cnsidabl vhads in tms f tim and bandwidth quimnts, spcially in cas f lag systm cnfiguatins. Ou appach is fcusd n shaping th wklad itslf, which is m lastic and can quickly spnd t changs, withut incuing additinal vhads. Liu t al. [] pps a Distibutd Flw Schduling (DFS) f ngy-awa data cnt ntwks. Such appachs d nt tak int accunt th natu f th cmmunicatin sucs and sinks, n th cspnding cmputatin data stag nds. Ou appach is m hlistic, i.. it taks int accunt multipl sucs that a usd duing th data cnts patins. Saha t al. [] psnt a distibutd uting which taks int accunt cmmunicatins, cmputatins and th hat f svs. Th lcal dcisins a basd n a full pw mdl including svs and ntwk tplgy lmnts. Th ppsd lad balancing quis als a discvy phas f all ptntial dstinatin svs and mdling all pssibl futu dcisins, which can caus significant vhads in lag-scal data cnts. Ou appach ffs simila sults with lss vhad. A simpl statgy t dal with in a htgnus stting is t simply slct th mst ngy-fficint sv [4]. Hwv, such gdy appach may b sht-sightd, lading t unvn ntwk lad distibutin. DEERS [5] assigns minimum subst f sucs t th wklad by calculatin f minimum cst f a multi cmmdity flw using th Bnds dcmpsitin. DEERS dmnstats a pfmanc impvmnt cmpad t DENS [6], but it cms at a cst f an incasd untim (f appximatly 0 tims). DEERS is als stictd t hmgnus svs. Th ppsd HEROS appach is inspid and basd n u cnt slutins DENS [6] and -STAB [7]. DENS is usd f th slctin f th bst fit cmputing sucs f a jb xcutin and spcifically dsignd t accunt f th cmmunicatin ptntial f data cnt cmpnnts. Th cmmunicatin ptntial is dfind as th amunt f nd-t-nd bandwidth pvidd t individual svs a gup f svs by th data cnt achitctu. In a thti data cnt tplgy, svs sha uplink channls f thi ack switch. Th cmmunicatin ptntial lis n th acks uplink buff ccupancy, such it acts t th gwing cngstin in acks pds ath than t tansmissin at vaiatins. Th sulting functin favs mpty quus and pnalizs fully ladd quus. An imptant link btwn DENS, -STAB, and DEERS is th fact that thy all bnfit f a fnc implmntatin in th Gnlud simulat, which pvids an unifm xpimntatin platfm. Hwv, nn f ths algithms can adapt wll t htgnus platfms. Th xpinc shws als that n f th limitatins f DENS is its stict binding with th th-ti achitctu, which is cmmnly usd, but has multipl altnativs (.g. Dll, Bub, Finn, DPilla). On th th hand, th -STAB algithm is a tw stp-pcdu that quis nlin knwldg abut th full data cnt ntwk utilizatin. As a sult, th lad is btt distibutd amng acks than in th cas f DENS. Th sv slctin functin f ths schduls has als diffnt shaps, sulting in ppsing bhavis: whil DENS pmts DNS, -STAB favs lw utilizatin and thus pvnts cnslidatin. T cnclud, w pps HEROS, which cmbins th bst fatus f DENS and -STAB and cntibuts with a nvl htgnity-awa dcisin making appach. It tlats any standad ntwk tplgy, as it pats n th ack lvl. Still, th ntwk lad is balancd amng multipl acks similaly t -STAB. T maximally duc ngycnsumptin, sv slctin functin pmts DNS, but pvnts t high utilizatin lvls. Th dcisin making is basd n agggatd infmatin and is chaactizd by a lw cmputatinal cmplxity. III. HEROS ADVANED HETEROGENEOUS SHEDULER W cnsid th pblm f task (us qust) schduling n distibutd cmputing infastuctus. a allcatd t svs, ith vitualizd physical. hav multipl cmpnnts, which a gupd by suc typ. Each cmpnnt is futh dscibd by a vct f numbs, calld capacitis, which quantitativly psnts thi capabilitis. An xampl f sv in this psntatin is psntd in Figu. Ou pvius studis psnt hw t div th ngy-fficincy paamts f a mdl that 74

3 nabls such spcificatin [], tgth with th implmntatin f th mdl in th Gnlud simulat [5]. mputing Nd Vitual Machin lud Applicatin Sv Nd VM Task Rsuc Typs Rsuc Supplis Rsuc apacitis mputing Mmy Stag Ntwking Intl Xn X40,.4 GHz, 8M ach, Tub Figu. 4 4GB Mmy (xgb), MHz Singl Rankd UDIMM 4 GB 500GB 7.K RPM SATA.5in N Raid 500 GB 0 GB SSD SATA N Raid 0 GB Badcm 5709 Dual Pt GbE NI w/toe issi, PI-4 Gbps Exampl f a sv with its cmpnnts. Gbps a indivisibl units f wk and thy a dscibd using th sam mdl as svs, nabling cmpsd allcatins as psntd in Figu. In pactic, ach task is dscibd by th fllwing mandaty paamts: input and utput cmmunicatins vlums, and th numb f PU instuctins t b xcutd. Ths paamts impact utilizatin f ntwking and cmputing sucs, spctivly. It is pssibl that a task quis additinal sucs,.g. stag n lcal div physical mmy. Dsciptins f tasks can b als fully htgnus, including sval quimnts f th sam typ f suc. Th ngy-fficint schduling has tw cntadicty bjctivs: cnsumd ngy and man spns tim ( man flwtim). Th minimizatin f th ngy cnsumptin is achivd by cnslidatin f th lad and putting idl svs t th slp stat. Th ngy cnsumptin is dfind as th ttal ngy cnsumd by th svs. Rspns tim is dfind as th tim diffnc btwn th catin f a task and th nd f its utput cmmunicatin. Th minimizatin f th spns tim is patd by a wid distibutin f th wklad, which minimizs th spns tim. Th intllignt, ngy-fficint schduling cmbins bth f ths chaactistics. Th psntd slutin f th ptimizatin pblm is calld Htgnus -fficint Rsuc allcatin Optimizing (HEROS). Th HEROS mthdlgy is basd n DENS [6] and -STAB [7], and backwad cmpatibl. It indd lis n a simila appach f stablishing sv slctin and cmmunicatin ptntial functins. Similaly t DENS, HEROS allcats tasks t th sv with maximum sc. Th sc is calculatd by a dcisin functin, which has tw main cmpnnts: th sv slctin functin and th cmmunicatin ptntial functin. Th nvl sv slctin functin is basd n th fact, that th ang, dmain, and shap f th pw cnsumptin m p u t i n g M m y S t a g N t w k i n g VM VM Task Task Task 4 Task 5 Task 6 Figu. Rsuc allcatin n a nd with htgnus sucs. ls psnt clud applicatins and VM typs, which must b cmpatibl. functins f htgnus hadwa may vay significantly. Fig. psnts vaius pw functins f th typs f htgnus cmputing nds: a cmmdity sv, which is th last fficint with a cncav pw functin, a high pfmanc cmputing (HP) sv with th highst pfmanc and a cnvx pw functin, and finally a highly fficint, yt lw pw mic sv with a lina pw functin. In th litatu, shap cnsidd f th pw functins is ftn dpndnt n th assumptins. mplmntay Mtal-Oxid Smicnduct (MOS) tchnlgy suggst a cnvx latin if Dynamic Vltag Fquncy Scaling (DVFS) is usd [8]. Th xpimntal studis shw that th pw functin is in ality lina [], [9] vn slightly cncav [0]. Bcaus f ths divgncs and th fact that futu gnatins f hadwa may b m ngypptinal [], w pps a gnal appach, which can ncmpass all f ths cass. Pfmanc p Watt (PpW) mtic [0] is usd t undlin ngy fficincy and can b dictly usd t slct th mst ngy fficint sv. Th PpW functin f sv s is dfind as: PpW s (l) =Pf s (l)/p s (l), () wh Pf s (l) is th pfmanc functin (.g. pfmanc in MIPS at lad l), and P s (l) is th pw cnsumptin functin. Du t th htgnity, it is ncssay t xpss l at 744

4 mmdity HP Mic mmdity HP Mic Pw [W] H(l) MIPS Figu. Pw functins f htgnus cmputing svs MIPS Figu 4. Slctin functins f htgnus cmputing svs. th sam scal f all svs, using a standad unit lvant f a schduld applicatin,.g. MIPS a numb f pcssd qusts p scnd. A pactical dawback f th staightfwad usag f PpW is th fact, that svs bcm th mst ngy-fficint whn fully ladd, which in pactic can asily lad t vlading and dastic ductin f pfmanc and ngy fficincy. T pvnt that, th HEROS sv slctin functin is dfind as: H s (l) =PpW s (l) ( γ ), () + α max (l β max ls)) ls wh max l s is th maximum sv lad. Th dmain, pssibl ang f valus f l s, is dfind as L s := [0; max l s ]. Th scnd tm f th slctin functin is a sigmid scald t th dmain L s and th ang f PpW s (l). Th sigmid aim is t cunt th impact f th PpW functin f high valus f lad. Th cfficint α dtmins shapnss f th dscnding slp, whil β is basd n th maximum accptabl lad f th sv. In pactic, ths vaiabls a st t α = 0, β = 0.9, and γ =., t assu a smth dgadatin f th slctin functin stating fm 90% f maximum lad. Fig. 4 psnts sv slctin functins f th th svs, wh thinn lins psnt th valus f PpW functins withut subtactin f th sigmid. Th cmmunicatin ptntial Q(u) is basd n th DENS cmmunicatin ptntial, but instad f quu buff siz, it uss actual link lad, and is dfind as fllws: u Q(u) = ( Umax ), () wh u is a cunt link lad and U max is th maximum link lad. This functin has nly n cmpnnt, i.. th cspnding tp-f-th-ack cmmunicatin ptntial, which maks HEROS applicabl t tplgis th than th-ti. Th cmmunicatin ptntial is illustatd in Figu 5. Th final dcisin functin is btaind by multiplicatin f th sv slctin functin and th cmmunicatin Q(u) Link lad, u/u max ptntial functin: Figu 5. mmunicatin ptntial. F s (l, u) =H s (l) Q s (u). (4) Fig. 6 psnts th dcisin functin f th th psntd sv typs. Th sv chsn t xcut a task is th n with th highst dcisin functin valu. In cas f a ti, th sv is chsn andmly amng th ns fatuing th bst valu. In cas f idl svs, th maximal PpW is multiplid by th cmmunicatin ptntial, t mak a balancd chic btwn ptntial ngy savings and balancing wklad amng acks. Givn that th data agggatin phas is pfmd n ach cmputing nd spaatly, th dcisin making shws littl cmplxity. In this pap, th cmplxity f algithm is O(n) in cas f scanning a list f machins in d t find th bst plac. Sting th list may futh facilitat th slctin pcdu. IV. EXPERIMENTS A. Gnlud Simulat Gnlud [4] is a wll-knwn simulatin tl which ffs fin-gaind simulatin f mdn clud cmputing 745

5 F(l,q) mmdity HP Mic MIPS Figu 6. HEROS dcisin functin Link lad nvinmnts fcusing n data cnt cmmunicatins and ngy fficincy. Gnlud is basd n ns- [] simulatin platfm. It fatus a dtaild mdling f th ngy cnsumd by th lmnts f th data cnt, such as cmputing svs, switchs, and ntwk links. It als implmnts a st f ngy fficint mtics []. Gnlud suppts taditinal th-ti data cnt achitctu as wll as mdn data cnt achitctus, such as Dll, Bub, Finn, and DPilla. Th th-ti achitctu, usd in this study, cnsists f th tpmst c ti, th agggatin ti that is spnsibl f uting, and th accss ti that hlds th pl f cmputing svs aangd int acks. An imptant dawback f such tplgy is ptntial vsubsciptin. Th Gnlud simulat was xtndd with functinalitis ncssay t mdl htgnus svs [5], [] t nabl th implmntatin f th HEROS schdul. B. Rsults f Simulatins Th ffctivity f th HEROS algithms is validatd using fnc algithms and a st f bnchmaks. Th fist th slctd algithms: Rund Rbin (RR), Randm, and Gn, a standad fnc schduls implmntd in th Gnlud simulat. Th fist tw schduls mak unifmd dcisins, ith cyclically allcating tasks t machins (RR) slcting a machin fm a andm distibutin (Randm), which is unifm by dfault. Th latt schdul maks a gdy cnslidatin f th lad: it lks f th fist Rsuc Pvid in th input list that can succssfully finish a task. Bcaus f that, it nds infmatin abut th cunt lad f RsucPvids. Anth slctd algithm is th fnc DENS algithm [6], discussd in Sctin II. Th simulatin scnais f validating HEROS a slctd t tst vaius cnditins. Ths bnchmak scnais a psntd in Tabl I. Siz is th fist attibut f ach scnai, whil htgnity is th scnd attibut. Mv, small siz scnais hav lag vsubsciptin f th links, including 48 svs in ach f th acks, in cmpaisn t hsts p ack in th Full-scal siz scnais. Th simulatd spcificatins f svs a psntd in Tabl II. Th svs hav bth DVFS and DNS mchanisms nabld, and thi pw mdls a lina, dfind by th minimum and maximum pws. In ach scnai, th data cnt lad is st t 0% f th ttal data cnt pw capacity. Th simulatin tim is st t 60s and th data cnt is mpty in th bginning. hav 00,000M instuctins, 8,500B f input data and 50KB f utput data, and ngligibl nds f mmy. Th intnal svs dadlin f task xcutin is st t.s. Bcaus f th lag numb f Mic svs with lw cmputatinal capacitis, th htgnus scnais gnat lss tasks than thi hmgnus cuntpats, cmpsd f cmmdity svs. Tabl I TESTED REFERENE THREE-TIER ONFIGURATIONS Small Ht. nfiguatin Small Fullscal Fullscal Ht. Switchs 8 8 Agggatin Switchs 6 6 Accss Switchs in a Rack Ttal mmdity HP Mic Avg. Submittd Simulatin Tim 60 s Tagt Systm Lad 0 % Tabl II SIMULATED SERVERS SPEIFIATIONS Sv mmdity HP Mic # MIPS/ Ttal MIPS Max Pw (W) Min Pw (W) Had disk Ys Ys N F ach cnfiguatin, 50 indpndnt uns with diffnt psud-andm numb gnat sds a pfmd p schdul. F ach schdul, th sam st f sds is usd, t nsu idntical task gnatin ats. Th man valus sving as quality indicats a psntd in Tabls III VI, whil th cspnding lativ valus a psntd in Figus 7 0. Each lativ valu is cmputd as th ati f th valu f an bjctiv f a schdul, t th maximum valu f th bjctiv amng all schduls in th scnai. Th main bjctivs f cmpaisn f th schduls a sv ngy cnsumptin and man spns tim. Bth f ths bjctivs shuld b minimizd. Additinally, it is bnficial if th spns tim standad dviatin is lw. 746

6 Anth quimnt is th succss at, which is dtmind by th numb f task failus n svs (i.. svs dtct that th tasks cannt b finishd bf dadlin, s thy dp thm), and th numb f unfinishd tasks (i.. tasks which d nt xit data cnt, which is th sum f th faild tasks and tasks that did nt finish snding thi utput cmmunicatin bf th nd f th simulatin). All psntd sults w tstd using statistical tsts. Th Shapi-Wilk tst [4] tund in many cass p- valus small than 0.05, which mans that th sults w nt nmally distibutd. T cmpnsat this ppty, th Wilcxn signd-ank tst [5] was applid t ach pai f th algithms f ach bjctiv. Th p-valus f th tsts w always small than 0.00, which givs stng statistical significanc than th standad thshld f Th fist cnsidd scnai is th small, hmgnus tplgy. In this scnai th a m than thusands tasks. As psntd in Tabl III, mst f th schduls succssfully schduld all tasks, with a singl xcptin. Th Gn schdul sults a nt accptabl, as m than 0% f th tasks a unfinishd. It is du t th gdy allcatin plicy, which lcats th wklad n a subst f svs that blng t th sam ack. As a sult, th task cmmunicatin utput f tasks allcatd t ths svs cannt lav th data cnt. Th lativ valus f this scnai a gaphically psntd in Fig. 7. Th HEROS schdul has th bst man spns tim and ttal ngy cnsumptin, fllwd by DENS. It pvs that th HEROS sv slctin functin may vn utpfm th n f DENS, which is dsignd f hmgnus data cnts. Th Randm algithm has ws pfmanc than RR. Bth RR and Randm hav lag ngy cnsumptin, as thy pvnt nting svs int slp md, and hav als ws spns tim than cmmunicatin-awa algithms DENS and HEROS. Th vy gd ngy sc f th Gn schdul is discditd by its lag spns tim and a lag numb f unfinishd tasks. Tabl III SMALL HOMOGENEOUS TOPOLOGY RESULTS Ttal Man Rspns Tim [s] Rspns Tim sd [s] Failus # # HEROS DENS Gn RR Randm Th sults f Full-scal hmgnus scnai a psntd in Tabl IV and Fig. 8. Th Gn schdul ds nt lav unfinishd tasks, as th vsubsciptin f links is small in this cas. Th bhavi f th Randm schdul lads t vlading sm f th svs in th lag tplgy and cnsqunt tasks failus. Th st f Rlativ Valu Ttal Figu 7. Man Rspns Rspns Tim Tim sd HEROS DENS Gn RundRbin Randm Small hmgnus tplgy lativ sults schduls hav accptabl sults. Th mst ngy-fficint algithm is DENS, fllwd by Gn and HEROS. Th man spns tim is th bst f HEROS, clsly fllwd by DENS. Th gd pfmanc f HEROS is undlind by th vy lw standad dviatin f th spns tim. Tabl IV FULL-SALE HOMOGENEOUS TOPOLOGY RESULTS Ttal Man Rspns Tim [s] Rspns Tim sd [s] Failus # # HEROS DENS Gn RR Randm Rlativ Valu Ttal Figu 8. Man Rspns Rspns Tim Tim sd HEROS DENS Gn RundRbin Randm Full-scal hmgnus tplgy lativ sults Th sults f small htgnus scnai a psntd in Tabl V and Fig. 9. Bcaus f th htgnity, th schduls that d nt vify th fasibility f thi allcatins (RR and Randm) caus failu n svs f m than a half f th tasks. In th htgnus scnais data cnts hav fw cmputatinal sucs in cmpaisn with th hmgnus scnais, whil th ntwking tplgy is th sam, s th is n cngstin in th ntwk. Amng ths schduls, Gn supisingly has th bst spns tim and th wst ngy cnsumptin, which is xplaind by th fact that it squntially chss svs fm a list that stats with th cmmdity svs, which a lativly 747

7 fast, but nt ngy-fficint. Th HEROS schdul xplits htgnity and its allcatins cnsums 9% lss svs ngy than in cas f th Gn schdul. Th small dgadatin f man spns tim f HEROS is causd by th usag f lss pfmant mic svs. DENS psnts a bhavi btwn Gn and HEROS, hwv its spns tim standad dviatin is high than f th th tw schduls. Tabl V SMALL HETEROGENEOUS TOPOLOGY RESULTS Rlativ Valu Ttal Man Rspns Rspns Tim Tim sd HEROS DENS Gn RundRbin Randm Ttal Man Rspns Tim [s] Rspns Tim sd [s] Failus # # HEROS DENS Gn RR Randm Rlativ Valu Ttal Figu 9. Man Rspns Rspns Tim Tim sd HEROS DENS Gn RundRbin Randm Small htgnus tplgy lativ sults Th sults f full-scal htgnus scnai a psntd in Tabl VI and Fig. 0. Mst f th sults in this stting a simila t th small htgnus scnai. Th HEROS schdul ffctivity is btt, cnsuming 47% lss svs ngy than th Gn schdul. Th sults f DENS a cls t th sults f Gn f ngy cnsumptin. Finally, th DENS schdul has th bst man spns tim, whil spns tims f Gn and HEROS a th sam. Tabl VI FULL-SALE HETEROGENEOUS TOPOLOGY RESULTS Ttal Man Rspns Tim [s] Rspns Tim sd [s] Failus # # HEROS DENS Gn RR Randm V. ONLUSION Th nvl HEROS schdul is an xtnsin f th statf-th-at ntwk- and ngy-awa schduls. HEROS is Figu 0. Full-scal htgnus tplgy lativ sults spcifically dsignd t pat in htgnus systms. It bass its dcisins n th agggatin f utilizatin and instantanus PpW f svs with th utilizatin f ntwk links. HEROS is implmntd in th Gnlud simulat, pving its ffctivity in cmpaisn with th fnc schduling appachs in hmgnus and htgnus systms, wh it savs up t 47% f svs ngy. Th dcisin functin f HEROS ffctivly simplifis cmplx dsciptin f htgnus svs. It als nmalizs capacitis and pw functins f svs, making th schdul xtnsibl and adaptiv t nw sttings. As a sult, HEROS pfms als wll in hmgnus cass. Additinally, nw typs f svs can b dynamically addd at untim, which nly quis simpl calculatin f thi dcisin functins. Th xact dcisin-making mchanism culd b futh labatd. In this pap, th cmplxity f algithm is O(n), in cas f scanning all list f machins in d t find th bst plac. Th futu wk will tst wightd und bin algithm appach, which wuld duc cmplxity t O() in cas scs a usd t pidically updat wights. M labatd schms may includ a distibutd ganizatin, ptimizd t minimiz ntwk taffic whil pviding th quid infmatin. Futu dictins includ pfming cmphnsiv xpimntatin, with nn-unifm task siz and task gnatin pattns, and simulatins f m cmplx, vitualizd, multi-tnant nvinmnts. HEROS culd b impvd by xtnsin f th st f ptimizd bjctivs, intgatin f th data sucs, and distibutin f HEROS using a multi-agnt famwk t nabl cpatin and xchang f infmatin by schduls in a singl data cnt, vn btwn multipl clud cmputing systms. HEROS culd als hlp in slving th, latd pblms,.g. ngy-fficint wkflw schduling [6]. In this cas, th cmbinatin f ntwk-awa mdls [7] with th dcisin functin f HEROS culd nabl achivmnt f scalabl and dynamic wkflw allcatin in clud systms. 748

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