A generalized model for investigating scheduling schemes in computational clusters

A Gene alized Model o In es iga ing Scheduling Schemes in Compu a ional Clus e s Tien V. Do a,c,∗,Binh T. Vu b, Xuan T. T an a,c , Anh P. Nguyen a aDepa men o Ne wo ked Sys ems and Se ices, Budapes Uni e si y o Technology and Economics, H-1117, Magya ud´osok k¨o ´u ja 2., Budapes , Hunga y. bIn e -Uni e si y Cen e o Telecommunica ions and In o ma ics, Budapes Uni e si y o Technology and Economics, 4028 Deb ecen, Kassai ´u 26., Hunga y cVie nam In e na ional Resea ch Ins i u e o Sciences, Ho Hoan Kiem 1428, Hanoi, Vie nam Abs ac In his pape , we p esen a gene alized model o he pe o mance e alua- ion o scheduling compu e-in ensi e jobs wi h unknown se ice imes in com- pu a ional clus e s. We p opose he applica ion o pa ame e s de ined in he SPECpowe ssj2008 benchma k o he S anda d Pe o mance E alua ion Co po- a ion o cons uc a pe o mance e alua ion model. In addi ion, we also de ine a me hod o ank physical se e s based on ei he he high pe o mance p io i y o he ene gy e iciency p io i y, and measu es o cha ac e ize he pe o mance o compu a ional clus e s. We in es iga e h ee schemes (sepa a e queue, class queue and common queue) o bu e ing jobs in a compu a ional clus e ha is buil om Comme cial O - The-Shel (COTS) se e s. Nume ical esul s show ha he bu e ing schemes do no ha e impac on pe o mance measu es ela ed o he ene gy consump ion o he in es iga ed clus e . Howe e , he bu e ing schemes play an impo an ole in he quali y o se ice pa ame e s such he wai ing ime and he esponse ime expe ienced by a i ing jobs. Fu he mo e, Dynamic Vol age and F equency Scal- ing should be ca e ully applied i one wan s o educe he ene gy consump ion o compu a ional clus e s. Keywo ds: he e ogeneous clus e model, bu e ing scheme, sepa a e queue, class queue, common queue, anking o se e s T. V. Do e al. A Gene alized Model o In es iga ing Scheduling Schemes in Compu a ional Clus e s. Simula ion Modell. P ac ice and Theo y, DOI:10.1016/j.simpa .2013.05.003, 2013 Accep ed o publ. in Simula ion Modell. P ac ice and Theo y 1 In oduc ion The ad ance o high-speed ne wo king and powe ul compu e s oge he wi h he apid decline o ha dwa e cos s led o he widesp ead applica ion o dis- ibu ed sys ems o o e se ices [1,2]. In such compu a ional g id sys ems, job scheduling is mul i-c i e ia in na u e and is a i al ask in s a e-o - he-a esou ce alloca ion s udies. The apid inc ease in he complexi y o compu a- ional clus e s and he numbe o use s has a signi ican impac on he ene gy consump ion, which is o be aken in o accoun in he ope a ion o g id sys- ems. In he li e a u e job alloca ion algo i hms a e p oposed o schedule a i ing jobs in compu a ional clus e s. These algo i hms a e applied dominan ly a wo le els: g id- and clus e -le el. Scheduling policies co e ing and combining bo h o hese le els a e lis ed in [3–5] and e e ences he ein. In addi ion, some algo i hms a e implemen ed wi h ega d o he knowledge abou cha ac e is- ics o jobs. These may belong o ei he clai oyan [6,7] o non-clai oyan algo i hms [8,9]. Nowadays, op imizing ene gy consump ion has become as c ucial as imp o - ing pe o mance [10,11]. Se e al powe managemen (PM) p ac ices can be applied: he on-o echnique comple ely shu s down he idle componen s (e.g., disk, CPU), while Dynamic Vol age and F equency Scaling (DVFS) [12] lowe s he ope a ing ol age/ equency o CPU o educe he ene gy consump ion. I is wo h men ioning ha PM is suppo ed in mode n COTS se e s. Fu - he mo e, he ene gy consump ion o compu a ional clus e s can be educed i he ope a o o compu a ional clus e s sys ema ically exploi s he esou ce he e ogenei y o dis ibu ed sys ems wi h many di e en ypes o p ocesso s o di e en powe cha ac e is ics and pe o mance capaci ies. This was he main mo i a ion o he in es iga ions pe o med by Zikos and Ka a za [13], whe e h ee policies applicable o clus e -le el scheduling we e compa ed: SQEE (Sho es Queue based policy wi h Ene gy E iciency p io i y), SQHP (Sho es Queue based policy wi h High Pe o mance p io i y), and PBP-SQ (Pe o mance-Based P obabilis ic - Sho es Queue). Thei simula ion esul s indica ed ha SQEE is he bes om he aspec o ene gy consump ion, SQHP ou pe o ms he o he wo schemes a he p ice o highe ene gy consump ion, and PBP-SQ p o ed o be he wo s among he h ee schemes. No e ha we ∗Co esponding au ho . Tel.: +36 14632070. Email add ess: do@hi .bme.hu (Tien V. Do). 2 ha e also examined he con igu a ion s udied by Zikos and Ka a za [13] and can con i m he conclusions o [13]. In addi ion, Te zopoulos and Ka a za [3], Gkou ioudi and Ka a za [14] also in es iga ed he scheduling in eal- ime g id sys ems. In his pape , we ollow he same app oach applied in he s udy by Zikos and Ka a za [13], whe e compu e-in ensi e jobs wi h unknown se ice imes a e o be scheduled in a clus e wi h he e ogeneous se e s. Jobs a e execu ed by se e s in a clus e . Compa ed o p e ious wo ks [13,3,14], his pape p o ides a gene alized model based on pa ame e s de ined by S anda d Pe - o mance E alua ion Co po a ion. We also de ine a me hod o ank physical se e s based on ei he high pe o mance p io i y o ene gy e iciency p io i y, and measu es o cha ac e ize he pe o mance o compu a ional clus e s. We in es iga e h ee schemes (Sepa a e Queue, Class Queue and Common Queue) o bu e ing jobs in a compu a ional clus e ha is buil om Comme cial O -The-Shel (COTS) se e s. These p oposals allow a sys ema ic way o in- es iga e he pe o mance o compu a ional clus e s. Simula ion esul s show ha he bu e ing schemes do no ha e impac on pe o mance measu es ela ed o he ene gy consump ion o he in es iga ed clus e , bu a e signi ican ac o s ega ding he wai ing ime and he esponse ime expe ienced by a i ing jobs. In addi ion, we also in es iga e a sa ing on he ene gy consump ion when a speci ic se e is swi ched o i he e is no job alloca ed o he speci ic se e and when a speci ic se e applies DVFS i a job is alloca ed o he se e . Resul s show ha DVFS should be ca e ully applied i one wan s o educe he ene gy consump ion o compu a ional clus e s. The es o he pape is o ganized as ollows. In Sec ion 2, a gene alized model along wi h me hods o ank physical se e s is p oposed. Simula ion esul s a e p esen ed in Sec ion 3. Finally, Sec ion 4 concludes he pape . 2 A Gene alized Model and Scheduling Algo i hms We conside a compu a ional clus e , which se es compu e-in ensi e jobs. Following [13], we assume ha jobs •can be execu ed on any se e , •a e a ended o by he Fi s Come Fi s Se ed (FCFS) se ice policy, •a e non-p eemp ible, which means hey canno be suspended un il comple- ion, •ha e se ice imes unknown o he local schedule . 3 Jobs a e o be execu ed by physical se e s acco ding o a speci ic scheduling algo i hm. In wha ollows, we desc ibe scheduling algo i hms ha alloca e a i ing jobs o a compu a ional clus e . To make he p esen a ion comp e- hensible, he classi ica ion o se e s is p o ided in Sec ion 2.1. Then, he desc ip ion o scheduling algo i hms is gi en in Sec ion 2.2. 2.1 Ranking o Se e s In a compu a ional clus e , each physical se e belongs o a speci ic se e ype. Le Sdeno e he se o se e ypes and K=|S|be he numbe o se e ypes. Se e ype s,s∈S, is cha ac e ized by pa ame e s Cs,Pac,s and Pid,s, whe e Csis he ssj ops alue ( he numbe o ope a ions inished du ing he measu emen in e al di ided by he numbe o seconds de ined o his in e - al, showing he h oughpu –wo kload ope a ions pe second– o his pe iod a 100% a ge load) and Pac,s deno es he a e age ac i e powe measu ed ac- co ding o he SPECpowe ssj2008 benchma k o he S anda d Pe o mance E alua ion Co po a ion (SPEC) a 100% a ge load (99.7% ac ual load). We assume ha when a se e is busy, i unc ions a he ull load wi h he powe consump ion o Pac,s. When a se e is idle, he se e s ops CPU main in e nal clocks ia so wa e and Pid,s is he powe consump ion o a se e in idle s a e. We in oduce wo unc ions as ollows. p(s) = Cs max i∈SCi , s ∈S, (1) e(s) = Cs Pac,s max i∈S Ci Pac,i s∈S. (2) I is wo h emphasizing ha Cs/Pac,s,s∈S, is he pe o mance o powe a io o class s. Le Spand Sedeno e he o de ed se s o se e ypes ha a e anked using unc ion (1) and (2), espec i ely. In anking Sp( he high pe o mance p io i y anking) and Se( he ene gy e iciency p io i y anking), •se e ypes a e anked om 1 o |S|=Kbased on he compu ed alues p(s) and e(s), s∈S, espec i ely; •numbe one is assigned o se e ype a g max s p(s) and a g max s e(s), espec i ely; 4 • ank K, assigned o se e ype a g min s p(s) and a g min s e(s), espec- i ely. Fo anking Sp, i he e a e wo se e ypes wi h he same ssj ops alue, he se e ype o highe a e age ac i e powe ge s a highe index. We o ganize physical se e s based on hei ype. Physical se e s o he same ype o m a class. The classes a e o de ed acco ding o ei he anking Sp (when he high pe o mance p io i y is chosen) o anking Se(when he ene gy e iciency p io i y is p e e ed). Physical se e s a e indexed by pai (i, j), (i= 1 ...,K;j= 1,...,M(i)). No e ha se e (1, j), j= 1,...,M(1), has he highes p io i y and se e (K, j), j= 1,...,M(K), has he lowes p io i y. 2.2 Scheduling The ask o scheduling algo i hms is o alloca e a i ing jobs o physical se e s. Scheduling algo i hms can ake in o accoun se e al ac o s such as he pe o mance, he powe consump ion, he numbe o wai ing jobs. Fu - he mo e, he o ganiza ion o wai ing space o jobs ha a e no immedia ely se ed upon hei a i al is an impo an ques ion. In [13], he au ho s assumed ha an a i ing job will wai in a speci ic physical se e a e he scheduling decision, which is qui e s aigh o wa d om he aspec o implemen a ion. In his pape , we call his queueing solu ion as a sepa a e queue scheme which is illus a ed in Figu e 1. Fu he mo e, we also in es iga e wo u he schemes o bu e ing jobs. •Sepa a e Queue Scheme: an a i ing job will wai in a speci ic physical se e a e he scheduling decision p esen ed in Algo i hm 1. As one obse es, he scheduling algo i hm chooses he se e wi h he sho es queue. I he e a e mo e idle se e s o se e s o he same queue leng h, a se e is selec ed based on he p io i y o i s se e ype. •Class Queue Scheme: he e is a common bu e associa ed o each class (Figu e 2). Jobs scheduled o a speci ic class wai in he bu e o a speci ic class when all se e s in he speci ic class a e busy. When a job depa s om any physical se e in he speci ic class, he i s wai ing job in he bu e o he speci ic class will be immedia ely ou ed o ha se e . This p ocedu e is also pe o med when an a i ing job ha is ou ed o he bu e inds an emp y se e in he speci ic class. Algo i hm 2 ou es an a i ing job based on c i e ia: idle se e s, he p io i y and he sho es queue leng h o classes. •Common Queue Scheme: he e is one bu e o s o ing jobs. On a job a i al, i he LS inds all se e s busy, he job will be s o ed in he common queue and will wai o an idle se e . A job is immedia ely se ed i Algo i hm 3 5 inds an idle se e upon i s a i al. LS . . . . . . . . . . . . . . . . . . . . . } Class 1 . . . . . . . . . . . . . . . . . . . . . . . . } Class 2 . . . . . . . . . . . . . . . . . . . . . } Class K Se e queues Fig. 1. Sepa a e Queue LS . . . . . . . . . } Class 1 . . . . . . . . . . . . } Class 2 . . . . . . . . . } Class K Class queues Fig. 2. Class Queue LS . . . } Class 1 . . . . . . . . . } Class 2 . . . } Class K Common queue Fig. 3. Common Queue 6 Algo i hm 1 The scheduling algo i hm o Sepa a e Queue bes se e ←(1,1) o i= 1 →Kdo o j= 1 →M(i)do i se e (i, j) is FREE hen ⊲ ee se e ound in class i bes se e ←(i, j) GOTO SCHEDULE else i queue leng h o se e (i, j)<queue leng h o se e bes se e hen bes se e ←(i, j) end i end i end o end o SCHEDULE: ROUTE job o queue o se e bes se e Algo i hm 2 The ou ing algo i hm o Class Queue bes class ←1 o i= 1 →Kdo o j= 1 →M(i)do i se e (i, j) is FREE hen ⊲ ee se e ound in class i bes class ←i GOTO SCHEDULE else i queue leng h o class i < queue leng h o class bes class hen bes class ←i end i end i end o end o SCHEDULE: ROUTE job o queue o class bes class 7 Algo i hm 3 The ou ing algo i hm o Common Queue o i= 1 →Kdo o j= 1 →M(i)do i se e (i, j) is FREE hen ⊲ ee se e ound in class i ee se e ←(i, j) GOTO SCHEDULE end i end o end o SCHEDULE: i ound ee se e hen ROUTE job o queue o class ee se e else ROUTE job o Common Queue end i 8 I is wo h emphasizing ha he p ac ical implemen a ion o he Sepa a e Queue Scheme is he easies . Tha is, wai ing jobs can be placed inside each physical se e . Fo example, jobs and pa ame e s can be alloca ed in he local disk o each physical se e . To implemen he Sepa a e Queue Scheme, he Class Queue Scheme and he Common Queue Scheme we p opose a p ac ical me hod as ollows. •A ile se e is ope a ed in a clus e . Files a e accessed using Se e Mes- sage Block/ he Common In e ne File Sys em (SMB/CIFS) p o ocol[15] o Ne wo k File Sys em (NFS) p o ocol [16]. •Each queue is alloca ed a sepa a e spool a ea/di ec o y in he ile se e . The spool a eas a e accessible by he Local Schedule (LS) and he espec i e se e s. •The LS main ains he communica ion wi h he physical se e s in he clus e wi h he help o a G id Compu ing F amewo k ha allows loading and execu ing asks. The LS also has he knowledge on he in o ma ion o he occupancy o each queue and he s a es o physical se e s in he clus e . Based on he knowledge, he communica ion mechanism and he common spool a eas, wai ing jobs can be easily alloca ed o se e s depending on he applied bu e ing app oaches. The alloca ion can be pe o med quickly in he as local ne wo k o he clus e , which minimally a ec s he pe o mance o he clus e . 2.3 Pe o mance Measu es and Ene gy Me ics Le wlbe he wai ing ime in queue o job lbe o e se ice and slbe he se ice ime ha akes he se e o p ocess job l. The mean wai ing ime WT(n) and mean se ice ime o ncomple ed jobs a e calcula ed as ollows: WT(n) = 1 n n X l=1 wl and ST(n) = 1 n n X l=1 sl. Response ime lo job lis he ime pe iod be ween he a i al ins an and he depa u e ins an o job l. We ha e l=wl+sl. The a e age esponse ime RT(n) o njobs ha inished se ice is RT(n) = 1 n n X l=1 l. 9 1300 1400 1500 1600 1700 50% 60% 70% 80% 90% AAE(W.s/job) U Sepa a e-Queue Class-Queue Common-Queue (a) EE policy applied 1300 1400 1500 1600 1700 50% 60% 70% 80% 90% AAE(W.s/job) U Sepa a e-Queue Class-Queue Common-Queue (b) HP policy applied Fig. 10. AEswi ch−o s. sys em load In Figu es 9 and 10, he ene gy consump ion pe job is compa ed when idle se e s a e no swi ched o and a e swi ched o , espec i ely. I can be ob- se ed ha he ene gy consump ion is independen o he scheduling schemes. When idle se e s a e no swi ched o (Figu e 9), he a e age ope a ing ene - gies o bo h policies ha e he g ea es di e ence no iceable a medium sys em load, whe e EE consumes 1769.27 W.s/job and HP 1989.82 W.s/job. The en- e gy consump ion pe job con e ges o 1550 W.s/job as he load inc eases. When idle se e s a e swi ched o (Figu e 10), he HP and EE policies show opposing endencies: su p isingly, wi h HP p io i y, he ene gy consump ion pe job dec eases as sys em load g ows, while AEswi ched−o inc eases wi h EE p io i y. The phenomenon can be explained by he ade-o be ween pe - o mance and ene gy sa ing. A medium sys em load, mos job eques s a e execu ed on se e s ope a ing wi h lowe ene gy cos , hus he ene gy dedi- ca ed o he execu ion o jobs is signi ican ly smalle (1399 W.s/job s. 1649 W.s/job). As demand o jobs g ows, he use o high pe o mance se e s becomes ine i able, since he policies a e based on sho es queue. In con- sequence, he di e ence in AE be ween SQEE and SQHP dec eases a high sys em load (1510 W.s/job s 1535 W.s/job a 90% o u iliza ion). The impac o sa ing ene gy by swi ching o se e s is illus a ed in Figu e 11. I is obse ed ha he sa ing dec eased as he load is inc eased. 3.2.3 DVFS To in es iga e he impac o DVFS, we c ea e a scena io (applying Common Queue bu e ing scheme) whe e p ocesso s educe hei equency and ol age compa ed o he ull powe (a %100 a ge load). In his scena io, he ssj ops alue (co esponds o 70% o he ull capaci y) and he powe consump ion o he se e s applying DVFS when hey execu e jobs a e as ollows: 16 1000 1100 1300 1600 2000 50% 60% 70% 80% 90% Mean sys em ene gy consump ion(W.s/job) U No swi ch-o Swi ch-o (a) EE policy applied 1000 1200 1400 1600 1800 2000 50% 60% 70% 80% 90% Mean sys em ene gy consump ion(W.s/job) U No swi ch-o Swi ch-o (b) HP policy applied Fig. 11. Mean ene gy consump ion s. sys em load •Ace AW2000h-Aw170h F2 (In el Xeon E5-2670): ssj ops is 4517449 and he powe is 1169W. •Ace AW2000h-AW170h F2 (In el Xeon E5-2660): ssj ops is 3706521 and he powe is 881W. •Powe Edge R820 (In el Xeon E5-4650L): ssj ops is 1961157 and he powe is 317W. In Figu es 12 and 13, we plo he a e age esponse ime and he a e age ene gy consump ion pe job s load o he DVFS scena io and he scena io (deno ed as “no DVFS”) wi h he ull p ocessing capaci y. I is wo h emphasizing ha a he same load alue he numbe o a i ing jobs is less in he DVFS scena io han in he scena io a he ull p ocessing capaci y (no e ha DVFS and “no DVFS” also swi ch o idle se e s). The impac o DVFS is qui e d as ic: he inc ease in he a e age esponse ime is much highe han he educ ion in he a e age ene gy consump ion pe job. 1 1.2 1.5 1.8 2.2 2.6 50% 60% 70% 80% 90% Response Time (s) U DVFS+Swi ch-o No DVFS (a) EE policy and Common Queue applied 1 1.2 1.5 1.8 2.2 2.6 50% 60% 70% 80% 90% Response Time (s) U DVFS+Swi ch-o No DVFS (b) HP policy and Common Queue applied Fig. 12. A e age esponse ime s. sys em load In Figu es 14, 15 and 16, we plo esul s when DVFS and “no DVFS” handle he same numbe o jobs by keeping he same a i al a e. As an icipa ed, he p ice o DVFS is he deg ada ion in he quali y o se ice compa ed o a case when p ocesso s unc ion a hei ull p ocessing capaci y, which is 17 800 1000 1200 1400 1600 1800 50% 60% 70% 80% 90% Mean Ene gy Consump ion(W.s/job) U DVFS+Swi ch-o No DVFS (a) EE policy and Common Queue applied 800 1000 1200 1400 1600 1800 50% 60% 70% 80% 90% Mean Ene gy Consump ion(W.s/job) U DVFS+Swi ch-o No DVFS (b) HP policy and Common Queue applied Fig. 13. Mean ene gy consump ion s. sys em load clea ly obse able in Figu e 14. Howe e , he ene gy consump ion o DVFS is highe han he non-DVFS when EE policy and Common Queue a e applied. The impac o DVFS on he ene gy consump ion is only obse ed o he HP policy and Common Queue bu e ing (see Figu e 16). The esul s illus a e ha DVFS should be ca e ully uned i one wan s o apply DVFS o educe he ene gy consump ion o compu a ional clus e s. 0.8 1.1 1.5 1.8 2.2 2.5 9.0 10.8 Response Time (s) λ DVFS+Swi ch-o No DVFS (a) EE policy and Common Queue applied 0.8 1.1 1.5 1.8 2.2 2.5 9.0 10.8 Response Time (s) λ DVFS+Swi ch-o No DVFS (b) HP policy and Common Queue applied Fig. 14. A e age esponse ime s. λ 0 0.2 0.4 0.6 0.8 1 0 5 10 15 20 CDF o RT x (seconds) No DVFS,lambda = 9.0 DVFS+Swi ch-o ,lambda = 9.0 No DVFS,lambda = 10.8 DVFS+Swi ch-o ,lambda = 10.8 (a) EE policy and Common Queue applied 0 0.2 0.4 0.6 0.8 1 0 5 10 15 20 CDF o RT x (seconds) No DVFS,lambda = 9.0 DVFS+Swi ch-o ,lambda = 9.0 No DVFS,lambda = 10.8 DVFS+Swi ch-o ,lambda = 10.8 (b) HP policy and Common Queue applied Fig. 15. CDF o esponse imes 18 1200 1400 1600 1800 2000 9.0 10.8 Mean Ene gy Consump ion(W.s/job) λ DVFS+Swi ch-o No DVFS (a) EE policy and Common Queue applied 1200 1400 1600 1800 2000 9.0 10.8 Mean Ene gy Consump ion(W.s/job) λ DVFS+Swi ch-o No DVFS (b) HP policy and Common Queue applied Fig. 16. Mean ene gy consump ion s. λ 4 Conclusion In his pape we p esen ed a gene alized model o he pe o mance e alua ion o scheduling compu e-in ensi e jobs wi h unknown se ice imes in compu- a ional clus e s. We ga e an implemen a ion o his model on h ee schemes (Sepa a e Queue, Class Queue and Common Queue), di e ing in he way in- coming jobs a e bu e ed, and on wo job-scheduling policies (SQEE, SQHP), p io i izing ei he ene gy e iciency o high pe o mance. We de ined a anking me hodology o physical se e s, which is used o schedule jobs. Nume ical esul s show ha he bu e ing schemes do no a ec he ene gy con- sump ion o he in es iga ed clus e s. Howe e , hey ha e a signi ican impac on he mean esponse ime and mean wai ing ime o incoming jobs. Fu he - mo e, he Common Queue and Class Queue schemes ma kedly ou pe o m he Sepa a e Queue scheme. The e o e, a good bu e ing scheme can esul in im- p o ed o e all clus e pe o mance wi hou inc eased powe consump ion and ene gy cos . Fu he mo e, Dynamic Vol age and F equency Scaling should be ca e ully applied o he pu pose o educing he ene gy consump ion o com- pu a ional clus e s. Acknowledgemen The publica ion was suppo ed by he T´ AMOP-4.2.2.C-11/1/KONV-2012- 0001 p ojec . The p ojec has been suppo ed by he Eu opean Union, co- inanced by he Eu opean Social Fund. 19 Re e ences [1] I. Fos e , “The ana omy o he g id: enabling scalable i ual o ganiza ions,” The In e na ional Jou nal o High Pe o mance Compu ing Applica ions, ol. 15, no. 3, pp. 200–222, 2001. [2] B. Yagoubi and Y. 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