A Hadoop Job Scheduling Model Based on Uncategorized Slot


 Sharleen Mathews
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1 Journl of Communctons Vol. 10, No. 10, October 2015 A Hdoop Job Schedulng Model Bsed on Unctegored Slot To Xue nd Tngtng L Deprtment of Computer Scence, X n Polytechnc Unversty, X n , Chn Eml: Abstrct As Job schedulng s becomng n mportnt prt of Hdoop frmework t present, job schedulng model bsed on unctegored ws reserched on ths pper. It could elmnte the lmtton of Job Tsk type nd ddn t dstngush between Mp nd Reduce ny more, however there ws only one type of left whch could be ssgned to execute the Mp tsks nd to run the Reduce tsks. By doptng Reduce dynmc prttonng, t cn rele swtchng smoothly the between two types of tsks, menwhle, compred wth the FIFO lgorthm whch need dstngush the type of, the expermentl result shows tht the model not only mproves the resources utlton nd betters lod blncng, but lso enhnces the prllelsm of tsks nd shortens the executon tme of the Job Tsks. Index Terms Dynmc prttonng lgorthms, Hdoop, job schedulng lgorthms, MpReduce I. INTRODUCTION Wth the hghspeed development of the Internet nd the rpd growth of customer number, the mount of dt growth rte lso presents exponentl growth, whch mkes Bg Dt become the most populr serch term currently. Fcng such lrge mount of dt, the trdtonl pproch cnnot hndle t wth the d of exstng tools or technques. Along wth the rpd development of softwre technology, dstrbuted pltforms hve sprung up. In numerous dstrbuted processng pltforms, Apche Hdoop [1], due to the powerful dstrbuted computng frmework of MpReduce, s by fr the most ctve nd wdely used pltform. By doptng the de of dvde nd conquer, MpReduce s knd of low cost nd hgh effcency pproch. It connects lrge number of lowcost PC ech other formed cluster to del wth mss dt storge nd nlyss. In nutshell, the trdtonl processng s replced by dstrbuted processng n the feld of bg dt processng. Hdoop pltform hdes the underlyng mplementton detls for developers. In ths pltform, you only need to wrte the Mp nd the Reduce functons ccordng to the Mnuscrpt receved My 24, 2015; revsed September 23, Ths work ws supported by the Chn's Ntonl Development nd Reform Commsson (NDRC) nd Hghtech ndustrlton projects (Shnx NDRC [2009] no. 1365); X'n Polytechnc Unversty doctorl reserch strtup fund (BS0725). Correspondng uthor eml: do: /jcm demnd, whch gretly smplfes the development of dstrbuted pplctons. As result, mny cloud opertors begn usng Hdoop, whch mkes the number of users nd ctve frequency constntly rsng; therefore the performnce of Hdoop pltform promoton s more nd more mportnt. In the study of Hdoop, job schedulng s lwys key pont of the reserch. Ths rtcle puts forwrd new knd of Hdoop job schedulng model. The remnder of ths pper s orgned s follows: Secton II revews the relted work. Secton III descrbes the lgorthm nd mplementton of job schedule model bsed on unctegored. Experment nd performnce nlyss re studed n Secton IV. Secton V, concludes the work of ths pper nd outlnes the future work. II. RELATED WORK In order to smplfy the resources llocton, Hdoop dopts the model bsed on the resources. Slot s the bsc unt of the msson n the MpReduce, whch s from the multdmensonl resources (CPU, MM, IO, etc.) to one dmensonl n TskTrcker node. Although there hs been mny schedulng lgorthms, none hve unfed resource llocton model. When the Mp tsks run, t tkes up the Mp. Whle the Reduce tsks execute, t occupy the Reduce. In other words, the two dfferent types of occupy ther own resources respectvely nd no shrng consderton. On TskTrcker node confgurton, ech node hs two Mp s nd one Reduce by defult, nd the Mp s only responsble for the Mp tsks nd the Reduce s only responsble for executng the Reduce tsk, whch lrgely decrese the tsk prllelsm nd resources utlton. Hdoop pltform possesses three lgorthms of schedulng, FIFO Scheduler [2], Fr Scheduler [3] nd Cpcty Scheduler [4]. The defult schedulng lgorthm s FIFO n ths pltform. In ddton, numerous of schedulng lgorthms re comng nto beng. For exmple, Dely Scheduler [5], whch s bsed on enhncng dt loclty; Dynmc Proportonl Scheduler [6], whch s bsed on user preferences nd chngng the wy of tsk llocton proporton nd dynmc prorty; ConstrntBsed Scheduler [7], whch s bsed on dedlne of Reltme; [8] proposed schedulng lgorthm whch consders when the tsk scheduler cn't choose the Dtlocl tsk whether t llows to ssgn the Nonlocl tsk or not; [9] proposed schedulng lgorthm 2015 Journl of Communctons 778
2 Journl of Communctons Vol. 10, No. 10, October 2015 whch s bsed on the number of Mp tsk node nd dt sheet replcton mode; LATE [10] (Longest Approxmte Tme to End) nd SAMR [11] (Selfdptve MpReduce Schedulng Algorthm), whch re bsed on under heterogeneous envronment how to mprove the schedulng effcency; [12] nd [13] proposed schedulng lgorthm whch re bsed on job types clssfyng nd the lod dynmc of heterogeneous; [14] proposed schedulng lgorthm whch s bsed on the dptve node cpcty; [15] proposed schedulng lgorthm whch s bsed on mtchng rules; beyond bove, the scheduler bsed on the ntellgent lgorthm nd smulted nnelng lgorthm [16] nd rtfcl fsh lgorthm [17] nd genetc lgorthm [18]; etc. however ll of these schedulng lgorthm don t consder from the resources llocton model, ther tsk schedulng strtegy s to dstngush the type. Ths pper presents schedulng model bsed on unctegored, usng only one type of resource. The cn be ether ssgned to run the Mp tsk or the Reduce tsk, mprovng the utlton rte of nd tsks prllelsm nd resources utlton. III. SCHEDULING MODEL BASED ON UNCATEGORIZED SLOT In the schedulng model bsed on unctegored, resources model expressed by one knd of, whch cn run both of the Mp tsks nd Reduce tsk, specfc llocte wht knd of ccordng to the executon of job progress nd the tsk scheduler s shown n Fg. 1. The key problem of schedulng model bsed on unctegored s how to mke the n smooth swtchng between two dfferent types of tsks. Becuse the demnd resources for the Mp tsks nd the Reduce tsks re dfferent, so we need to desgn the unfed resources representton model. Accordng to the bove resons we need to do followng four steps: 1) schedulng model relted defntons; 2) resources model representton; 3) dynmc prttonng; 4) schedulng lgorthm mplementton. TskTrcker 1 tsk tsk JobTrcker tsk Fg. 1. Tsk llocton model bsed on unctegored. A. Schedulng Model Relted Defntons TskTrcker 2 Becuse of the Reduce tsk nput source dt s from the Mp phse output dt, so the Reduce phse hs dt dependences wth the Mp phse. When the Mp tsk process s completed, the dt buffer storge to the locl dsk to next sortng work. In the schedulng model to dstngush the type, R s sttc vlue nd represents the number of Reduce s. The prtton functon dvded the dt nto R peces of dt blocks then R represents the number of Reduce Tsks nd ech Reduce tsk shufflng mddle results of dt submtted to the correspondng Reduce functon. The MpReduce dt flow s shown n Fg. 2. Fg. 2. MpReduce dt flow. In the unctegored model, the ntl vlue of R s 1. Becuse the vlue of R wll chnge long wth the number of s ssgned to the Reduce tsk, ths wll chnge the drecton of dt flow whch ws generted by the Mp tsk. In order to blnce ech drecton of dt flow nd dt skew stuton doesn t pper, so we need to get resources nformton nd dt through Gngl n job runtme. Gngl s n open source cluster montorng softwre, whch cn montor nd dsply vrous knds of sttus nformton n the cluster nodes, such s CPU, Memory, hrd dsk utlton, I/O worklod, Network trffc stuton, etc. Becuse Gngl does lttle to the consumpton of computng resources nd whch s fully comptble wth Hdoop, so deploy RRDTool nd gmetd 2015 Journl of Communctons 779
3 Journl of Communctons Vol. 10, No. 10, October 2015 whch summry of cluster resources on the JobTrcker node nd deploy gmond whch collecton of ech node resources on the TskTrcker node s very convenent. We cn defne the concept of the rte rto of Mp nd Reduce from the nformton collected bove. Gettng the S m whch s totl nternl dt output per unt tme n the Mp phse nd the S r whch s the mount of dt processed per unt tme n Reduce phse, so s the speed rto. Prmeter s the key determnnts of the whether to execute the Mp tsks or the Reduce tsks. s r (1) In cse the ntermedte dt to be covered by runnng too mny Mp tsks or processng speed too slow by runnng too mny Reduce tsks, we hve to defne two vrble prmeters, whch re the rnge of, LowLmt (LL) [19] nd UpperLmt (UL), through the two prmeters to djust the number of whch execute dfferent types of tsks. B. Slot Resources Model Representton In the process of tsk executon, not only the Mp phse nd the Reduce phse demnd for dfferent resources, but lso the chld n the process demnds for dfferent resources too t sme tme. Such s, copy the block of dt mnly usng the network I/O nd dsk I/O resources t the begnnng of the Reduce phse nd n the process of the shuffle. Then the CPU s the mn use of resources n the process of the Reduce phse. So we cn control the resources by chngng vrous prmeters. In the followng three equtons, set R represents the model of the wth fve dmensons group resources, represents the elements n the resources model, represents correspondng element weght vlue of resources. s R { CPU, MM, Dsk, IO, DskIO } (2) 5 1 m (3) 5 1 (4) 1 s vlue wll dynmc chnge n the dfferent phse of job executon long wth the hstorcl sttstcs of JobTrcker nd the current mplementton. Here ntroduce Dynmc Stblty Control (DSC) [19], whch djust the number of TskTrcker s ccordng to the node lod nd the vlue dynmclly. C. Reduce Dynmc Prttonng In the sme job, the dt dependences between Mp tsks nd Reduce tsks mnly dsplys n the mddle of the block dt block replcton. The Reduce tsk cn be scheduled fter the completon of certn percentge of the Mp tsks, so n unctegored model tsk executon s stll dvded nto Mp phse nd Reduce phse. When hs been processed the Mp tsk then request tsk scheduler ssgned new tsk, the scheduler llocte the to Mp tsk or Reduce tsk ccordng to the work of mplementton. If the node llocted Reduce tsk, the went nto the Reduce phse, whch drectly cuse the R vlue nd the drecton of ntermedte dt flow chnge. For ech tsk, the frst step s smplng {key, vlue} of the dt nd estblshng n ndex tree Btree, s shown n Fg. 3. Not lef node represents the key "bytepth", whle ech lef node represents the former two key "bytespth", nd the rnge of the prtton number s sved n the lef node, t the men tme, the sme prefx key ws ssgned to the sme lef node. lef node Fg. 3. A prtton ndex tree. / A B C A B C BA BB BC For key, the process of prtton: 1) judge the frst byte of the key, then fnd the not lef node of frst lyer; 2) ccordng to the second byte of the key, ech lef node my correspond to multple smplng (.e., multple r), then compre wth ech smples one by one nd determne whch r cn be ssgned to. In the cse of not dstngush the, the ntl vlue of R s 1, nd ts vlue wll chnge long wth the number of Reduce tsks. Here s the desgn of dynmc Hsh functon H(R): H( key) key MOD R (5) The vlue of R s synchronous, whch cn chnge wth the number of dstrbuton to the Reduce tsk, so the number of prttons nd rnge wll chnge ccordngly. The whole model s dvded nto four stges: ntl, splt, merge nd recovery stge. The ntl stge: smplng the dt n the process of Mp tsk executon, nd ccordng to the smple pont estblsh Bndex tree. After certn percentge of the Mp tsk, frmework utomtclly ssgned to the Reduce tsk. Due to the ntl vlue of R s 1, so there s only one executng Reduce tsk, n other words, ll the smplng re ped to the ntervl {, } of ths r, s shown n Fg. 4: Fg. 4. The ntl prtton. prtton 2015 Journl of Communctons 780
4 Journl of Communctons Vol. 10, No. 10, October 2015 The splt phse: wth the development of tsk executon, the mount of dt hve ncresed n the Mp phse nd the ccumulton of ntermedte dt, forcng us to dd R x Reduces, t ths tme R x s wll be selected to execute Reduce tsks. In order to cheve the copy of the ntermedte dt block, the scheduler wll dd R x smple ponts nd then splt the orgnl prtton. The selecton of smplng ponts s on the bss of the sorted mddle dt nd then dvded nto R x eqully, thus tppng pont s the new smple pont. As shown n Fg. 5, three new smple ponts d, h, t ws dded, nd the orgnl ntervl ws dvded nto four prts {, d}, {d, h}, {h, t}, {t, }, s result ech ntervl correspondng pece of ntermedte dt. Strtng Reduce tsk tkes up, t the men tme, copy the Mp results n the correspondng secton of the dt, n ths wy scheduler swtches prt of Mp tsks to the Reduce tsk. fnshng operton of Reduce phse. D. Implementton As schedulng model hs been defned n the prevous, the lst problem s the tmng of strtng the Reduce tsk. Due to the smlrty of dt n the sme job, so tht we cn ssess the rest tsks by the resources whch hve been operted the mssons. Usng the celng LL nd lower lmt UL of prmeter, f lrge thn UL, for the current llocton tsk, f lower thn LL, for the current llocton tsk, somewhere n between, contnued n ccordnce wth the dstrbuton of tsk type before. Flowchrt s shown n Fg. 8. d h t r/m prtton >UL [LL,UL] <LL Fg. 5. Dvde prtton. contnue Fg. 8. flowchrt of entrety Tsk llocton. Fg. 6. Prtton merge. prtton 1 h prtton t prtton 4 The merge phse: when the Reduce phse process to be fnshed or lrger thn the UL, wll be ssgned to Mp tsk or other Reduce tsk, result n the current operton of wll ccordngly, so we need to decrese the smplng ponts nd merge ntervl dt. As shown n Fg. 6, when removng the smple pont d, reducng R to 3, mergng ntervl {, d} nd {d, h} to ntervl {, h}, correspondng wll ped to new ntervl perod of {, h} block of dt, these led to to be relesed the request of other tsks. Fg. 7. Prtton recovery. prtton The recovery phse: s the lst job executon s completed, smple pont s d to ero, nd R s reset to the ntl vlue 1, so ll smll ntervls merge nto bg ntervl, s shown n Fg. 7, the wll del wth the fnl Algorthm1: schedulng bsed on unctegored Begn 1: Determne the number of s ccordng to the node lod nd the vlue of ; 2: Allocte the Mp tsk for ll the s; 3: If (Mp phse completed more thn set threshold or less thn the LL) { 4: Allocte the tsk; 5: Select the free ; 6 R + 1, dd the smplng ponts, dynmc prttonng dvson; } 7: else f ( s greter thn the UL) { 8: Allocte the Mp tsk; 9: Select the free ; 10: R  1, the smplng pont, dynmc prttonng merge; } 11: else ( between LL nd UL) 12: contnue; 13: end IV. EXPERIMENT AND RESULTS E. Expermentl Envronment Expermentl envronment s Hdoop cluster, whch s mde up of nne compute nodes nd these nodes dstrbuton on the two hosts, one contnng the nme node nd four dt nodes, the other nclude the remnng nodes. Two hosts v ggbt swtches nterconnected, s 2015 Journl of Communctons 781
5 Journl of Communctons Vol. 10, No. 10, October 2015 shown n Tble I, Tble II nd Tble III. the fluctutons re n smll rnge, untl the completon of job schedulng nd the recovery of the ntl vlue of the number. TABLE I: EXPERIMENT ENVIRONMENT Verson Fle system Test progrm Schedulng model Dt Hdoop1.1.2 HDFS Sort progrm FIFO/unctegored model Use RndomWrter rndomly generted TABLE II: JOBTRACKER NODE CONFIGURATION CPU OS Kernel Memory Intel Xeon(R)CPU 2 Ubuntu12.04 Lnux generc 4G TABLE III: TASKTRACKER NODE CONFIGURATION CPU OS Kernel Memory Intel Xeon(R)CPU 2 Ubuntu12.04 Lnux generc 2G Fg. 10. Slot number on unctegored model. Unctegored mproves resources utlton cn lso be reflected n the mprovement of job executon tme. Under the dfferent dt se (MB), two knds of schedulng model executon tme contrst n the sortng test hs been shown n Fg. 11. In the proposed schedulng model, job selecton strtegy s consstent wth the FIFO, except no dstngush type. In Fg. 11 we cn see unctegored schedulng model s fster thn the FIFO schedulng n executon tme. Ths s becuse when unctegored schedulng model s schedulng wth hgher prllelsm of tsks, more s cn be prtcpte n the tsk, thus mprovng the executon effcency s nevtble. Some Common Mstkes The Gngl nstlled n the cluster s to montor ech TskTrcker node nd the JobTrcker node, menwhle, ccordng to the montorng sttstcs [19], t wll set the UL to 0.8 nd set the LL to 0.6 nd ntl set to 0.2. F. Results Anlyss At frst two models for the utlton of resources by the number of s n the process of job executon re mesured. As contrst, to dstngush the n the model, we choose the most typcl nd wdely used FIFO lgorthm. Fg. 9. Slot number on FIFO model. The s number vres wth job executng under dfferent se of clusters s shown n Fg. 9 nd Fg. 10. The Grde x represents dfferent scle of dt. Fg. 9 llustrtes t the Mp phse of one tsk, the number of Mp s ncrese t frst nd then decrese, when the Mp tsk executon s fnshed, Mp s decrese to ntl vlue, nd then phse strt nd the trend s s sme s the bove. Obvously the Mp phse nd the Reduce phse resources s under stte of overlod, so the resources lod n the whole process s not blnced, whle n the two stges of coheson resources utlton rte s very low. Fg. 10 shows the s number chnges n the unctegored model. In Fg. 10, durng the entre process of executon, the chnge tendency of the number s dfferent from Fg. 9 under dfferent scle of dt, whle 2015 Journl of Communctons Fg. 11. executon tme contrst n two models. V. CONCLUSION Ths pper proposes Hdoop job schedulng model bsed on unctegored. Ths model cn elmnte the lmtton of the on the tsks nd tsks n tsk llocton, whch mkes the ssgned to ny types of tsk nd t not only cn mprove the resources utlton nd tsk prllelsm, but lso shorten the executon tme of the operton. For future work, we wll contnue on studyng the performnce stblty of module Job schedulng s well s 782
6 Journl of Communctons Vol. 10, No. 10, October 2015 the other modules n our unctegored schedulng. The system level prllelton nd schedulng lso wll be studed. REFERENCES [1] Apche Hdoop, Hdoop. (My 2015). [Onlne]. Avlble: [2] Chen He, Y. Lu, nd D. Swnson, Mtchmkng: A new schedulng technque, n Proc. IEEE Thrd Interntonl Conference on Cloud Computng Technology nd Scence, Nov. 2011, pp [3] M. Zhr, D. Borthkur, nd J. S. Srm, Job schedulng for multuser MpReduce clusters, EECS Deprtment, Unversty of Clforn, [4] A. Rj, K. Kur, nd U. Dutt, Enhncement of hdoop clusters wth vrtulton usng the cpcty scheduler, n Proc. Thrd Interntonl Conference on Servces n Emergng Mrkets, 2012, pp [5] M. Zhr, D. Borthkur, nd J. S. Srm, Dely schedulng: A smple technque for chevng loclty nd frness n cluster schedulng, n Proc. 5th Europen Conference on Computer Systems, 2010, pp [6] T. Sndholm nd K. L, Dynmc proportonl shre schedulng n Hdoop, Job Schedulng Strteges for Prllel Processng, pp , [7] K. Kml nd K. Anynwu, Schedulng Hdoop jobs to meet dedlnes, n Proc. IEEE Interntonl Conference on Cloud Computng Technology nd Scence, 2012, pp [8] X. Zhng, Y. Feng, nd S. Feng, An effectve dt loclty wre tsk schedulng method for MpReduce frmework n heterogeneous envronments, n Proc. Interntonl Conference on Cloud nd Servce Computng, 2011, pp [9] Ibrhm, Mestro: Replcwre schedulng for MpReduce, n Proc. IEEE/ACM Interntonl Symposum on Cluster, Cloud nd Grd Computng, 2012, pp [10] S. Selvlkshm nd S. Vnodkumr, Heurstc bsed heterogeneous longest pproxmte tme to end lgorthm of scentfc workflows n computtonl clouds, Interntonl Journl of Reserch n Advent Technology, vol. 2, no. 10, pp , Oct [11] Q. Chen, D. Zhng, M. Guo, Q. Deng, nd S. Guo, SAMR: A selfdptve schedulng lgorthm n heterogeneous envronment, n Proc. IEEE Interntonl Conference on Computer nd Informton Technology, 2010, pp [12] S. Ptl nd S. Deshmukh, Survey on tsk ssgnment technques n Hdoop, Interntonl Journl of Computer Applctons, pp , [13] J. Dhok, N. Mheshwr, nd V. Vrm, Lernng bsed opportunstc dmsson control lgorthm for s servce, n Proc. 3rd Ind Softwre Engneerng Conference, 2010, pp [14] W. K nd Z. Hou, A provenncebsed dptve schedulng heurstc for prllel scentfc workflows n clouds, Journl of Grd Computng, vol. 10, no. 3, pp , Sept [15] C. Tn, H. Zhou, nd Y. He, A dynmc MpReduce scheduler for heterogeneous worklods, n Proc. Eghth Interntonl Conference on Grd nd Coopertve Computng, Aug. 2009, pp [16] E. Torbdeh, Cloud theorybsed smulted nnelng pproch for schedulng n the twostge ssembly flowshop, Advnces n Engneerng Softwre, pp , [17] G. S. Sdsvm nd D. Selvrj A novel prllel hybrd PSOGA usng MpReduce to schedule jobs n Hdoop dt grds, n Proc. 2nd World Congress on Nture nd Bologclly Inspred Computng, Dec. 2010, pp [18] A. Beloglov, J. Abwjy, nd R. Buyy, Energywre resource llocton heurstcs for effcent mngement of dt centers for Cloud computng, Future Generton Computer Systems, vol. 28, pp , Apr [19] H. Mo, S. Hu, Z. Zhng, L. Xo, nd L. Run, A loddrven tsk scheduler wth dptve DSC for MpReduce, n Proc. IEEE/ACM Interntonl Conference on Green Computng nd Communctons, Aug. 2011, pp To Xue ws born n Shnx Provnce, Chn, n He receved the B.S. degree n mechncl engneerng from the X 'n Jotong Unversty of Chn (XJTU), X 'n, n 1995 nd the M.S. degree n computer scence from the Northwest Unversty of Chn (NWU), X 'n, n 2000 nd the Ph.D. degree n computer softwre nd theory from the XJTU, n He s currently n Assocte Professor n the Computer Scence Dept t X n Polytechnc Unversty. Hs reserch nterests nclude Cloud Computng, Bg Dt nd Contentbsed Networkng. Tngtng L ws born n Zhejng Provnce, Chn, n She receved the B.S. degree n network engneerng from the X n Polytechnc Unversty of Chn, X 'n, n She s currently pursung the M.S. degree wth the Deprtment of Computer Scence, X n Polytechnc Unversty. Her reserch nterests nclude Cloud Computng, Bg Dt nd Contentbsed Networkng Journl of Communctons 783
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