Hybrid Job Scheduling Mechanism Using a Backfill-based Multi-queue Strategy in Distributed Grid Computing

Transcription

1 IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.12 No.9, September Hybrd Job Schedulng Mechansm Usng a Backfll-based Mult-queue Strategy n Dstrbuted Grd Computng Ken Park Changhoon Kang and Sungsook Km Aou Unversty, Suwon, Gyeongg-do, South Korea Gangdong College, Icheon, Gyeongg-do, South Korea Anyang Unversty, Anyang, Gyeongg-do, South Korea Summary In a dstrbuted computng envronment, effectve ob schedulng s a crtcal challenge. In ths paper, a hybrd ob schedulng mechansm s proposed that consders both the meta-schedulng scheme for dstrbutng obs to overall nodes and the local ob schedulng scheme for assgnng obs wthn a local node at a smultaneous tme. Dependng on the number of requred processors and the expected executon tme of obs, the order of prortes s establshed. Jobs wth hgh prorty are then allocated to a ob queue whereas those wth low prorty are assgned to a backfll queue wth remote obs that are sent from other nodes. Experments conducted to evaluate the performance of the proposed mechansm show that the utlzaton of a grd computng system becomes more effcent and watng tmes are consderably reduced. Key words: Meta-schedulng, Job schedulng, Backfll schedulng, Dstrbuted Grd computng 1. Introducton As modern technologes advance, hgh-performance computng power s more desred to solve complex and large-scale computaton problems. To meet ths need, a sgnfcant amount of research has been conducted to explot grd computng, whch bnds up heterogeneous computng resources nto a sngle hgh performance system [1]. Recently, a varety of grd tools have been developed to support the basc grd servces such as 1) grd resource dscovery servce to locate avalable resources that are connected to grds [2], 2) grd ob schedulng servce to dstrbute and schedule obs for an effcent processng, 3) grd securty servce for the system protecton, and 4) grd accountng servce to assess costs ncurred by usng the computng resources [3]. In addton, the research on the grd mddleware lke Globus [4] and Legon [5] have made mplementatons of grd applcaton programs much easer. In a grd computng envronment, effectve ob schedulng s a crtcal challenge. The functons of the scheduler n a grd computng envronment can bascally be classfed nto two categores that nclude: 1) meta-schedulng functon regardng the dstrbuted processng of the complex problem among nodes and 2) the local ob schedulng functon wthn a local node [6]. The metaschedulng can mprove system utlzaton by dspatchng obs to local nodes n an effcent manner, and the local ob schedulng can reduce the slowdown of ob processng by schedulng obs wthn a node. So far, a maorty of researchers have consdered these two types of functons separately. Therefore, t s desrable to develop a hybrd ob schedulng mechansm that consders both the metaschedulng scheme and the local ob schedulng scheme n order to mprove the overall performance of the grd computng system. For example, Lawson et. al.[7] presented a schedulng for both remote obs that s mgrated from another node and local obs by use of the mult-queue concept n a dstrbuted parallel processng envronment. Although a varety of dstrbuted computng schedulng algorthms have been studed [8], they tend to consder only the problem s complexty, and they may be unsutable to be drectly used n a geographcally dstrbuted grd computng envronment. A backfll algorthm [9] that fts for a heterogeneous dstrbuted parallel computng envronment was ntroduced to acheve a faster response tme n grd computng. However, there exst trade-offs among performance metrcs because the algorthm may yeld dfferent ob dspatchng results dependng on the characterstcs and prortes of obs. More specfcally, prortes of a ob that are determned to ncrease the system utlzaton can ncur ob slowdown, and as a consequence may result n a decrease of system utlzaton. It s well known that NP-completeness results when the utlzaton of the grd computng system and ob slowdown s smultaneously mproved by adustng the prorty of obs based on ob characterstcs [10]. Motvated by ths, we propose a hybrd ob schedulng mechansm that can explot both the meta-schedulng scheme and the local ob schedulng scheme. Ths paper s organzed as follows. In secton 2, related work s presented, and the hybrd ob schedulng mechansm s then presented n secton 3. Secton 4 dscusses the Manuscrpt receved September 5, 2012 Manuscrpt revsed September 20, 2012

2 40 IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.12 No.9, September 2012 backfll-based mult-queues schedulng scheme. In secton 5, the experments and the results are provded to llustrate the performance of the proposed mechansm, and the research summary and future drectons for possble research are dscussed n Secton Related Work The meta-schedulng schemes can be classfed nto centralzed schedulng and de-centralzed schedulng. In the centralzed schedulng, all grd obs are submtted to the meta-scheduler that dstrbutes them to nodes based on the status of the nodes n whch ths status nformaton s managed by the scheduler. Although ths approach can be satsfactory, t may not be scalable because the metascheduler needs to manage all nformaton and dstrbute the obs. On the other hand, the de-centralzed schedulng does not requre a separate meta-scheduler snce all the obs are submtted drectly to the schedulers of the relevant local node. Dependng on a schedulng polcy, the scheduler of each node then assgns obs to tself or sends them to another avalable node. Although hgh scalablty can be realzed by ths approach, ths scheme s consderably more dffcult to mplement, and synchronzaton s hard to acheve [11]. Most of the research on grd schedulng s conducted based on the decentralzed schedulng scheme where submtted obs n a certan node are assgned to other nodes when the obs cannot be processed n that node [12]-[14]. Wth regard to the ob processng types, there are three types: batch processng, nteractve processng, and parallel processng. Snce the batch processng and the nteractve processng are executed n a sngle node, submtted obs are usually allocated to the least-loaded node. If a node s overloaded, the obs beng executed n that node are sent to another node. On the other hand, the parallel processng requres multple concurrent processors to process obs. Therefore, heavy delays n communcatng among nodes or long ob executon tmes can be ncurred f the obs that are beng executed on multple nodes do not run n a synchronous manner [15]. Varable parttonng scheme s one of the most frequently used schedulng schemes for parallel processng ob schedulng. In ths scheme, assgnments of obs can be descrbed as rectangular shapes n a graph wth the requred resource space on the x-axs and the ob executon tme on the y-axs. As Fg. 1 shows, the occuped area represents the system utlzaton of a parallel system. It s worth notng that a ob wth a low prorty cannot be executed even when resources are avalable snce the varable parttonng follows the frst-come frst-served (FCFS) polcy. As shown n Fg. 1, t s obvous that ob (4) that arrves after ob (3) has been completed cannot be executed snce the resource s not enough due to the two obs (1 and 2) that are beng currently processed. Nor can ob (5) be executed even though there exsts suffcent avalable resources to process t. Ths s because ob (5) has a lower prorty than ob (4) accordng to FCFS polcy. Ths causes fragmentaton that decreases the system performance as a result of beng unable to use dle resources. Therefore, t can be nferred that the response tme for ob (5) can be reduced f the ob can be assgned wthout nfluencng ob (4). Tme Job 5 arrves Job 4 arrves Runnng Job (J1) Runnng Job (J2) Watng Job (5) Watng Job (4) Runnng Job (J3) Fg. 1 Varable parttonng scheme. Shortage System resource The backfll scheme has been proposed to address the fragmentaton problem [9]. Ths scheme changes the prorty of grd obs n the ob queue n such a way that a ob can be moved to the top of the ob queue f t can be executed by currently avalable resources wthout delayng the processng of hgher prorty obs. A number of schemes based on backfll schedulng have been developed to enhance performance by: 1) reducng ob slowdown by assgnng a hgh prorty to a ob wth short executon tme [16], 2) reducng ob slowdown by sortng ob orders accordng to the watng tme snce ther submsson [17], and 3) ncreasng system utlzaton by allowng ob slowdowns ncurred by obs wth short executon tme or low prorty [18]. In spte of the research, the trade-off between system utlzaton and ob slowdowns remans to be addressed [19]. Although gang schedulng [20] and dynamc partton [21] have also been proposed for the purpose of addressng the fragmentaton problem, several lmtatons such as communcaton overhead or clock synchronzaton n a node have restrcted ther extensve use. In the backfll schedulng schemes, the conservatve backfll schemes allow executon of a low prorty ob only

3 IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.12 No.9, September when t does not cause any slowdowns of all the other obs wth hgher prorty. The EASY backfll schemes execute low prorty obs n a queue when the slowdown of only the frst ob n the queue s not caused. The EASY backfll can ncrease the system utlzaton of the entre system by backfllng more obs than the conservatve backfll. However, t may not be able to guarantee ob completon tmes to users due to possble unbounded delays caused by the ob slowdowns. In ths paper, a hybrd ob schedulng mechansm s presented to ncrease system utlzaton that s not easy to acheve by the conservatve backfll and to prevent the unbounded delay ncurred by the EASY backfll. The mechansm dstrbutes hgh-prorty obs to the ob queue where reserved obs are stored. Also, t allocates obs wth low prorty or remote obs that are sent from other nodes to the backfll queues. Several experments are conducted to evaluate the proposed mechansm wth real data, and the results show that the utlzaton of a grd computng system ncreases whle ob slowdown decreases at the same tme. meta-scheduler that keeps track of processng requests for remote obs, and t then allocates the ob n the ob queue f the meta-schedulng polcy s not volated (7. Allocate Job). After the recevng node completes the ob n accordance wth a local ob schedulng strategy, t notfes the user (8. Reply Job). 3. Structure of Dstrbuted Grd Computng System Fg. 2 shows the archtecture of the grd computng system that enables the hybrd ob schedulng mechansm. In the archtecture, grd workers (.e. computers) consttutng a grd computng node are lnked as a sngle logcal group through the Internet, and they operate as a sngle computer system. Each node conssts of grd workers, a metascheduler, and a local ob scheduler. The hybrd ob schedulng mechansm proposed n ths paper supports the nteractons between a meta-scheduler and a local ob scheduler to mnmze the schedulng load wthn a local node and to facltate cooperaton among nodes. As such, t can mprove the effcency and the avalablty of a grd computng system by allowng both the meta-schedulng and the local ob schedulng to be smultaneously utlzed. When a user submts a ob to a node (0. Intate Job), the scheduler of the node ether allocates the ob to the ob queue for the submtted ob to be processed by a local ob schedulng polcy, or sends t to one of the backfll queues where the ob wats to be assgned to the ob queue (1. Request Job). If t s found to be mpossble to execute the ob at the node due to excessve load, the scheduler requests other nodes for processng t (2. Request Remote Job). If resources are avalable n other node(s) by broadcastng and replyng among nodes (3. Dscover & 4. Volunteer), the ob s sent to the other node(s) (5. Negotate & 6. Request Job). At ths tme, the local scheduler n the recevng node communcates wth the Fg. 2 Archtecture of dstrbuted grd computng system. To put t another way, the proposed archtecture for the hybrd ob schedulng can ncorporate centralzed herarchcal schedulng strateges that can be exercsed by meta-schedulers and de-centralzed schedulng polces mplemented by local ob schedulers smultaneously. Thus, communcaton bottlenecks, SPOF (Sngle Pont of Falure) problems, or scalablty problems can be addressed wth the grd computng archtecture. Fg. 3 Mult-queue scheduler of computng nodes. Fg. 3 shows the structure of the mult-queue scheduler of

4 42 IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.12 No.9, September 2012 local nodes n a dstrbuted grd computng system. Each local node conssts of workers partcpatng n the grd computng and a scheduler that supports both the metaschedulng and the local ob schedulng by means of the mult-queue backfll mechansm. The scheduler agan conssts of two types of queues: The frst s the ob queue that stores local obs that are executed by FCFS polcy, and the second are the two backfll queues where obs are watng for backfllng. It should be noted that the scheduler consders both the states of all the workers and the ob characterstcs when t employs the schedulng strategy. In other words, a ob that s submtted to an arbtrary node s assgned to one of the three queues wthn the node by the local ob scheduler. To whch queue a ob s assgned s determned based on the number of requred processors and the expected executon tme of the ob. Jobs that requre a large number of processors are assgned to the ob queue where other obs are reserved dependng on a ob prorty. On the other hand, local obs requrng a small number of processors and obs that are receved from other nodes are sent to the backfll queues. If the node s overloaded, t s sent to other node wth the least-loaded backfll queues by the meta-scheduler. The ob s then stored n one of the backfll queues of the recevng node. 4. Mult-queue Schedulng Mechansm 4.1 Mult-queue Management Strategy The mult-queue management strategy (MMS) s related to the process that allocates submtted obs to ether the ob queue or the backfll queues based on ther characterstcs. In backfll schedulng, t s mportant to determne how many obs are to be backflled n an effcent way. Jobs that requre a small number of processors may have hgh possblty of beng backflled snce t s easer for them to acqure avalable processors wthn a node. Ths approach can ncrease the lkelhood of mprovng the utlzaton of the processors. In addton, whle obs n the ob queue are executed n FCFS fashon, obs n the backfll queue can be backflled whenever a backfll s possble even when a ob n the ob queue s beng executed. Thus, MMS strategy can be appled when 1) a ob s submtted to a local node, 2) a ob on the local node s completed, or 3) a remote ob s dspatched from other nodes. It s worth remarkng that prortes of remote obs are set to be lower than those of the local obs. Ths s appled by allocatng them to the backfll queues rather than to the ob queue. It can contrbute to guaranteeng the completon tme of local obs that are submtted drectly to a local node. More specfcally, n order to prevent a local ob submtted to a certan node from beng slowed down by remote obs, flags are gven to obs of specfed ob types such as a local ob or a remote ob Job Classfcaton and Prorty Assgnment Strateges for Local Jobs For the meta-schedulers and the local ob schedulers to apply MMS, they reference basc nformaton that ncludes ob number (), ob arrval tme ( TA ), and node number ( k ) on the grd. In addton, obs submtted to a certan local node contan the parameters wth regard to the number of the requred processors ( P ) for executon and the expected executon tme ( TES ). Thus, a ob can be represented by the followng expresson (1). JOB( TA, TES, P, k ) (1) Based on nformaton about obs, MMS classfes and prortzes obs through the followng steps. In step 1, obs are classfed nto three groups dependng on the total number of requred processors n the node to whch they are drectly submtted or remotely dspatched. In step 2, each group s further classfed nto two subgroups accordng to the expected executon tme. For those obs that are classfed n one of sx groups that are formed through these steps, MMS s appled n such a way that 1) obs requrng a large number of processors are allocated to the ob queue for fast completon, and 2) those requrng a small number of processors are assgned to the backfll queues for the hgher system utlzaton through more frequent backflls (refer to Fgure 4). In step 3, prortes of obs are assgned. n.mult.dstrbute () f (.req <= n.processor * 1/3) n.mult.enqueue(, backfll2); else f (.req <= n.processor * 2/3) n.mult.enqueue(, backfll1); else n.mult.enqueue(, obqueue); n.mult.mgrate(n, ); n.mult.enqueue(, backfll1); Fg. 4 Mult-queue management strategy pseudo-code. Step 1 Job classfcaton by the number of processors

5 IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.12 No.9, September In ths step, obs are classfed nto three groups based on the number of requred processors as expressed by condtons (2) (4), where TP s the number of processors n node. After beng classfed, obs are allocated to the queues n a node. Large obs are stored n the ob queue, and medum obs and small obs are stored n the frst backfll queue and the second backfll queue, respectvely. L(Large Job) : TP 2 P 3 TP M(Medum Job) : TP 1 2 P 3 < TP 3 S(Small Job) : 1 P 1 < TP 3 Step 2 Job classfcaton by expected executon tme In ths step, obs n each group formed by the step 1 are further dvded nto two types based on the expected executon tme. If the expected executon tme of a ob s longer than the average executon tme of obs that have been completed n the recevng node up to a current pont of tme, the ob s classfed as L-type (Long Job). Otherwse, t s characterzed as S-type (Short Job). Ths classfcaton s expressed n condtons (5)-(6): (2) (3) (4) L(Long Job) : TES TMEAN (5) S(Short Job) : TES < TMEAN ( 6) where TES s the expected executon tme of the ob, and TMEAN s the average executon tme of obs that have been executed on the recevng node. It should be noted that the average executon tme n a node vares as the node contnues to process multple obs. Therefore, the classfcaton crteron changes n a dynamcal manner. SS (Small Short) Low SL (Small Long) ML MS (Medum Long) (Medum Short) prorty Fg. 5 Prorty order of local obs. LL (Large Long) LS (Large Short) Step 3- Prorty assgnment by ob class A ob type that s obtaned through the above mentoned ob classfcaton steps s used to determne the prorty of ob processng n the ob queue and the sequence of the backfll n the backfll queues. More specfcally, S-type obs have hgher prortes n processng or backfllng than L-type obs. Fg. 5 shows the prortes of local obs that Hgh are determned by the two steps descrbed above. Jobs n the rghtmost cells have the hghest prorty, and those n the leftmost have lowest prorty Job Classfcaton and Prorty Assgnment Strateges for Remote Jobs As aforementoned, remote obs sent from other nodes are allocated to the backfll queues of a recevng node. The second part n Fg. 4 shows the step that processes the remote obs. Any remote obs are assgned to the backfll queue (e.g. Backfll1) of a recevng node by the dstrbuton strategy for remote obs. Ths s n order to gve the local obs hgher prorty over remote obs so that they can be processed earler than the remote obs. The classfcaton of the remote obs s done n a smlar way as the local obs. In the frst step, remote obs are classfed accordng to the number of the requred processors ( P ) for executon, and they are labeled as ether Remote Large (RL) obs or Remote Small (RS) obs dependng on condtons (7) (8). RL (Remote Large Job) : TP 1 P 2 TP RS (Remote Small Job) : 1 P 1 < TP 2 In the second step, they are further classfed based on the executon tme as n condtons (9)-(10). When a ob s sent from another node, the recevng node references nformaton concernng ob executon tme and compares t wth the average executon tme up to a current pont of tme to determne the type of the remote ob. (7) (8) RL (Remote Long Job) : TES TMEAN (9) RS (Remote Short Job) : TES < TMEAN (10) The Fg. 6 shows the prorty orders of remote obs that are determned by the two steps mentoned above. RSS (Remote Small Short) Low RSL (Remote Small Long) RLL (Remote Large Long) prorty Fg. 6 Prorty order of remote obs. 4.2 Hybrd Job Schedulng Strategy RLS (Remote Large Short) Hgh The meta-schedulng strategy ams at mprovng the system utlzaton of the entre grd system and the local ob

6 44 IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.12 No.9, September 2012 schedulng strategy helps acheve an effcent ob dstrbuton wthn a node. Jobs n the backfll queues can be delayed unboundedly when requred processors for executon of a ob are not obtaned, and the obs are not reserved n the backfll queue. In partcular, the probablty of unbounded delay of remote obs can be larger than that of local obs. The drawbacks of both the conservatve and the EASY backfll mechansms that were descrbed n secton 2 can be allevated by the mechansm proposed n ths paper, whch prevents unbounded delays and mproves system utlzaton. n.global.dscover () V = volunteer lst wth dle resources; for each v V v.global.negotate(n, ); n.global.negotate (n, ) runnable = n.local.create(); f (runnable) n.global.commt(n, ); else n.global.wat(n, ); n.global.commt (n, ) n.mult.mgrate(n, ); n.global.wat (n, ) f (n.mult..resv_counterg++ > Threshold){ fx ob s prorty nto the urgent level; n.global.reserve(); } (a) meta scheduler n.local.create () span = some runs durng an admtted tme; f (n.aval >=.req) n.local.execute (, span); else { q = n.mult.dstrbute(); n.local.wat(); } n.local.remote () n.global.dscover(); n.local.execute (, span).met =.met span; f (.met == ) n.mult.depart(); else { span = some runs durng an admtted tme; n.local.execute(, span); } n.local.wat ( ) f (n.mult..rcounterl++> Threshold){ fx ob s prorty nto the urgent level; n.local.reserve(); } n.local.reserve () span = thereserved executon slotsfor.req; n.local.backfll(, span); n.local.backfll (, span).met =.met span; f (.met == ) n.mult.depart(); else n.local.execute(); n.local.volunteer () newly = current dle resources; n.aval = n.aval + newly; (b) local scheduler Fg. 7 The meta- and the local ob schedulng procedure. Meta-schedulng Strategy: When a ob s submtted to a node, the meta-scheduler determnes whether the ob s to be processed n the recevng node or to be sent to another node based on the status of recevng node and grd system. In ths stage, the meta-scheduler negotates wth meta-schedulers of other nodes for better ob allocaton. Fg. 7 (a) shows the procedures of the meta- schedulng. When a ob () s submtted to a node (n), the meta-scheduler of node (n) searches for volunteer nodes for processng a ob () (n. global.dscover ()). At ths tme, the meta-scheduler of a volunteer node (e.g., node v) communcates wth the local ob scheduler to check resource avalablty for processng the ob (). If a resource s avalable, node (v) responds wth a commt message (n.global.commt (v, )). Then, ob () s mgrated from node (n) to node (v) (n.global.mgrate (v, )). Local Job schedulng Strategy: The local ob schedulers can manage nformaton about a node status regardng the number of avalable processors, the system utlzaton (memory usage, network usage, etc.), system slowdowns, and expected response tme. Based on ths nformaton, the scheduler makes a decson wth regard to ob executon and dspatchng to other node (refer to Fg. 7 (b)). To prevent unbounded delay of obs n the ob queue, a certan number of obs are reserved to be executed so that they are not slowed down due to other obs n the backfll queue. By dong ths, the system utlzaton can also be ncreased more than the EASY backfll whereas only the frst ob s reserved n the EASY backfll mechansm. The number of the reserved obs ( J ) n the ob queue s r determned by (11): J r = α J, 0.1 α 0.5 (11) n where J s the number of queued obs n the ob queue, n and α s the threshold value used to adust the number of the reserved obs. To prevent unbounded delays of obs n the backfll queue, a reservaton technque s also appled to obs n the backfll queue. More specfcally, when the number of obs that are not backflled ( J ) s found to be larger than a NB threshold value ( β ) at a certan pont of tme, those obs are dspatched to the ob queue, or sent to other node f they can be executable n that node. It can be expressed as (12). J β (12) NB When the slowdowns of obs n the backfll queues occur even f the obs are reserved by condton 12, those obs need to be sent to another node. In ths case, those obs become remote obs n the other node that receves them. To determne whch node the obs are to be sent to, the mechansm proposed n ths paper checks whether the obs can be executed n the recevng node n consderaton of executon and transfer tme of the obs. By dong ths, the nfluence of remote obs n a recevng node can be mnmzed. The number of reserved obs plays a key role n the proposed mechansm. It s set to greater than n the EASY backfll and less than n the conservatve backfll.

7 IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.12 No.9, September Performance Evaluaton In order to evaluate the performance of the hybrd ob schedulng mechansm proposed n ths paper, several experments were conducted. In ths type of experment, nput data play a crtcal role n evaluatng the performance of dfferent mechansms because of ther nfluence on the ob load. Accordng to the followng two steps, the nput data are generated. In the frst step, parameters of ndependent varables (e.g., mean arrval tme, mean estmated executon tme, mean number of processors, mean wdth, etc.) are generated from the set of workload logs of Fetelson Archve [22]. Table 1 shows the parameters that are generated from CTC trace (CTC: the Cornell Theory Center 512-node IBM SP2, 79,296 obs) and Fetelson workload. The nodes of the CTC computer are not all dentcal and they dffer n type and memory. Table 1: Workload data Month Total A Type B Type July Aug Sep Oct Nov Dec Jan Feb Mar Apr May Average compared by means of the average response tme metrcs. The average slowdown tme of the ob can be obtaned usng (13): [16] N resp( ) = wdth( ) * max( S, exec( )) AverageSlowdownTme = 1 N (13) where N s the total number of obs, S s the processng tme of the ob wth the shortest executon tme, resp() s the tme elapsed between ob submsson and completon, exec() s the executon tme of the ob, and wdth() s the number of the processors requred for the ob to be executed. To compare the effectveness of the mult-queue (AverageSlowdownTme m ) and the sngle queue (AverageSlowdownTme 1 ) strateges, the slowdown rato s used. SlowdownRato (14) AverageSlowdownTme1 AverageSlowdownTmem = mn( AverageSlowdownTme, 1 AverageSlowdownTme ) As can be observed n (14), a postve value of SlowdownRato means that the average slowdown of the sngle queue strategy s greater than that of the mult-queue strategy. On the other hand, negatve value of SlowdownRato ndcates that the mult-queue strategy causes greater slowdowns due to the management overheads. m A Type: Jobs requrng at most 256 nodes, B Type: Jobs requrng at most 128 nodes In the second step, wth the parameters that are generated n the frst step, other parameters for the experments are then obtaned based on the probablty functons whch are descrbed as follows. The average ob arrval, the ob request tme, and the average number of requred processors are assumed to follow an exponental dstrbuton wth the rate of 0.167/mn. ( 7200 obs/month), an exponental dstrbuton wth the average of 100 mn, and a unform dstrbuton from 1 to 64, respectvely [9]. Fnally, a grd system wth 8 nodes of 64 processors each s used. The backfllng ratos that s the rato of the number of backflled obs to the number of queued obs wth regard to the system utlzaton are measured, and the mpact of the hybrd mult-queue and the remote ob processng s assessed n terms of the slowdown rato of the obs (SlowdownRato, refer to equaton 14). In addton, the slowdowns caused by dfferent schedulng strateges are Backfllng Rato CONS EASY HYBRID System Utlzaton Fg. 8 Backfllng ratos dependng on the system utlzaton. Fg. 8 shows the changes of the backfllng ratos of three dfferent strateges dependng on the system utlzaton that s calculated by dvdng the ob arrval tme by the average

8 46 IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.12 No.9, September CONS 0.6 EASY HYBRID 0.4 Slowdown Rato dspatchng obs to remote nodes plays an mportant role n mprovng the response tme of ob executons f a dedcated network s not used to connect nodes of a grd. Note that the ncreased system utlzaton does not always mean reduced ob response tme. 24 Average Response Tme (n 10 3 sec) processng tme. Therefore, more frequent ob requests can ncrease the system utlzaton. As can be observed n Fg. 8, the EASY backfll method has more backflls than the conservatve backfll method. Furthermore, backflls occur the most frequently when the hybrd mult-queue schedulng mechansm s appled, whch means that hybrd mult-queue schedulng mechansm mproves the system utlzaton of the entre grd system. 0.2 CONS EASY 22 HYBRID SS SL MS ML LS 10 LL System Utlzaton -0.4 Fg. 10 Average response tme accordng to system utlzaton Job Classfcaton Fg. 9 The changes of the slowdown rato. 20 nse Tme (n 10 3 sec) Average Respo α Fg. 9 shows the changes of the slowdown rato of obs accordng to the number of requred processors and the expected executon tme for whch both are used ob classfcaton. It can be observed that the hybrd multqueue s more effectve to decrease the ob slowdown rato than the sngle queue regardless of schedulng strategy. In addton, the slowdowns occur for the obs (LS and LL) that requre a large number of processors due to the backflls of the obs wth low prorty. However, the hybrd mult-queue schedulng mechansm causes fewer slowdowns than both the conservatve and the EASY backfll methods even f a sngle queue s used. From ths observaton, t can be nferred that the hybrd ob schedulng method can have advantages that nclude the unbounded delay preventon and the ncreased backfll rato whch are the drawbacks of the conservatve backfll method and of the EASY backfll methods, respectvely. Fg. 10 shows the changes of the average response tme wth regard to the system utlzaton. In case of the EASY backfll method, hgher system utlzaton ncurs a steep ncrease n the average response tme due to ncreased number of backflls. In the case of the hybrd method proposed n ths paper, the average response tme s greater than the conservatve method n spte of smaller ob slowdown. Ths s because the hybrd method dspatches obs to other nodes more frequently than the conservatve method. Therefore, t can be nferred that the overhead of β Fg. 11 Average response tme wth parameters ( α, β ) used n the reservaton polcy. Fg. 11 shows the graph of the average response tmes accordng to the value of α (the threshold value that s used to adust the rato of the reserved obs to queued obs n ob queue) and β (the threshold value for those obs that are backflled for executon) that are ntroduced to mplement the reservaton polcy of the conservatve and the EASY backflll methods and to prevent unbounded delay of the EASY backfll method, respectvely. As α ncreases, the average response tme also ncreases. Ths s because more obs are moved to the ob queue and backflled, resultng n longer watng tme of obs that are

9 IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.12 No.9, September not backflled. Also, the decrease of the threshold value of β that restrans the backfll of obs that can otherwse be backflled causes the ncrease n the average response tme. However, the mpact of the value of β on the average response tme s observed to decrease for somewhat large values of β (e.g., β 64). Ths s more obvous n case of small values of α. In partcular, because there can be more avalable processors f each node has more processors, the probablty of the slowdowns caused by the backflls decreases. 6. Concluson Although a sgnfcant amount of research has been conducted on the developments of schedulng mechansms to reduce ob slowdown, mprove the response tme and the system utlzaton of a grd system, most of the research has been pursued n a separate manner to solve problems of the meta-schedulng methods that dstrbute obs to each node and the local ob schedulng methods wthn a node. In order to address ths stuaton, we have proposed the hybrd ob schedulng mechansm that consders both the meta-schedulng and the local ob schedulng approaches at the same tme. Jobs that are submtted to grd computng nodes are classfed based on the number of requred processors and the expected ob executon tme. Ths classfcaton schema s then used to determne whether obs are processed n a local node or dspatched to other node(s) accordng to the ob load. As the backfllng rato and ob slowdowns are mproved wth the hybrd mult-queue schedulng mechansm, the utlzaton of a grd computng system becomes more effcent and watng tmes are reduced. Future work wll nclude the nvestgaton of the mpact of the network communcaton overhead on overall performance of a grd system and wll be checkng for the mplementaton ssues wth varous nput data sets. Acknowledgments Ths work (Grants No. C ) was supported by Busness for Cooperatve R&D between Industry, Academy, and Research Insttute funded Korea Small and Medum Busness Admnstraton n References [1] I. Foster, et. al., Grd Servces for Dstrbuted System Integraton," Computer, Vol. 35, No. 6, pp , [2] Ken Park, et. al., Credble Worker Selecton Mechansm for Grd Computng, IEICE Transactons on Informaton and Systems, Vol. E89-D, No.2, pp , Feb [3] K. Krauter, et. al., A Taxonomy and Survey of Grd Resource Management Systems for Dstrbuted Computng, Software Practce and Experence Journal, Vol. 32, No. 2, pp , Feb Summary: Grd computng s concerned wth the sharng and coordnated use of dverse resources n dstrbuted "vrtual organzatons." The dynamc and multnsttutonal nature of these envronments ntroduces challengng securty ssues that demand new techn... [4] I. Foster, et. al., Globus: A Metacomputng Infrastructure Toolkt, The Internatonal Journal of Supercomputer Applcatons and Performance Computng, Vol. 11, No. 2, pp , Oct [5] A. Grmshaw, et. al., Legon: Lessons Learned Buldng a Grd Operatng System, Proceedngs of the IEEE, Vol. 93, Issue 3, pp , Mar [6] H. Shan, et. al., Job Superscheduler Archtecture and Performance n Computatonal Grd Envronments, In SC 2003 Conference, [7] B. Lawson, et. al., "Multple-queue Backfllng Schedulng wth Prortes and Reservatons for Parallel Systems," The 8th Internatonal Workshop, JSSPP 2002 Ednburgh, Scotland, UK, pp , July [8] T. Braun, et. al., A Comparson of Eleven Statc Heurstcs for Mappng a Class of Independent Tasks onto Heterogeneous Dstrbuted Computng Systems, Journal of Parallel and Dstrbuted Computng, Vol. 61, pp , [9] A. Mualem, et. al., Utlzaton, Predctablty, Workloads and User Run tme Estmates n Schedulng the IBM SP2 wth Backfllng, IEEE Trans. Parallel and Dstrbuted System, Vol. 12, No. 6, pp , June [10] O. Ibarra, et. al., Heurstc Algorthm for Schedulng Independent Tasks on Nondentcal Processors, Journal of ACM, Vol. 24, No. 2, pp , Apr [11] V. Hamscher, et. al., "Evaluaton of Job-Schedulng Strateges for Grd Computng," The 1st IEEE/ACM Internatonal Workshop on Grd Computng (Grd 2000) at the 7th Internatonal Conference on Hgh Performance Computng (HPC-2000), LNCS 1971, pp , [12] V. Subraman, et. al., Dstrbuted Job Schedulng on Computatonal Grds Usng Multple Smultaneous Requests, The 11th IEEE Internatonal Symposum on Hgh Performance Dstrbuted Computng (HPDC ), pp , July [13] Q. Wang, et. al., De-centralzed Job Schedulng on Computatonal Grds Usng Dstrbuted Backfllng, Grd and Cooperatve Computng - GCC 2004: Thrd Internatonal Conference, LNCS 3251, pp , Oct [14] K. L, Job Schedulng for Grd Computng on Metacomputers, The 19th Internatonal Parallel and Dstrbuted Processng Symposum (IPDPS 2005), Abstracts Proceedngs, Apr [15] D. Fetelson, et. al., Theory and Practce n Parallel Job Schedulng, Job Schedulng Strateges for Parallel Processng, IPPS'97 Workshop, Geneva, Swtzerland, LNCS 1291, pp. 1-34, Apr. 5, [16] D. Zotkn, et al., Job-Length Estmaton and Performance n Backfllng Schedulers, The 8th IEEE Internatonal

10 48 IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.12 No.9, September 2012 Symposum on Hgh Performance Dstrbuted Computng (HPDC'99), Aug [17] S. Srnvasan, et al., Characterzaton of Backfllng Strateges for Parallel Jobs Schedulng, 31st Internatonal Conference on Parallel Processng Workshops (ICPP 2002 Workshops), pp , Aug [18] W. A. Ward Jr., et al., Schedulng Jobs on Parallel Systems Usng a Relaxed Backfll Strategy,, 8th Internatonal Workshop on Job Schedulng Strateges for Parallel Processng(JSSPP 2002), pp , July [19] B. G. Lawson, et al., Self-Adaptng Backfllng Schedulng for Parallel Systems, 31st Internatonal Conference on Parallel Processng (ICPP 2002), pp , Aug [20] D. Fetelson, et al., Improved Utlzaton and Responsveness wth Gang Schedulng, Job Schedulng Strateges for Parallel Processng, IPPS'97 Workshop, Geneva, Swtzerland, LNCS 1291, pp , Apr [21] R. McCann, et. al., A Dynamc Processor Allocaton Polcy for Multprogrammed Sharedmemory Multprocessors, ACM Trans. on Computer System, Vol. 11, No. 2, pp , May [22] D. Fetelson, Logs of Real Parallel Workloads from Producton Systems, Avalable: Ken Park receved the B.S. degree n ndustral engneerng from Hanyang Unversty, Seoul, Korea, n 1989, the M.S. degree n ndustral engneerng from Pohang Unversty of Scence and Technology, Pohang, Korea, n 1991, and the Ph.D. degree from the Department of Computer Engneerng, Graduate School, Aou Unversty, Suwon, Korea, n From 1991 to 1997, he was wth the Software Research and Development Center, Samsung Electroncs Company Ltd., Suwon, as a Senor Researcher. From 2001 to 2002, he was wth the Network Equpment Test Center, Electroncs and Telecommuncatons Research Insttute, Daeeon, Korea, as a Senor Researcher. From 2002 to 2004, he was wth the Department of Computer Engneerng, Anyang Unversty, Anyang, Korea, as a Professor. Snce 2004, he has been an Assocate Professor wth the Dvson of Industral and Informaton Systems Engneerng, Aou Unversty. From 2010 to 2011, he was wth Rutgers, The State Unversty of New Jersey, Pscataway, as a Vstng Professor. Hs research nterests nclude n-vehcle network, fault-tolerant computng, and cloud computng. Changhoon Kang was born n Daeeon, Korea. He receved the B.S. and M.S. degrees n computer scence from Chungnam Natonal Unversty, Korea, n 1986 and 1988, respectvely, and Ph. D. degree n Department of Computer Engneerng, Graduate School of Aou Unversty n Korea n He s currently an Assocate Professor n Department of Vsual Broadcastng Meda, Kangdong College. From 1990 to 1993, he worked n the Computer Center of Chungnam Natonal Unversty, Korea, as an Assstant Teacher. Hs research nterests nclude cluster computng, and grd computng. Sung sook Km receved the B.S degree n Educatonal technology from Hanyang Unversty, Seoul, Korea, n 1991, the M.S. degree n E-Learnng from Aou Unversty, Suwon, Korea, n 2009, respectvely, where she s currently workng toward the Ph.D. degree n the Department of Computer Scence & Engneerng n Anyang Unversty. Her research nterests nclude real-tme data mnng from moble log data and task schedulng for MapReduce software framework n cloud computng.