Real-time Fault-tolerant Scheduling Algorithm for Distributed Computing Systems

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1 Real-tme Fault-tolerant Schedulng Algorthm for Dstrbuted Computng Systems Yun Lng, Y Ouyang College of Computer Scence and Informaton Engneerng Zheang Gongshang Unversty Postal code: P.R.CHINA {ylng, oyy}@mal.zgsu.edu.cn Journal of Dgtal Informaton Management ABSTRACT: Ths artcle proposes a Dstrbuted Realtme Fault-tolerant model, prorty Real-tme Fault-tolerant algorthm and computatonal archtecture of Dstrbuted Real-tme Fault-tolerant. Accordng to ths model, the problem of how to schedule a weghted Drected Acyclc Graph (DAG) n Dstrbuted computng system for hgh relablty can be solved n the presence of multprocessors faults. When some tasks n a DAG have dependence on each other, a task must be scheduled to make sure that maxmum communcaton effcency and hgh relablty can be guaranteed due to a processor falure. Frstly, ths paper propose task model, communcaton model and relablty model for evaluatng fault-tolerant performance of the system, and defne the prorty of a task so that a crtcal task s defned as one wth the hghest prorty. To add some constraned condtons that do not nfluence the earlest start tme of ts successors n the process of schedulng task, The Dstrbuted Realtme Fault-tolerant Schedulng Algorthm (DRFACS) targets maxmzng relablty to dynamcally schedule dependent, non-preemptve, non-perodc real-tme tasks to mprove the qualty of servce through schedulng n the case of arsng massve resource falures. Fnally, Expermental results demonstrate the feasblty of the proposed algorthm. Categores and Subect Descrptors: D.4.7 [Organzaton and Desgn]; Dstrbuted systems C.4 [Performance of Systems]; Fault tolerance General Terms: Dstrbuted computng, Fault-tolerance systems Keywords: Fault-tolerant Schedulng Algorthm, Drected Acyclc Graph (DAG), Communcaton relabty Receved: 15 March 2012, Revsed 28 May 2012, Accepted 9 June Introducton Fault tolerance, communcaton effcency and relablty are mportant requrements n dstrbuted computng, whch often ncludes geographcally dstrbuted nodes cooperatng n executng tasks. The study on fault-tolerance plays a key role n dstrbuted computng, especally when mult-processors fal. In the last decade the feld of Dstrbuted Computng Systems had an outstandng evoluton from scentfc areas to commercal areas. Wth the development of these areas, lookng for cheap and powerful computng platform around the corner - the Dstrbuted computng system came nto beng. Dstrbuted computng s to enable dstrbuted computng tasks on a large number of dstrbuted computers. The man dea of Dstrbuted computng s the computng resources on the Internet all together and t manly focused on the feature of hgh relablty. However, large-scale Dstrbuted computng resources used by the system are hghly dynamc and heterogeneous resources. The nherent state of envronment s unrelable, so the dstrbuted computng platform has greater possblty of errors than the tradtonal condton. The challenge n Dstrbuted computng s to tolerate large-scale dstrbuted system or elmnate these errors, whch s caused by computer hardware falures, software errors and falures of other sources [1-2]. 2. Related works Hgh relablty and mnmzed communcaton tme nvolved n the research are few. The case for mult-treatment falure, some methods have been proposed, such as FTBAR [4], FTSA, MC-FTSA [5], CAFT [6]. FTBAR s based on Lst heurstc actve replcaton technology. Schedulng algorthm, whch allows a task schedulng N +1 copes to dfferent processors, they can be parallel executon and tolerate up to N processors falure. However, a large number of copes of the task and assgnng these tasks to processors can cause a lot of communcaton latency and mprove the computng complexty degrees. In order to solve the communcaton problem, FTSA proposed a new data structure to reduce the large number of redundant Journal of Dgtal Informaton Management Volume 10 Number 5 October

2 communcaton t by mapped to the communcaton between the processor and task as one to one relatonshp. However, the reducton of communcaton wll affect the start tme of communcaton task. The cause s that FTSA ddn t take nto account the start tme of follow-up tasks so that the executon tme of DAG s not optmzed. CAFT, MC-FTSA from communcaton and tme complexty pont, by multple copes of the task mapped to dfferent processors, they have mnmzed the number of communcaton and tolerance of processor falure has been largely restrcted, that s, the less the number of processors can tolerate less communcatons falure. However, n most of the prevous work only constraned expermental settngs are consdered. In contrast, we focus on relablty-drven. The paper focus on the case n large-scale resources, especally mult-processor falure, the relablty of the consderaton of dynamc schedulng, from nterdependent schedulng obectve, non-preemptve, to non-perodc tasks n real tme algorthm, named t-drfacs, attempts to mprove schedulng system relablty, whle the task schedulng algorthms to fully consder the tme and communcaton tme of nter-processor, makng the algorthm more realstc and more accurate. 3. System model and problem statement 3.1 Task Model The tasks and the relatonshps among them can be smulated by DAG (a weghted Drected Acyclc Graph), as shown n Fgure 1. Let T = (V, E ), where V s a set of nodes correspondng to all non-perodc, non-preemptve propertes of real-tme tasks. Meanwhle, the tasks are stll dvded nto ndependent and dependent ones. The ndependent one has no relatonshp connected wth others. The dependent task means that t s the result of a task whch wll affects the mplementaton of others. E s a set of edges correspondng to all the prorty relatons between tasks and communcaton between tasks. v = V s the number of nodes, e = E s the number of edges. In the DAG, f a node does not have predecessor node, t can be begnnng node. If a node does not have successor node, t can be end node. For a task, Sdp () represent the set of predecessor nodes. Sda () represents the set of successor nodes of task. We use w (, ) to represent the amount of data sent from the task to task. 3.2 Communcaton Model There are a set P = {P1, P2,, Pm} representng a set of processors n Dstrbuted computng system. It can be also dvded nto heterogeneous and homogeneous processor. Dstrbuted computng system s heterogeneous processors and these processors are fully connected. The lnk L kb shows that the processor R k and R b s connecton. We use ε :V P R to smulate heterogeneous computng tasks, and t means each processor on each task executon tme ε (, P ), 1 m. The property of communcaton heterogeneous can use W (, ) = v (, ) d (R k, R b ) to represent, whch means task map to processor. means requred for tme n sendng unt length of data. If the task s deployed to the same processor, the communcaton tme s zero. Task mappng matrx X s a 0-1 matrx, means the relatonshp between the tasks n DAG be mappng to the processors on the system. x =1 represents task be mappng to the processor P, and otherwse x = Relablty Model Snce many real-tme systems are non-determnstc envronment n whch even dangerous, system fault tolerance s necessary and mportant. In order to effectvely verfy the level of fault-tolerant systems, a relablty model needs to be proposed, t s generally assumed that the error arrval rate s stable and the error probablty at any tme ntervals meets Posson dstrbuton [7-8]. Thus, by the followng dervaton, the relablty mode wll be used to evaluate the performance of our proposed fault-tolerant schedulng strategy. We defne the relablty of the model s smlar to Lterature [9]. We assume that the probablty of permanent falure obeys the Posson dstrbuton and they are mutually ndependent. Lterature [10] proposed a K-Tmely Schedulng (K-TFT schedulng), whch thnk that there are K processors workng falure, but the process of the task schedulng meet the deadlne stll. Ths paper schedulng goal s that as schedulng mult-processor falure appears, we can reach K-TFT also. At the same tme, fault-tolerant schedulng for each task has a schedule matrx X = [x I ] m (Z+1), and assume ε +1 the copy of a task assgned to a processor, when a copy of the task assgned to the processor P, x I J = 1, the otherwse x I = 0, where = 1, 2,... (ε +1), = 1, 2,...m. Start f o l v p w g q u c d m The processor falure may occur durng dle tme, but the relablty of the model does not take nto account the processor falures n ths tme. It s caused by two reasons: frstly, n addton to affectng system relablty, the dle tme of the falure wll affect only the task completon tme; secondly, the use of a spare cell to replace the faled processor can be a good deal of processor falure, whch means that for relablty analyss of a smlar falure s not crtcal. Fgure 1. A Weghted Drected Acyclc Graph The relablty cost, C, of copy of task s decded by the 290 Journal of Dgtal Informaton Management Volume 10 Number 5 October 2012

3 falure rate λ of processors and executon tme of copy of task. Therefore, the relablty cost of processors s the sum of all copes of the relablty cost durng the task schedulng. Gven a falure rate vector = (λ 1, λ 2,... λ m ), a defnte schedule X and a task, the processor relablty cost can be represent: C (1) PN (, X, I = m z +1 λ x I c ) Σ Σ = 1 So, the sum of relablty cost of processors s: n C p = ΣI = 1 C (, X, I ) (2) PN Before estmatng the relablty cost of the connecton processors, we frst ntroduce a set of messages E kb, t contans all the messages from the processor R k to R b that s: E kb = {(v I, v I ) e > 0 x I b = 1 }, 1 k, b m, k b The falure rate of the connect processors s expressed as µ kb. The relablty cost of a message e E kb s decded by µ kb and w kb e. Therefore, the relablty cost of message can be calculated as µ kb x I k x I b w kb e = µ kb w kb e. Based on relablty cost of a message-based defnton, we can express the relablty cost of the connecton processors as C kb (M, X, I, J ). It s the total cost of the relablty of all nformaton through ths path, more specfcally t can be get as the followng equaton (4), where M s the matrx of connecton falure rate ([µ kb ] m m ). C kb (M, X, I, J ) = ( µ kb x I k x I b w kb e ) (3) z +1 Σ z +1 = 1 (4) Σ = 1 Due to the assumpton that the system contans n tasks, the relablty cost of the connecton processors s: n n Σ I = 1 ΣJ = 1, J I C kb = C kb (M, X, I, J ) Therefore, we can derve the total relablty cost of the connecton processors RCLINK, t s the connect relablty cost of all connectons, that s C LINK = m n Σ k = 1 Σb = 1, b k C kb Now, we can determne the cost of system relablty RC, whch s connected to all processors and the total cost of relablty. Therefore, the cost of system relablty can be expressed as: C sum = C p + C LINK (7) For a gven dstrbuted computng system, ts relablty can be expressed as: R(, Μ, X, I ) = exp ( C sum ) = exp ( C p ) ( C LINK ) (8) 4. Schedulng algorthm In ths secton, we propose a fault-tolerant schedulng (5) (6) algorthm, whch ams to ensure maxmum relablty under completon tme, and the task mappng process can tolerate any number of processor falures. DRFACS method use actve replcaton technque to assgn ε +1 copes of the task on to dfferent processors. The DRFACS s a greedy heurstc algorthms based on the mportance and relablty of tasks. Local DAG uses the dle task the length of the longest path to express the mportance of task. A task s dle, and f t does not dspatch and at the same tme, all of ts predecessors have been dspatched. S s a set of scheduled tasks, U s a set of not scheduled tasks, U f U s the set of dle tasks. Once a task beng scheduled to the processor, we wll get the start tme t,ε and completon tme t, f. Most real-tme schedulng usng prorty-based schedulng algorthm, ths algorthm assgns a prorty to the task, prorty schedulng at each schedulng tme wth the hghest prorty task executon. Accordng to prorty assgnment, real-tme schedulng can be dvded nto statc prorty schedulng and dynamc prorty schedulng. Statc prorty schedulng algorthm wth good mathematcs because of ts theoretcal bass, and can handle complex task model, s wdely regarded as the man hard real-tme system desgn bass [12-13]. Unlke statc prorty schedulng algorthms, dynamc prorty schedulng algorthm based on the task resource requrements to dynamcally assgn prorty, the am s to resource allocaton and schedulng wth greater flexblty to the dstrbuted computng system schedulng task executon. Therefore, the dynamc prorty schedulng polcy s more sutable for the schedulng of dstrbuted computng systems analyss and research. An dle task wth hghest prorty s a crtcal task. The prorty of a task by the H() + L() to decde, where H() means that the dynamc hgh level and L() means low level of statc, we can use the two methods [5-6] to calculate them. Because system does not know what the task assgned to the processor, we consder the worst case communcaton to use the average task executon tme m ε () = Σ ε (, P ) and the average communcaton tme W (, ) = v (, ) d between tasks, where d means that the system per unt length of data sent between processors, v (, ) means the bandwdth of node to node, the average delay. t P( ), means the task completon tme on the processor, that s the start tme on the task processor, whch contnued late n the task of all messages before the arrval processor, but also wll be avalable later than the processor avalable tme t P, eat = max s (x t P, f ). Therefore, the task s start tme: t P( ), a = max (max S (t P (), f + W (, )}, t P, eat ) (9) dp( ) To provde a good measure of the mportance of task: the more mportant task, along ths path the more the task of deployment. DRFACS consder the heterogeneous Journal of Dgtal Informaton Management Volume 10 Number 5 October

4 computng systems and followng obectves: (1) delay constrant, t s able to tolerate the permanent falure of any number of processors; (2) to accurately calculate the crtcal task to make the mportant tasks to be completed earler; (3) system relablty. Each step n the mappng process, DRFACS chooses a crtcal task at the same tme usng the followng equaton to smulate the mappng of tasks to all processors: 1 m, t P ( ), f = EXP [λ ε (, P ) + (1 λ) (10) Λ ({t P(u k ), f, W(u k, ), t P u, eat })] k Where λ means regulatory factor, Λ s the spendng tme mappng functon whch determned by average communcaton tme W(, ), executon tme t P( ), a,processor avalable tme t P, eat. Lemma 1: Under Actve Replcaton strategy, f and only f P( k ) P( k+1 ), k =1, 2,..., ε, the task has å processors n the case of permanent falures can ensure truly mplementaton. Proof: Because ε +1 copes of the task mapped to ε +1 dfferent processors, so f ε processors fal, then there s P( z ) (1 z ε +1) no falure and wll be executed successfully. However, f there s a processor P( u ) (1 u ε +1), and P( u ) = P( z ) = P w, when P w faled, then u and z could not be executed successfully. However, we found that start tme and relablty have conflct at sometmes, f fully consder the relablty, the task wll result n a longer response tme thus affectng the earlest start tme of predecessors of task. Only consder start tme wll affect the relablty of the system. Therefore, we ntroduce a parameter t r a () to lmt the task response tme, and t can lead to a balance between the start tme and relablty. t r a () = mn S () {max q S () {t P (q), f + W (q, )}} ds dq q (11) DRFACS algorthm: Step1: Parameters ntalze. Set the processors faled number for system support the maxmum number ε ; Calculate each L() of task n DAG, and ntalze H()=0 of each task ; Determne the system s processors set P = {P1, P2,, Pm}; Intalze S = Ø; U = V, dle task lst α = Ø; Let ntal task nsert the lst α ; Step2: Usng Dynamc Programmng method to calculate the scheduled tasks Select the hghest prorty task H(α); Intalzaton relablty r 0,and t P, f ; For each processor P do compute t P, ef usng equaton (10); calculate the earlest completon tme f tasks can be completed on P (z +1), and executon tme s less than t r- a () Usng equaton (8) to detect R to make sure the task n the processor P(z+1) and connecton relablty; f ((R > R) or (R = R ) and (t P, f > t P, ef ) Then schedule the task to the ε +1 processors; End for f the task s not possble for the processor, then return (FAIL); Step3: Update schedulng queue. The processor wll be assgned to the task when the relablty of processor reach the maxmum; take task nto S, at the same tme updatng the prorty of the predecessors; The dle predecessors of task wll be placed n α; Update U, U U(); Update nformaton of messages; Tme Complexty Analyss: For the tme complexty of the algorthm, t calculates bl (), whch takes O (e + v) (e s the number of edges and v s the number of nodes.), and nsert and delete nodes need the cost of O (log α ) ( α w, w s the DAG s wdth). Durng the executon of each task, the acton of nsert delete only once, so the management for á of tme complexty s O(vlogw). Analyss algorthm, we found that the maor computng costng at teraton. Loop need v tmes. The head node needs O (vlogw) to fnd. Computng tasks at the same tme the start tme or completon tme need O( S dp () (ε +1)m) (m s the processor number), the reason s that the predecessor of all copes n task need for testng on each processor P ( =1,2,,m) to detect. Also, because ε < m, the entre cost of v cycles s O(em2). Next, we want to update the prorty of task s drect successors, and the cost s O( S dp () ), the total cost n v tmes teraton s O (e). So the tme complexty of the DRFACS algorthm s O (em2 + vlogw). 5. Performance test In ths secton, we wll evaluate the performance of the proposed method. We randomly generated task graphs, the parameters desgnng n accordance wth the method of the lterature [Benot et al.2008]. We use three parameters to descrbe the resultng task graph: (1) the number of tasks from [100,150] range of unform samplng; (2) correspondng to each task nput/out edges s set to [1,3]; (3) The task graph of the computaton and communcaton rato: CCR (G) by ncrements of 0.2 from [0.2,2.0] range choosng, that s - the slowest of each 292 Journal of Dgtal Informaton Management Volume 10 Number 5 October 2012

5 Average overheads Average overheads edge of each task communcaton tme and the slowest executon tme rato, and f CCR (G) < 1, means that n contrast to the task executon tme t receves a small amount of communcaton tme. The number of processors s set to {20,40,80}, the falures number of processors range from {3,7}, these processors have dfferent processng speed, assumng that they obey the [1.0,10] unform dstrbuton whle ther falure rates can be balanced from the MINR and MAXR. In lterature [11], MINR means per hour , MAXR accordance wth the ncremental changes between and At the same tme, takng nto account the heterogeneous communcaton system, the connectons between task and messages capacty of the unt message passng delay can be samplng, respectvely, from the range [0.5,1] and [50,150] n the unform selecton. These crtera from the schedulng ablty, latency and relablty pont of vew to descrbe the performance of the algorthm. 5.1 Results and dscussons From schedulng, relablty and communcaton effcency three aspects, we compare the experment results. In order to obtan stable and accurate results, our experment was repeated 20 tmes to get average value. At the same tme, our dstrbuted computng system ncludes 20 processors, smlar to 40 and 80 processors wll have smlar results. We have to evaluate ths part of the mpact of CCR, whch represents the communcaton tme and executon tme rato. The hgh CCR value represents the relatve value of large executon tme, communcaton tme s rather large, CCR value s randomly generated. We present n ths Secton the performance results on the fault-tolerance heurstc. We compute the faulttolerance overhead n the followng way: In contrast to the applcaton s executon tme, schedulng tme wll be gnored [5-6]. F 1 F 2 Overheads = F 100,where F 1 s Fault Tolerant Schedule Length. F 2 1 non Fault tolerant schedule length. Fgure 2 (a, b) shows that As the CCR ncreases, DRFACS also gradually mprove the relablty, when the CCR value s very small, DRFACS the schedulng ablty better than FTSA, FTBAR. But wth the ncrease n the value of CCR, DRFACS performance was gradually reduced, and FTSA, FTBAR methods even worse than DRFACS trend. The results show that when CCR s small DRFACS would be much better than FTSA, FTBAR, when CCR s large, FTSA, FTBAR s also worse than DRFACS. The reason s the ncrease wth the CCR value wll ncrease the proporton of communcaton tme, FTSA, FTBAR have a strong focus on communcaton tme of our algorthm desgn process for communcaton tme and executon tme of balance, so they wll be slghtly better than the results DRFACS show that our algorthm s n the schedulng of an acceptable range. Fgure 3 (a, b) shows that wth the ncrease of CCR, DRFACS, FTSA and FTBAR delays have tended to declne. The reason s manly the ncrease of communcaton tme to reduce the executon tme wth the CCR s value ncreasng. When the three processors falure, DRFACS, FTSA and FTBAR relatonshp among the three tender to stable, and when falure occurs between the 7 processors, FTBAR volatlty s very strong. The DRFACS, FTSA clearly superor to FTBAR performance. Ths s manly because the factors are taken nto account wll affect the delay, the process nvolves the communcaton tme and executon tme of the relatonshp. Ths shows that wth the change n the number of processor falures, the performance of the proposed DRFACS method qute good performance, and good scalablty. Average overheads n the absence of processor falures DRFACS FTBAR FIST CCR 4 8 (a) Average overheads n the absence of processor falures DRFACS FTBAR FIST Number of operatons (b) Fgure 2. Schedulng capablty and CCR (a) (b) Fgure 3. Delay tme and CCR Journal of Dgtal Informaton Management Volume 10 Number 5 October

6 6. Conclusons Ths paper consdered the stuaton of mult-processor falure, and defned system relablty model, the tme constrants and the crtcal tasks. Based on these defntons, we proposed a relablty-based fault-tolerant schedulng algorthm. And from the schedulng, latency and relablty of the three aspects, comparng the smulaton results show that the proposed algorthm has good performance, especally to ensure that schedulng and delay, whle greatly mprovng the relablty of the system. Real-tme dstrbuted computng system faulttolerant schedulng algorthm s stll n an early stage, follow-up wll mprove and expand the relablty of the exstng dstrbuted computng system, and study the relablty of a hybrd schedulng model, whch contans a mxture of perodc task and non-perodc task model, and consder the relablty of communcaton between tasks. Our method ntegrate wth the physcal envronment at the same tme, and smulaton experment has not yet detected a problem from the algorthm, the future research can use real data for further testng the avalablty of the method n real computng envronment. 7. Acknowledges Ths work s supported by the Zheang Provncal Natural Scence Foundaton of Chna (Grand No. LR12F02002) as well as the Scentfc Research Fund of Zheang Provnce of Chna (Grand No. 2011C24008 and No. 2012C23121). References [1] Lng, Y., Luo, Z. S., Ge, Y. J. (2010). Fault-tolerant Schedulng Algorthm for Precedence Constraned Tasks n Grd Computng Systems wth Communcaton Effcency. In: Proc. the 3rd Internatonal Conference on Informaton Scences and Interacton Scences, p , Jun. [2] Xaomn Zhu, Chuan He, Rong Ge, Pezhong Lu.(2011)Boostng adaptvty of fault-tolerant schedulng for real-tme tasks wth servce requrements on clusters Orgnal Research Artcle.Journal of Systems and Software, 84 (10) , October. [3] Anne Benot, Mourad Hakem, Yves Robert.(2009). Contenton awareness and fault-tolerant schedulng for precedence constraned tasks n heterogeneous systems Orgnal Research Artcle. Parallel Computng, 35 (2) , February. [4] Grault, A., Kalla, H., Sghreanu, M., Sorel, Y. (2003). An algorthm for automatcally obtanng dstrbuted and fault-tolerant statc schedules. In: Proc. Internatonal Conference on Dependable Systems and Networks (DSN 03), p , Apr. [5] Benot, A., Hakem, M., Robert, Y. (2008). Fault Tolerant Schedulng of Precedence Task Graphs on Heterogeneous Platforms. In: Proc. IEEE Internatonal Symposum on Parallel and Dstrbuted Processng, p. 1-8, Apr. [6] Benot, A., Hakem, M., Robert, Y. (2008). Realstc Model and Effcent Algorthms for Fault Tolerant Schedulng on Heterogeneous Platforms. In: Proc. the 37 th Internatonal Conference on Parallel Processng, p , Sept. [7] Dongarra, J., Jeannot, E., Saule, E., Sh, Z. (2007). B-obectve schedulng algorthms for optmzng makespan and relablty on heterogeneous systems. In: Proc. the 19 th Annual ACM Symposum on Parallel Algorthms and Archtectures (SPAA 07), p , Jul. [8] Qn, X., Jang, H. (2001). Relablty-drven schedulng for parallel real-tme obs n heterogeneous systems. In: Proc. the nternatonal Conference on Parallel Processng, p , Feb. [9] Qn, X., Jang, H. (2005). A dynamc and relabltydrven schedulng algorthm for parallel real-tme obs executng on heterogeneous clusters. Journal of Parallel Dstrbuton Computaton, 65 (2) [10] Oh Y., Son S.H. (1997). Schedulng real-tme tasks for dependablty. Journal of Operatonal Research Socety 48 (6) [11] Qn, X., Han, Z., Pang, L., L, S. (2000). Desgn and performance analyss of a hybrd real-tme schedulng algorthm wth fault-tolerance. Journal of software, 11 (5) [12] Yang, C., Gu, W., J, L. (2003). A fault-tolerant schedulng algorthm of hybrd real-tme tasks based on multprocessors. Chnese Journal of Computers, 26 (11) [13] Xaomn Zhu, Chuan He, Kenl L, Xao Qn. (2012). Adaptve energy-effcent schedulng for real-tme tasks on DVS-enabled heterogeneous clusters. Journal of Parallel and Dstrbuted Computng, 72 (6), June, p Author bographes Yun Lng s a Full Professor. Hs maor research areas nclude computer networkng, dstrbuted computng and ubqutous/pervasve computng. He has led many natonal and nternatonal research proects and ndustral proects. He has researched on the ways of analyzng the performance of computng grd and put forward a range-based approach to dstrbuted computng performance. Y Ouyang s a Assocate Professor. Hs maor research areas focus on vsual computng subects n the areas of computer vson and pattern recognton. He has researched on obect detecton, trackng, pose estmaton, and recognton. Potental applcatons of hs research resde n the domans of human-computer nteracton, vsual survellance, and vdeo analyss and content extracton. 294 Journal of Dgtal Informaton Management Volume 10 Number 5 October 2012