Clustered Multimedia NOD : Popularity-Based Article Prefetching and Placement

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1 Clustered Multmeda NOD : Popularty-Based Artcle Prefetchng and Placement Y.J.Km, T.U.Cho, K.O.Jung, Y.K.Kang, S.H.Park, K-Dong Chung Department of Computer Scence, Pusan Natonal Unversty, Korea Abstract Accordng to the current profound development of multmeda and networkng technologes, the way people communcate wth, naturally, has evolved from a textorented nto a multmeda-orented. But, the VOD system technologes can t be easly appled to MNOD system because of the dfference of the basc data and ts propertes. Ths s the reason why we propose the MNOD system. NOD data composng news artcles make a dfference to VOD data n terms of meda type and sze, lfe cycle of artcles and frequency of clents nteracton. Because of NOD data's ntrnsc characterstcs, NOD artcle popularty model may be dfferent from that of VOD vdeos. Hence, we analyze statstcally the log data of one electronc newspaper and show the popularty dstrbuton of artcles s dfferent form Zpf s, whch s a popularty model of VOD data. We propose a new artcle popularty model for NOD data, whch we call Mult-Selecton Zpf dstrbuton. Also, we propose the artcle prefetchng polcy based on the popularty model and lfe cycle model of NOD artcles for ncreasng performance n a MNOD system. And, we consder the data placement on the MNOD system wth prefetch cache. The user requests can be servced from a cache or a dsk. Consequently, n order to place a NOD data, we must consder the correlaton between dsks and cache accordng to data popularty. In ths paper, we propose the data placement polcy that consders both dsk-exstence probablty and cache-exstence probablty of a data usng data popularty. Introducton. Motvaton As a VOD system gets more popularty these days, a NOD(News On Demand) system supportng multmeda servces, we call a MNOD (Multmeda NOD) system n ths paper, wll be popular as a news servce n the near future. Furthermore, the developments of computer network and multmeda technology are makng the multmeda-orented news servces possble. Prevous works generally regarded VOD and MNOD systems as an dentcal category from the vewpont of usng multmeda data. Multmeda data usually need hgh bandwdth and massve storage space and have real-tme crtera. Therefore, we bascally utlze the research results of a VOD system. Clearly, a MNOD system can be categorzed nto the same multmeda applcaton as a VOD system, but t has some ntrnsc aspects that a VOD system doesn't possess[4]. Frst, the artcles of NOD servce are made on and off n a day whle the VOD data s made once a halfmonth or a month. Second, the more recent artcle s made, the more users access the artcle. The good moves are preferred to the others for a long tme, but NOD data aren't. The NOD artcles, whch have the best popularty, don't last more than three days. Thrd, the number of artcles that are requested from each user s vared. Owng to the long length of vdeo data, users servced n VOD system select just one or two vdeo data. But the length of NOD data s short, as a result, users can select several data at a tme. Fnally, NOD data have the temporal and spatal access localty. The user requests burst accordng to the access tme and the knd of artcles. So the VOD system can't be fully appled to a MNOD system because of these dfferent characterstcs. In short, we use the VOD system research results bascally, but wll revse and decorate t. The prevous work showed that the system wth prefetchng has the better performance than the system wthout prefetchng[, 5]. But wthout consderng the other characterzatons such as short-term lfe cycle, the dfference of user s access pattern accordng to tme and etc, we can not expect to the ncreased performance n the MNOD system. The system wth prefetchng only based on data popularty has the problem, whch s data replacement happens too often. Hence, usng the analyss of electronc newspaper logfles, we propose the popularty model and the lfe cycle model of NOD artcles. And we wll suggest the artcle prefetchng polcy based on tme wndow accordng as the models. And then we consder the data placement on system wth prefetch cache. The user requests can be servced from a cache or a dsk. Consequently, n order to place a MNOD data, we must consder the correlaton between dsks and cache accordng to data popularty. In ths paper, we wll propose the data placement polcy that consders both dskexstence probablty and cache-exstence probablty of a data usng data popularty. The remander of ths paper s organzed as follow: In secton 2 the overall archtecture of a proposed MNOD system s presented brefly. Secton 3 shows the popularty model and lfe cycle model of NOD artcles va the statstcal analyss of log fles of electronc newspapers, and n secton 4 we propose the artcle prefetchng polcy based on the popularty and lfe cycle model of NOD artcles. In secton 5 4

2 the artcle placement polcy n the MNOD system wth prefetch cache s proposed and evaluated. Lastly, n secton 6 we menton the concluson and future works..2 Related work Data cachng and Prefetchng In order to establsh cachng polcy for NOD data, the popularty and lfe cycle model are mportant condtons. [] showed that the popularty dstrbuton of real vdeo was a double exponental dstrbuton rather than a Zpf s and proposed cache polcy based on the lfe cycle of vdeos. In [], the long-term model of move popularty was proposed for a herarchcal VOD system, whch was the modfed exponental dstrbuton wth three parameters. The strateges of these papers are not acceptable for MNOD system because these analyzed only a VOD data, whose lfe cycle s longer than that of NOD artcle. [] made a psychologcal analyss of access pattern of users, based on a model on human memory and nssted that the recency of data was more mportant crteron n data cachng algorthm than the frequency. But because of varous characterstcs of NOD artcles, consderng only access recency s nsuffcent for cachng or placement of artcles. [4] proposed a news artcle prefetchng polcy but had not the correct crteron for the data replacement. In ths paper, we wll show the measure of short-term and long-term of artcle popularty. And we wll propose artcle prefetchng polcy consderng that measures. Data placement In Multmeda system, data placement polces have been studed so as to ncrease the success rato of user requests. Therefore, most studes were contnued to get the hgh dsk bandwdth. Data Strpng method s the representatve placement polcy. The famous strpng methods are round-robn, random[3], staggered[7], permutaton strpng[8] and etc. And then, the polces to prevent dsk bottleneck, such as dynamc replca polcy[5] and data placement polcy based on popularty were proposed. But these polces consdered only dsk condtons based on data popularty. But n these days, snce almost all systems have data cache, the user requests are servced from a cache or a dsk. Consequently, n order to place data, we must consder the correlaton between dsks and cache accordng as data popularty. 2. MNOD system overvew Though a NOD(News On Demand) system has many dfferences from the well-known VOD(Vdeo On Demand) system, a NOD system s stll, n many aspects, smlar to a VOD system n terms of usng multmeda data. Hence, we can draw out the structure of a MNOD system usng that of a VOD system. And the scalablty of an MNOD system has to be nvestgated to support hundreds of thousands of smultaneous users. As shown n Fgure, we suggest the two-layered clustered MNOD server system by consderng the scalablty. The suggested system structure contans two man modules: a control node and a storage node. Many studes on the VOD system archtectures, theoretcally, assume that there are two logcal parts n the server[, 2, 5]. Roughly speakng, the control node deals wth the admsson control for the storage node and the storage node schedules the admtted requests from the control node and treats storng and retrevng data. The control node s entrusted wth the control part of the whole MNOD server. It controls the permsson nto enterng the storage node. If a NOD stream arrves at the control node, the admsson manager checks f the requested stream may be admtted or not and whenever a request s permtted, t s passed from the control node to the storage node. The storage node s responsble for both storng and retrevng NOD data. Now that the storage node only schedules the permtted streams from the control node, no &RQWUROÃRGH 6WRUDJHÃRGV Fgure. Proposed clustered MNOD system specal admsson control s requred. 3. Artcle popularty model &RQWUROÃRGH NOD data composng news artcles make a dfference to vdeo data of VOD n terms of meda type and sze, lfe cycle of artcle and frequent clents nteracton etc. And, NOD data may be created on and off at any tmes, and t s popular and recent artcles that most of the clents access manly. Because of these characterstcs, NOD artcle 5

3 popularty model may be dfferent from that of VOD move. In ths secton we gve a statstcal analyss of log data of electronc newspaper runnng currently, and show that the popularty dstrbuton of artcle s dfferent from Zpf s, whch s a popularty model of VOD. In partcular, we propose a new artcle popularty model, whch we call Mult-Selecton Zpf dstrbuton, and the algorthm generatng ts probablty dstrbuton. Ths artcle popularty model s a short-term popularty model that denotes popularty dstrbuton among artcles n an nstant tme. Whle the short-term popularty model notfes what artcles are prefetched, long-term popularty models, denotng the lfe cycle of artcles, notfy when the artcles are prefetched. In addton, these models can be used for cachng and placement of artcles. Thus, n ths secton we also present a long-term popularty model of NOD artcles. 3. Mult-Selecton Zpf dstrbuton (a) Average access popularty NOD data Zpf Exponental (b) Comparson wth other dstrbutons Fgure 2. The real access dstrbuton of NOD artcle The typcal popularty model of VOD data s Zpf dstrbuton; t denotes access probablty of the -th popular move when the moves are sorted n the order of decreasng popularty. Then, do the popularty of NOD data follow a Zpf dstrbuton? We analyzed statstcally the log fles of electronc newspapers runnng currently and found that news artcle s popularty model s dfferent from Zpf s. In Fgure2, (a) shows the mean access popularty of news artcles for a month, resulted from makng a statstcal analyss of log data of June 8. (b) shows the comparson of NOD artcle popularty wth Zpf s and Exponental s; The upper porton of NOD artcle dstrbuton decrease less drastcally than Zpf s but more fast than Exponental s. When a user connects wth a NOD server, the user requests several artcles rather than only one artcle. Ths mply that popularty does not concentrate on one artcle but spread over several artcles. Consequently the curve of popularty dstrbuton of NOD artcles s less steep than Zpf s. For comparson, we use a pure Zpf dstrbuton wthout skew and an exponental dstrbuton wth λ =.. A user does not watch only one artcle but several artcles whle he s connected wth a NOD server. If a user selects k artcles on average n NOD server wth N artcles, the possble number of k artcle selectons s as follow: N N! Μ = = k k!( N k)! In other words t may be thought that he selects a group composed of k artcles n Μ artcle groups. Suppose that the popularty of these Μ artcle groups follow a Zpf dstrbuton, the probablty on whch each artcle s selected s the normalzed sum of probabltes of groups contanng the artcle. The defnton of Mult-Selecton Zpf dstrbuton and the algorthm generatng the probablty dstrbuton are as follows: Defnton: Suppose that a user selects k artcles on average n NOD server wth N artcles and the popularty of the Μ artcle groups follows a Zpf dstrbuton, we defne artcle access probablty as the normalzed sum of probabltes of artcle groups contanng each artcle. We call the dstrbuton of artcle access probablty Mult- Selecton Zpf dstrbuton Input: k (the number of selectons), N (the number of artcle) Algorthm: Generate Μ artcle groups; Decde popularty of the groups and sort n Popularty order; Calculate the populartes of groups usng Zpf dstrbuton; whle (all artcles) { addng the probablty of groups contanng the artcle; normalze such that the sum of calculated probabltes s ; Fgure 3. Mult-Selecton Zpf probablty generaton algorthm Fgure4 compares Mult-Selecton Zpf dstrbuton wth Zpf s; (a) shows that the curve of Mult-Selecton Zpf s s less steep than Zpf s. In (b), as the number of 6

4 access popularty (a)comparson of k=5 wth Zpf Mzpf(K3) Mzpf(K5) (b)comparson of k=5 wth k=3 Fgure 4. Mult-selecton Zpf dstrbuton and Zpf s averg Mzpf(K5) hour (a) Access Popularty as artcle lfe cycle.2 access popularty.5..5 atrcle A artcle B hour (b) Lfe cycle of two artcles Fgure 6. Artcle lfe cycle selectons s 3 and 5, It shows that the dstrbuton wth 5 selectons decrease slower than 3 selectons. That mples that the more selectons are, the less steep the curve s. Fgure5 shows the comparson of the real popularty resulted from log fles wth Mult-Selecton Zpf s wth 5 selectons. 3.2 Long-term popularty model Fgure 5. Mult-selecton Zpf dstrbuton & the real dstrbuton of NOD artcle The popularty of artcles changes as tme passes because new artcles are generated at any tme and users 7 prefer new artcles. Long-term popularty model, called as artcle lfe cycle, reflects the changes of the popularty of an artcle. In Fgure6, (a) shows a long-term popularty dstrbuton as a result of statstcal analyss of log fles; t has left skew, whch shows that users prefer new artcles to the old ones. Long-term popularty model can be used for decdng the tme to prefetch an artcle nto the cache to decrease the retreval overhead. In Fgure6 (b), the popularty of artcle A decrease and artcle B ncrease, and the best pont that the

5 artcle A should be replaced wth artcle B n the cache s around the hour, when the curves of two artcles meet. 4. Artcle prefetchng polcy In prevously suggested clustered MNOD server, clents request artcles from CSVR(Control Server), and CSVR fetches artcles from SSVR(Storage Server) and sends them to clents. If popular artcles are prefetched n CSVR, the servce delay can be reduced and network traffcs can be decreased by elmnatng the tme to retreve artcles from SSVR[5]. In prefetchng popular artcles, old artcles must be replaced wth new popular ones when new artcles are generated and the populartes of old artcles decrease. So, we need to consder two popularty models mentoned n the prevous secton for better performance n artcle prefetchng. In ths secton we present the new prefetchng measure combned two popularty models and suggest the artcle prefetchng algorthm employng the measure. 4. The measure of prefetchng We express the measure of short-term artcle popularty n terms of the access frequency durng some tme ntervals and the measure of long-term artcle popularty n terms of the ncreased or decreased amount of frequency durng some tme nterval. For example, the artcle whose access frequency ncrease rapdly may be a new artcle whle the artcle whose access frequency decrease may be less old one and the artcle that access frequency hardly decrease may be old one. Note that we use tme wndow whose nterval s several ten mnutes and calculate f, the access frequency, by cumulatng references to the artcle durng any wndow. By takng a weghted average of fluctuatons n all the wndows, we can calculate a, the expected varaton amount of access frequency n next wndow as follows: a [Eq.] = sf ( f f ) + ( sf ) a In Eq, sf, denotng a smoothng factor, s the weght that ndcate the mportance of frequency varatons of current wndow; The larger sf s, the heaver the weght of the current wndow s frequency s. Consequently we propose a new measure LBF (Lfe-cycle Based Frequency) by combnng f wth a as follows: LBF = f + a [Eq.2] 4.2 Prefetchng scheme CSVR mantans logs of reference to each artcle durng each wndow. From the logs, access frequency f of each artcle n the wndow can be calculated. Compute a usng [Eq.] and LBF of each artcle usng [Eq.2]. Because the artcle that has the largest LBF s expected to be accessed most frequently n next wndow, the artcles that have large LBF have to be prefetched. Once artcles are prefetched n CSVR, the artcles can t be replaced wth others durng current wndow. Fgure7 shows the prefetchng algorthm. whle () { f (a new wndow) { whle (for all artcles n wndow) { a = sf ( f f ) + ( sf ) a sort the artcles n LBF = f + r LBF order; prefetch the artcles that have large untl cache memory s full; Fgure 7. Artcle prefetchng algorthm 4.3 The effects of prefetchng LBF In the proposed clustered MNOD server, the prefetchng n CSVR promotes the effcency of the server system because of decreasng the overhead of retrevals from dsks n SSVR. We employ the mss rate and the concurrent users to show the effects of prefetchng. For smulaton, we used the log fles of Pusanlbo s electronc newspaper n Korea, and the parameters expressed n Table. Table. Smulaton parameters Parameter Block sze The data rate that a request requre The data sze that a request requre The bandwdth of Storage Server Values 256k M/s 3M 6M/s Fgure8 shows the relatons between the amount of prefetchng and the mss rate; as the amount of prefetchng ncreases, the mss rate decreases. Fgure also shows that as the prefetchng amount ncreases, the number of concurrent users by LBF scheme and ones only ncludng prefetchng overheads. Because prefetchng an artcle s equal to processng a request, the overheads of artcle prefetchng are calculated by multplyng the number of prefetched artcles and the overheads that a request requres. Thus the real effect of artcle prefetchng s expressed as the dfference between the number of concurrent users by LBF scheme and 8

6 the number of concurrent users only ncludng prefetchng M 2M 3M 4M 5M overhead. As a result, prefetchng artcles n CSVR ncreases the performance of MNOD server system by.5 tmes. 5. Artcle Placement Polcy 5. Prevous Data Placement mss rate Fgure 8. Mss rate accordng to prefetchng sze Cuncurrent Users C.U ncludng the overhead M M 2M 3M 4M 5M Fgure. Concurrent users accordng to prefetchng sze We ntended to ncrease user servce throughput by arrangng replca of popular data on dsks. Ths method, however, had a few problems. NOD artcle s not as bg as VOD data n sze. In MNOD system, relatvely large amount of data exsts n the cache, and the probablty that hot data s n the cache s hgher than VOD system. Naturally, dsk access probablty wll be lower relatvely. And, the number of NOD artcles s very much than VOD data, and by reason of storage overhead the number of artcles beng replcated needs to be restrcted to artcles wth hgh popularty. Thus, the data that has lower popularty but more growng probablty of dsk access wll be suffered from long servce delay because of small number of replca due to low popularty. 5.2 New Scenaro of Data Placement The new scenaro we suggest for data placement n the MNOD system s to manage varous numbers of replcas accordng to data popularty. The artcle data wth hgher popularty wll be cached by some cachng method based on data popularty and have lower probablty of dsk access. The artcle data that has lower popularty but more growng probablty of dsk access needs hgher dsk I/O bandwdth. So, more many of replcas need to be assgned for artcle data wth lower popularty. By reason of ths, we emphasze the artcle data that has relatvely lower popularty than some hot data. 5.3 Artcle Placement Algorthm 5.3. Determnng the number of artcle replca Artcle data s arranged n decreasng order accordng to ts popularty and has an dentty number of ts own that s able to be dscerned from other ones. We wll bestow the replcaton level, T, on artcle data a n order to determne the number of replca. The replcaton level T s the nverse of dfference between mean artcle popularty and popularty value of artcle data. Ths algorthm also takes advantage of standard dervaton for assgnng largest value of T to the data that has to be repeated many tmes. And, by that result, the average s shfted. We can express T as [Eq.3] by ntegratng the expressed mentons above E( X) + cs ( X) - p( x ), -5. ˆ c ˆ5. where x : dentty of artcle data px ( ) : popularty of x X : random varable of p( x ) E( X) : expected value (mean) of X s ( X) : standard devaton of X Artcle placement on dsk [Eq. 3] Artcle data conssts of several data blocks that are unts of placement. We place artcle data on the dsk usng round-robn method and the dsk where the frst block of news data s placed s selected randomly. If replcas of artcle data exst, we wll place them usng the same scheme, but we must utlze a random seed for seekng a start dsk that s not the same dsk storng orgnal artcle data, so as to secure hgher dsk I/O bandwdth from dsk access collson. Fgure shows the artcle placement algorthm proposed n ths paper and the

7 parameters used n the algorthm are expressed n Table 2. Table 2. Parameters of artcle placement polcy C The number replcas of each artcle data, x Bnews The number of blocks of each artcle data D Dsk number Tdsk Total amount of dsk S Storage capacty for dsk (the amount of dsk blocks) s The amount of block occuped on the dsk, D s = ; whle ( less than total number of news data) { whle ( less than C ) { D s ntalzed by random functon; whle (less than B ){ news whle () { f ( s <= S ) { dsk store a data block on the dsk D ; /* for usng round-robn method */ ncrease D by one ; D = D % T ; dsk ncrease s by one; break; else { /* dsk D s full */ ncrease D by one ; D = D % T ; dsk Fgure. Artcle placement algorthm Processng flow of user request If the user s request arrves at a CSVR(Control Server) n Fgure, the CSVR wll dvde the request nto several servce blocks and look nto whether the request exsts n cache memory or not wth server control nformaton. If the request exst n cache memory, the user request s served from cache memory. Otherwse, retreval requests of blocks wll be sent to the SSVR(Storage Server) havng requested blocks and be servced form dsks. In order to reduce the servce delay of user request, the SSVR wll process each block as follows: Frst, the SSVR wll look for dsks that have the block and next, compare the length of the queue of each dsk to seek the dsk that has the mnmum length of ts own queue. Fnally, the SSVR assgns the block request to the mnmum loaded dsk. Therefore, by usng ths method load balancng can be acheved among dsks, and the servce delay s decreased. Fgure s a flowchart of user request processng. Fgure. Flowchart of servce procedure for user requests 5.4 Evaluaton of artcle placement polcy 5.4. Smulaton envronment We enforced smulatons n Sun Sparc staton-2. We supposed that user requests requre Mbyte/s capactes of dsk I/O bandwdth, that the sze of data whch s compressed n MPEG II s Mbytes ( seconds), and that the number of data s restrcted to forty. One block sze of dsks s 256K and 24 blocks are processed wthn one second. The volume of a dsk s Gbyte and the number of dsks s 4 ths sze accommodates total data as twce Evaluaton of Smulaton We examned the servce falure rate (deadlne mss request/total servce request) when the popularty level beng replcated fully s changed nto the hgher level %~%, %~2%, and 2%~3%. Fgure2 shows the servce falure rate of a MNOD system wth a cache. The total servce performance s better than that of no cachng system. In ths case, artcles wth the second best popularty have the hghest dsk access probabltes because the cache ht ratos of them are lower. 2

8 Thus, mantanng the fully replcated popularty level to the hgher level %~2% makes the best performance. W D U Ã H U X O L D I Ã H F L Y U H 6 Fgure 3. Servce falure to every dsk n cachng system Fgure3 present servce falure rates of each dsk when the number of concurrent users s 6 and. The results show that the proposed artcle placement polcy controls the load balance of every dsk as well. 6. Concluson )X O\ÃUHSOLFDWHGÃSRSXODULW\ÃOHYHO Fgure 2. Servce falure to amount of users Servce falure rate $PRXQWÃRIÃXVHUV %~% () %~% (6) % % 2%~3% 2%~3% ~2%() ~2%(6) () (6) Fully replcated popularty level In ths paper we propose popularty-based artcle prefetchng and placement polcy for NOD artcle data. Because NOD data composng news artcles make a dfference to vdeo data of VOD n terms of meda type and sze, lfe cycle of artcles and frequency of clents nteracton, a new NOD artcle popularty model s needed to explan user access patterns. As a result, we show that the popularty dstrbuton of artcles s dfferent from Zpf s, whch s a popularty model of VOD data. In partcular, we propose a new artcle popularty model, whch we call Mult- Selecton Zpf dstrbuton. The proposed model s the shortterm popularty model that denotes popularty dstrbuton among artcles n an nstant tme. If the artcle prefetchng algorthm may be consdered, whle the short-term Total dsk dsk2 dsk3 dsk4 popularty model notfes what artcles are prefetched, longterm popularty model, denotng the lfe cycle of artcles, announces when the artcles are prefetched. Thus, we propose the artcle prefetchng polcy based on the popularty and lfe cycle model of NOD artcles for ncreasng performance n a MNOD system. And, we propose the data placement polcy for NOD data that consders both artcle popularty and dsk access probablty. In ths polcy, we replcate data that have not best popularty, but hgh dsk access probablty because the probablty that the best popular data s n cache memory s hgh and relatvely t's dsk access probablty s low. And, orgnal data and replca are placed to mnmum loaded dsks to acheve load balancng among dsks. The proposed artcle placement polcy decreases the servce falure rate and ncreases the servce throughput by way of load balancng among dsks. Our future work s to develop a mathematcal model of Mult-Selecton Zpf dstrbuton and to nvestgate artcle placement and prefetchng algorthms n clustered archtecture envronment. Reference [] Lous P. Slothouber, "A Model of Web Server Performance", modelpaper.html, 6. [2] Odysseas I. Pentakalos, Danel A. Menascé, "An Approxmate Performance Model of a Untree Mass Storage System", 4 th IEEE Symposum on Mass Storage Systems, pp , Sep.5. [3] J. Alemany, J. S. Thathacher, Random Strpng for News on Demand Servers, Techncal Report UW-CSE , Unv. Washngton, 7. [4] Y. U. Park, G. H. Back, W. I. Seo, Y.J. Km, K. D. Chung, New Buffer polcy for NOD data, Conference on Korea Broadcast Engneerng, Korea, 7. [5] A. Dan, M. Kenzle, D. Staram, A dynamc polcy of segment replcaton for load balancng n vdeo-ondemand servers, Multmeda System 3:3-3, 5. [6] T. D. C. Lttle, D. Venkatesh, Populary - based assgnment of moves to storage devces n vdeo-ondemand system, Multmeda System 2:28-287, 5. [7] S. Berson, R. Muntz, S. Ghandeharzadeh, X. Ju, Staggered Strpng n Multmeda Informaton Systems, SIGMOD Conference, pp. 7-, 4. [8] R. Flynn, W. Tetzlaff, Dsk Strpng and Block Replcaton Algorthm For Vdeo Fle Servers, IEEE 3 rd Internatonal Conference on Multmeda Computng Systems, 6. [] J. E. Ptkow, M. M. Recker, A Smple Yet Robust Cachng Algorthm Based on Dynamc Access Patterns, 2

9 In the Proceedng of the Second Internatonal WWW Conference, 7. [] S. A. Barnett, G. J. Ando, H. W. Beadle, Cachng Polces n a Dstrbuted Vdeo-on-Demand System, In the Proceedng of Australan Telecommuncaton Networks and Applcatons Conference, 5. [] C. Grwodz, M. Bar, L. C. Wolf, Long-term Move Popularty Models n Vdeo-on-Demand Systems or The Lfe of an on-demand Move, The ffth ACM Internatonal Multmeda Conference, 7. [2] I. Tatarnov, V. Solovev, A. Rousskov, Statc Cachng n Web Servers, submtted to the 6 th nternatonal Conference on Computer Communcatons and Networks. [3] Lee D., Cho J., J. Km, S. H. Noh, S. L. Mn, Y. Cho, C. S. Km, LRFU Replacement Polcy: A spectrum of block replacement polces, In Seoul Natonal Unversty Techncal Report SNU-CE-AN-6-4, Korea, 6. [4] Renu Tewar, Harrck M. Vn, Ast Dan & Dnkar Staram, Resource Based Cachng for Web Servers, Proceedngs of SPIE/ACM Conference on Multmeda Computng and Network, 8. [5] Renu Tewar, Rrajat Mukherjee, Danel M. Das, Harrck M. Vn, "Real-Tme Issues for Clustered Multmeda Servers", IBM Research Center, Techncal Report, 5. 22