Dynamic Processor Scheduling with Client Resources for Fast Multi-resolution WWW Image Browsing

Transcription

1 Dynamic Pocesso Scheduling with Resouces fo Fast Multi-esolution WWW Image Bowsing Daniel Andesen, Tao Yang, David Watson, and Athanassios Poulakidas Depatment of Compute Science Univesity of Califonia Santa Babaa, CA fdandese, tyang, david, Abstact WWW-based Intenet infomation sevice has gown enomously duing the last few yeas, and majo pefomance bottlenecks have been caused by WWW seve and Intenet bandwidth inadequacies. Utilizing client-site computing powe and also multi-pocesso suppot at the seve site can substantially impove the system esponse time. In this pape, we examine the use of scheduling techniques in monitoing and adapting to wokload vaiation at client and seve sites fo suppoting fast WWW image bowsing. We povide both analytic and expeimental esults to identify the impact of system loads and netwok bandwidth on esponse times and demonstate the effectiveness of ou scheduling stategy. 1 Intoduction One of the fundamental oles fo Wold-Wide Web (WWW) bowses and seves in today' s envionment is to povide a unifom inteface fo the on-line access of thousands of digitized documents, such as images. The main pefomance bottlenecks ae seve computing capability and Intenet bandwidth. We examine these two issues unde the scope of WWW-based digital libay (DL) systems [12]. While multi-pocesso suppot fo a seve is citical fo a popula WWW site [3], tansfeing pat of the seve's wokload to the client is also possible since the cuent Web bowses have achieved the ability to download executable content. Taking advantage of multipocesso suppot with client esouces can lead to significantly impoved use intefaces and esponse times. An application of the seve-client load shifting is in digital image bowsing. The cuent collections of the Alexandia Digital Libay (ADL) poject at UCSB [1] involve geogaphicallyefeenced mateials, such as maps, satellite images, digitized aeial photogaphs, and associated metadata. The image size is lage, fom MB. In such a case, it is obviously impactical to send all the images matching a quey in thei entiety. The seve must send lowesolution images fo the use to futhe cull befoe highesolution final vesions ae deliveed. A technique fo educing the netwok bandwidth equiements in viewing high-esolution images is to let the client fist bowse lowesolution thumbnails, then econstuct high-esolution images fom the existing thumbnails with a small amount of additional data deliveed fom the seve. This technique is called pogessive image delivey and equies cetain computing esouces at client sites. The main eseach challenge is the effective management and utilization of esouces fom multi-pocesso WWW seves and client-site machines. Blindly tansfeing load onto clients may not be advisable, since the bytecode pefomance of Java is usually 5-10 times slowe than a client machine' s potential. Also a numbe of commecial copoations ae developing netwok computes, with little o no had dive, a minimal pocesso, but with Java and the Intenet netwoking built in. A caeful design of the scheduling stategy is needed to avoid imposing too much buden on these Net PCs. In this pape we pesent a scheduling model fo patitioning and mapping client-seve computation based on dynamically changing seve load and client-site compute capabilities. Ou model incopoates multiple factos including disk, netwok, and computational abilities. We show how pope scheduling can lead to significantly impoved esponse times ove most cuent implementations in suppoting WWW image bowsing. The pape is oganized as follows: Section 2 gives the backgound on pogessive image bowsing. Section 3 pesents the patitioning of wavelet-based image accessing opeations. Section 4 discusses the scheduling stategy fo a multi-pocesso WWW seve and the cost modeling fo a wavelet task. Section 5 gives an analysis of the esponse time pefomance fo pocessing a fixed numbe of equests. Section 6 pesents the expeimental esults. Section 7 discusses elated wok and conclusions. 1

2 2 Multi-esolution image bowsing We focus on bowsing lage digitized data objects, e.g. images in the ADL system. With cuent netwok speeds, it is quite infeasible to conside sending the full contents of an image file to uses fo the bowsing puposes. The ADL has adopted pogessive multi-esolution image delivey and subegion bowsing as stategies to educe Intenet taffic when accessing map images [1]. This appoach is based on the idea that uses often bowse lage images via a thumbnail (coase esolution), and desie to apidly view highe-esolution vesions and subegions of those images aleady being viewed. We biefly descibe the techniques of wavelet image data etieval and tansfomation fo multi-esolution bowsing. Given an image, a fowad wavelet tansfom poduces a sub-sampled image of lowe esolution called a thumbnail, and thee additional coefficient data sets. Moe fomally, fo the given quantized image I 1 of esolution R R 1, we specify the input and output of the fowad wavelet tansfom as follows. (I 2 ; C 1 ; C 2 ; C 3 ) = F owad W avelet(i 1 ) I 2 is the thumbnail of esolution R 2 R, C 2 1; C 2 and C 3 ae of esolution R 2 R. Figue 1 depicts the esult of wavelet 2 tansfom. be econstucted at the client site. The image econstuction is not time consuming, taking about 1.5 seconds fo a image on a SUN SPARC 5. The size of compessed data C 1 ; C 2 ; C 3 to be tansfeed is geneally in the ange of 10 to 100KBytes, which takes less than 1 second ove a T1 link. If a use wishes to access subegions of an image I 1, then the coesponding subegions in thumbnail I 2 ; C 1 ; C 2 ; C 3 can be etieved and the econstuction pefomed accodingly. We model such a pocess as follows. subegion(i1) = Inv W(subegion(I2); subegion(c1); subegion(c2); subegion(c3)): A detailed definition of fowad and invese wavelet functions can be found in [8]. The time complexity of wavelet tansfoms is popotional to the image size. The wavelet tansfom can be applied ecusively, namely the thumbnail I 2 can be decomposed futhe to poduce smalle thumbnails. The actual wavelet implementation fo data access and image econstuction is a combination of Java and Computational Gateway Inteface (CGI) pogams [13]. Namely, an HTTP equest activates a CGI pogam at the seve, which computes the seve' s data using the pedefined pocedue. The esults ae sent to the client and then pocessed by the Java client-side application. Oiginal view subimage selection wavelet decomposition 3 Task patitioning fo pogessive subegion image delivey Reconstuction Seve side seve disk image data extact subegion eceate coefficients Thumbnail econs. pictue D 1 D 2 D 3 D 4 memoy view pictue side highe-esolution subimage Figue 1: Reconstucting a high-esolution subegion fom the thumbnail and coefficients. The invese wavelet tansfom (Inv W ) can be pefomed to e-constuct the oiginal image on-the-fly fom the coefficient data sets and the thumbnail. I 1 = Inv W (I 2 ; C 1 ; C 2 ; C 3 ): If image thumbnail I 2 is available at the client site, then by equesting that the seve sends C 1 ; C 2 ; C 3, image I 1 can 1 Rectangula shapes can also be suppoted while squae images ae used hee fo demonstation. Figue 2: A task chain fo wavelet image enhancement. Fou cutoff points ae depicted fo possible computation patitioning between client and seve. If the client pefoms the image econstuction, the thumbnail is aleady available fom the client memoy and does need to be tansmitted fom the seve. The computation involved in multi-esolution image constuction can be patially executed at a seve and at a client also. Based on an implementation in [15], we model the computation and communication involved using a chain of subtasks depicted in Figue 2: 1) Fetching compessed wavelet data and extacting the subegion. The wavelet image data is stoed in a combined quadtee/huffman encoded fom on a disk. These compessed files must be fetched. Then the appopiate subtee of a quadtee with its associated compessed coefficient data must be extacted in its compessed fom. The compessed

3 coefficient data is sent on to the next stage. 2) Receating the coefficients. The compessed coefficients must be expanded to thei oiginal fom. 3) Reconstucting the pixels. Afte the coefficients ae available, the invese wavelet function is called to ceate the new highe-esolution image fom the thumbnail image. Notice that the thumbnail image needs to be fetched fom the seve disk if the econstuction is conducted on the seve. Othewise, the thumbnail image is aleady available on the memoy of the client machine. 4) Viewing the image. Fo ou puposes, we assume the viewing of the image takes no computation time, and must be done on the client. Figue 2 depicts the above pocessing steps and fou possible cutoff points fo patitioning this chain fo the seve and client. We discuss the possible computation and communication scenaios fo fou patitioning points below. Notice that we also need to conside that the data sent fom the seve to the client may be compessed fist fo tansmission, then decompessed at the client site. D1: The client stats fom subegion extaction. The entie compessed image data needs to be tansfeed, but the image thumbnail does not need to be tansmitted. The tansmitted wavelet data is not futhe compessible. D2: The client stats fom coefficient data eceation. A pat of compessed image data is etieved on the seve based on the subegion position. The image thumbnail does not need to be tansmitted. The tansmitted subimage data is not futhe compessible. D3: The client stats with image econstuction. Coefficient econstuction is conducted at the seve site. But the deived subegion coefficient data must be futhe compessed othewise the size of uncompessed coefficient data is simila to that of the oiginal subegion image and it would be moe efficient to send the oiginal image. Thus the ovehead of seve compession and client decompession must be incopoated. The image thumbnail does not need to be tansmitted. D4: The client does not do any computation. The image thumbnail needs to be etieved fom the seve disk. The esult of image econstuction is not compessible futhe. 4 Pocesso scheduling fo WWW equests and cost modeling fo wavelet tasks Ou WWW seve consists of a set of wokstation nodes connected with a fast local netwok as shown in Figue 3 and is pesented as a single logical seve to the Intenet. The use equests ae fist evenly outed to pocessos via DNS otation [13]. The seve nodes in the system communicate with each othe and ediect equests to the pope node by actively monitoing the usages of CPU, I/O channels, and the inteconnection netwok. Ou scheduling stategy fo ediecting a equest to an appopiate seve node is to find a pocesso that minimizes the oveall esponse time. In [3], we have poposed Intenet HTTP equests DNS Disks WWW seve Fast netwok Figue 3: The achitectue of a multi-pocesso WWW seve. a pediction model fo appoximating the pocessing time of a given equest on a pocesso. In this model, we conside seveal factos that affect the esponse time. These include loads on CPU, disk, and netwok esouces. The load of a pocessing unit must be monitoed so that equests can be distibuted to elatively lightly loaded pocessos. Disk channel usage must also be obseved and simultaneous use equests accessing diffeent disks can utilize paallel I/O to achieve highe thoughput. The local inteconnection netwok bandwidth affects the pefomance of file etieval since many files may not eside on the local disk of a pocesso, so emote file etieval though the netwok file system will be involved. Local netwok taffic congestion could damatically slow the equest pocessing. Fo a wavelet task chain, we need to not only select an appopiate seve node fo pocessing, but also patition this chain into two pats. One pat is executed on an appopiate seve node, anothe pat is executed on the client. The optimum patitioning point is the one which minimizes the oveall pocessing time, which equies the dynamic consideation of client/seve machine loads and capabilities, and the netwok bandwidth between client and seve. We assume that the local communication delay between subtasks within the same machine (client o seve) is zeo while client-seve communication delay is detemined by the cuent available bandwidth and latency between them. The cost fo pocessing a wavelet chain is modeled as: t s = t ediection + t data + t seve + t net + t client : t ediection is the cost to ediect the equest to anothe pocesso, if equied. t seve is the time fo seve computation equied. t client is the time fo any client computation equied. The values of t seve and t client depend on how a task chain is patitioned. t data is the seve time to tansfe the equied data fom the seve disk dive, o fom the emote disk if the file is not local. t net is the cost fo tansfeing the pocessing esults ove the Intenet. We discuss t data and t net in moe detail. ( tlstatup + data size b t data = disk 1 If local, t statup + data size min(b disk 1;b lnet 2) If emote. The equested file is the compessed image data. If image econstuction is conducted at the seve then the thumbnail image should also be included. If econstuction is

4 conducted at the client then the thumbnail image is aleady available and thus it is not included. If the file is local, the time equied to fetch the data is simply the file size divided by the available bandwidth of the local stoage system, b disk, plus some statup ovehead t lstatup. We also measue the disk channel load 1. If thee ae many concuent equests, the disk tansmission pefomance degades accodingly. If the data is emote, then the file must be etieved though the inteconnection netwok. The local netwok bandwidth, b lnet, and load 2 must be incopoated, plus the statup ovehead t stat. Expeimentally, we found on the Meiko appoximately a 10% penalty fo a emote NFS access, and on the SUN wokstations connected by Spac/Ethenet the cost inceases by 50%-70%. In ou cuent implementation, t lstatup and t statup ae neglected because if the files ae lage, these costs ae elatively small and if the files ae small, ediection t ediection and netwok ovehead will dominate. t net = t nstat + client-seve communication size : Net bandwidth This tem is used to estimate the time necessay to etun the esults back to the client ove the netwok. The numbe of bytes equied again depends on how the patitioning is conducted. If the seve does image econstuction, then the entie subegion image needs to be shipped. If the client only does image econstuction, the seve only needs to send compessed the coefficient data. t nstat is the statup time fo netwok connection and is ignoed in the cuent setting fo simila easons to those given fo t lstat and t stat. Given this cost pediction fo an image bowsing equest, the system compaes all seve nodes and enumeate all possible patitioning choices fo the coesponding chain, then selects a patitioning and a seve node to each the minimum esponse time. 5 An analysis fo homogeneous client-seve systems It is difficult to analyze the pefomance of this scheme fo geneal cases. We make a numbe of assumptions to examine the impact of system esouces on the selection of patitioning points. While ou scheme woks fo pocessing a sequence of equests, we study the esponse time in simultaneously pocessing a fixed numbe of equests. Ou esult eflects the scheduling pefomance fo esponding to a bust of equests, which occus fequently in many WWW sites [5, 9]. In the following analysis, we assume that the system is homogeneous in the sense that all nodes have the same CPU speed and the initial load, and each node has a local disk with the same bandwidth. We assume that all clients ae unifomly loaded with the same machine capabilities. We assume that all equests have the same type of image opeations fo econstucting an n n subimage. Each seve node pocesso eceives a unifom numbe of equests, and poduces a stable thoughput of infomation equested. We define the following tems: R equests eceived fo the entie system. p the numbe of seve nodes. equests eceived pe pocesso. = R. Notice that p we assume that the equests aiving at each node afte the division via DNS ae unifom. A the aveage ovehead in pepocessing a equest, and deciding a ediection. the aveage bandwidth of local disk access. the aveage bandwidth of emote disk access. c the aveage pobability of a pocessed equest accessing a local disk. S the aveage slowdown atio of the client CPU compaed to the seve node. S = CP U seve speed CP U client speed. d the aveage ediection pobability. O the aveage ovehead of ediection. B the aveage netwok bandwidth available between the seve cluste and the client. H the aveage pocessing time fo each equest. F 1 the aveage size of the compessed wavelet data (quadtee and coefficients). k the aveage faction of F 1 actually needed fo a subegion. g the constant atio fo the seve cost of sending disk files, e.g., gf 1 is the time fo sending data F 1. F 2 the size of the thumbnail. The image size is n=2n=2. E 1 the aveage seve CPU time fo extacting the subegion infomation. E 2 the aveage seve CPU time fo ceating the subegion coefficient data. E 3 the aveage seve CPU time fo image econstuction. E c the aveage seve CPU time fo coefficient data compession. E d the aveage seve CPU time fo coefficient data decompession. Among the equests aiving at each node, we assume the pobability of accessing one of the seve disks is equal to 1/p. Then 1=p equests ae accessing the local disk. Among those equests, d of them will be ediected to othe nodes but d equests will be ediected fom othe nodes to this node (we also assume that ediection is unifomly distibuted because of the homogeneous system). Ou expeiments show that in such cases, the ediected equests tend to follow file locality. Thus the total numbe of equests pocessed at each pocesso afte ediection is equests pe second. Among them, the total numbe of equests accessing the local disk is =p fom the oiginal aival tasks, plus an additional d ediected equests. Then the pobability of accessing a local disk fo those equests is: p c + d = = 1 p + d: Let H be the aveage esponse time fo each equest (the time fom when the client launches a equest to the time the client eceives desied data). Then H = t sys + t data + W 1 + W 2 + W 3 + W 4 + W 5 + t net :

5 whee t sys is the ovehead fo possible ediection, HTTP connection and pasing. t data is seve time spent fo eading the compessed data and thumbnail file if needed. W 1 is time spent fo extacting a subegion. W 2 is time spent fo eceating the wavelet coefficient data. W 3 is fo the wavelet image econstuction. W 4 is the ovehead fo compessing/uncompessing data tansmitted between client and seve. W 5 is the seve time equied to send data to the client. t net is the netwok time fo client-seve data tansmission. Fo the case of accessing local disk, the disk bandwidth is shaed by equests with pobability of c. Fo the case of accessing a emote disk, the netwok bandwidth is shaed by equests with pobability of (1? c). Thus we have t data = c F + (1? c) F whee F = F 1 + F 2 if the seve does the econstuction, o F = F 1 othewise. The computation cost is the non-ovelapped CPU cycles fo pocessing a equest. Notice that CPU cycles ae shaed by equests. t sys = (A + d(a + O)): And, E1 if seve extacts a subegion, W 1 = S E 1 if client does it. E2 if seve eceates coefficient data, W 2 = S E 2 if client does it. E3 if seve does image econstuction, W 3 = S E 3 if client does it. Ec + E W d S if cutoff point is D3, 4 = 0 else. W 5 = g F n, and t net = F n =B whee F n = 8 >< >: F 1 if cutoff point is D1, k F 1 if cutoff point is D2, k F 1 if cutoff point is D3, n 2 if cutoff point is D4. Thee ae 4 possible patitions and we mak the esponse time fo these patitions as H 1 ; H 2 ; H 3 and H 4. We choose the one with the minimum pocessing time. Patition D1: H 1 = Patition D2: H 2 = Patition D3: H 3 = c F1 + (1? c) F1 + (A + d(a + O)) + gf 1 + S E 1 + S E 2 + S E 3 + F 1 =B: c F1 + (1? c) F1 + (A + d(a + O)) + (E 1 + gkf 1 ) + S (E 2 + E 3 ) + k F 1 =B: c F1 + (1? c) F1 + (E 1 + E 2 + E c + gkf 1 ) +S (E 3 + E d ) + k F 1 =B: + (A + d(a + O)) Patition D4: H 4 = c F1+F2 + (1? c) F1+F2 + (A + d(a + O)) + (E 1 + E 2 + E 3 + gn 2 ) + n 2 =B: We can futhe detemine the ediection atio d fo diffeent patitions in ode to minimize the esponse time. A detailed analysis can be found in [2]. Due to space estictions, the esults ae summaized as follows: Case a: When A + O (F 1 + F 2 )( 1? 1 ), d = 0 fo all H 1 ; H 2 ; H 3 and H 4. Case b: When A + O < F 1 ( 1? 1 ), d = 1? 1=p fo all H 1 ; H 2 ; H 3 and H 4. Case c: When F 1 ( 1 b? 1 2 b ) 1 A+O < (F 1 +F 2 )( 1 b? 2 1 ), d = 0 fo H 1 ; H 2 and H 3, and 1? 1=p fo H 4. Then, H = min(h 1 ; H 2 ; H 3 ; H 4 ): The above fomula can help us to undestand the patitioning selection. Fo example in Figue 4, we illustate the modeled esponse times and thei elationship to seve load, client capabilities, and netwok bandwidth using the following paametes based on ou expeimental esults: n=512, k = 0.25, R = 6, p = 6, E 1 = 1.4 sec, E 5 = 0.4 sec, E 6 = 2.6 sec, = byte pe second, = bytes pe second, A = sec, O = 0.1 sec, g = 2:5 10?7 sec/byte, F 1 = bytes, F 2 = (n=2) 2, E c = 0.9 sec, E d = 0.9 sec. Figue 4 (a) plots the esults fo H 1 ; H 2 ; H 3 ; and H 4, when S is set to ange fom 0.5 to 3. Fo (b), B anges fom to bytes/sec while S is 2. Fom (a) and (b), we can see that if the seve is vey fast o the Intenet communication is vey fast, then D4 is the best patition and the seve does eveything. Othewise, the best patition is at D2, and it is advisable to send ove the compessed data fo the client to pocess. 6 Expeimental Results We have implemented a pototype of ou dynamic scheduling scheme with client esouces on a Meiko CS- 2 distibuted memoy paallel machine based on ou pevious SWEB wok [3]. The Meiko CS-2 is essentially a wokstation cluste with a fast netwok inteconnect. Each node has a scala pocessing unit (a 40Mhz SupeSpac chip) with 32MB of RAM unning SUN Solais 2.3. Ou pimay expeimental testbed consists of six Meiko CS-2 nodes as ou seve, each of which is connected to a dedicated 1GB had dive on which the test files eside. Disk sevice is available to all othe nodes via NSF mounts. The client machines ae loaded with ou custom Java applet libay implementing some of the basic opeations, including wavelet econstuction. To avoid Intenet bandwidth fluctuations, clients ae located within the campus netwok

6 16 512x512 Zoom x512 Zoom * Fo H1 x Fo H o Fo H3 + Fo H4 Seconds 10 8 Seconds Fo H4 o Fo H3 x Fo H2 * Fo H (a) S: CPU Ratio of Seve to (b) Netwok Bandwidth x 10 4 Figue 4: Impact of system esouces on patitioning. (a) powe vaying, (b) Netwok bandwidth vaying. to assist in expeimental stability ove multiple uns. We pimaily examine the pefomance of wavelet subimage etieval. We select an opeation which extacts a subegion at full esolution fom a 2K 2K map image, epesenting the use zooming in on a point of inteest at a highe esolution afte examining at an image thumbnail. The ovehead of ou scheduling and load monitoing is quite small fo all expeiments. Analyzing a equest takes about the ange of 2-4ms, and load monitoing takes about 0.1% of CPU esouces. These esults ae vey simila to those epoted in [3]. Aveage Response time (seconds) Seves with Resouces 6 nodes 5 nodes 4 nodes 3 nodes 2 nodes 1 node Requests pe second Figue 5: Request esponse times with client esouces as the numbe of seves and RPS change. Pefomance impovement using seve and client esouces. We examine the pefomance of the multipocesso seve with client esouces. We un a test fo a peiod of 30 seconds, at each second R equests ae launched fom clients. Figue 5 shows the aveage esponse times fo 1, 2, 3, 4, 5 and 6 seve nodes with client computing esouces. We can see that the esponse times dop significantly by using multiple seve nodes. Fo example, with RPS=1, the aveage esponse time fo the 6- node seves with client esouces is less than 5 seconds while the time fo the 1-node seve is about 25 seconds. With and without client esouces. We also compae the impovement atio of the esponse time with client esouces ove the esponse time without client esouces in Table 1. As the seve load inceases steadily, the esponse time impovement atio inceases damatically. We also note a significant incease in the numbe of equests pe second (RPS) a seve system can complete by using client esouces. The detailed esults ae in [2]. RPS= p=6 142 % 164% 192% 679% 4029% p=5 96% 183% 191% 1838% 7893% Table 1: Response time impovement atio with and without client esouce. Load balancing with hotspots. Hotspots ae a typical poblem with DNS otation, whee a single seve node exhibits a highe load than its pees [13, 14]. We examine how ou system deals with hotspots. We diected a fixed numbe of equests to a subset of nodes in ou seve cluste, giving a wide ange of load dispaities. Without ou schedule, those nodes would have to pocess all of those equests. Ou scheduling algoithm can effectively deal with tempoay hotspots at vaious nodes by ediecting equests to othe nodes in the goup. The esult is shown in Figue 6. The X axis shows the ange of pocessos which eceive equests. The uppe cuve of Figue 6 shows the aveage esponse time in seconds when no scheduling is pefomed and the equest pocessing is limited to the fixed subset of nodes. The lowe cuve shows the aveage esponse time with pesence of ou schedule. In that case the load is evenly distibuted among all pocessos by ediection, and esponse times ae vey unifom. Dynamic scheduling with vaying client capabilities. Vaious clients will have diffeing capabilities in tems of netwok bandwidth and pocessing capabilities. We illustate the effect of these on the scheduling decisions of ou scheme and examine if the theoetical esults pesented in Section 5 match the system decisions unde the specified assumptions. Figue 7 shows how the system makes the decision in tems of cutoff points fo the subimage extac-

7 Aveage Response time (seconds) With Scheduling Without Scheduling Numbe of Nodes Figue 6: System pefomance with equest concentation at fixed seve subsets. Tests fo a peiod of 30 sec., 4 RPS. tion when pocessing R concuent equests whee R=18, and when atificially adjusting the seve/client bandwidth and CPU atio epoted by the client. Each coodinate enty in Figue 7 is maked with the decision of schedule. We ovelaid the theoetical pedictions fom the fomula of Section 5 on the system esults. Fo each enty, if the choices fo all equests agee with the theoetical pediction, we mak the actual selected cutoff decision, othewise we mak the pecentage of disageement. Fo example, In Figue 7, when the bandwidth is 100,000 bytes/sec. and S = 4, the pecentage of disageement with the theoetical model is 6% among all pocessed equests. We can see the theoetical model closely matches the system' s selections. As bandwidth deceases, the schedule would incease the pecentage of equests who make use of client CPU fo data decompession and image econstuction, minimizing the data sent ove the netwok. On the othe hand, as client CPU speeds decease, we expect the seve to do moe pocessing. -seve bandwidth Seve/ CPU Speed Ratio D2 D2 D2 D2 D2 D2 D2 D2 500 D2 D2 D2 D2 D2 D2 D2 D2 1,000 D2 D2 D2 D2 D2 D2 D2 D2 10,000 D2 D2 D2 D2 D2 D2 D4 D4 100,000 D2 D2 D D4 D4 D4 150,000 D2 D2 D2 77 D4 D4 D4 D4 250,000 D2 D2 D2 93 D4 D4 D4 D4 500,000 D2 D2 D2 95 D4 D4 D4 D4 750,000 D2 D2 D2 88 D4 D4 D4 D4 1,000,000 D2 D2 D2 98 D4 D4 D4 D4 bytes/sec Pedicted D2 egion Pedicted D4 egion Figue 7: Effects of CPU speed and netwok bandwidth on decisions. 7 Related wok and concluding emaks We have pesented a dynamic scheduling and cost model fo pocessing image bowsing equests by utilizing both client and WWW seve esouces. We have demonstated the effectiveness of ou scheme in adapting to diffeent client-seve capabilities fo minimizing esponse times. Shifting computation fom a seve to its clients essentially scattes the wokload aound the wold. This elates the global computing and application-level scheduling pojects [6, 7, 10]. Those pojects deal with an integation of diffeent machines as one vitual machine. Ou expeience in using bandwidth and load infomation fo scheduling could be useful to this eseach. Addessing client configuation vaiation is discussed in [11] fo filteing multi-media data in ode to educe netwok bandwidth equiements but does not conside the use of client esouces fo integated computing. Ou cuent wok is to genealize this wok to suppot othe applications on the WWW fo adaptive seve/client scheduling [2]. Acknowledgments This wok was suppoted in pat by funding fom NSF IRI , NSF CCR , NSF CDA and a gant fom NRaD. 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