FRES-CAR: An Adaptive Cache Replacement Policy

Transcription

1 FRES-CAR: An Adaptve Cache Replacement Polcy George Palls, Athena Vaal, Eythms Sdropoulos Department of Informatcs Arstotle Unversty of Thessalon, 54124, Thessalon, Greece {avaal, Abstract Cachng Web objects has become a common practce towards mprovng content delvery and users servcng. A Web cachng framewor s characterzed by ts cache replacement polcy, whch dentfes the objects (.e. the elements on a Web page, whch nclude text, graphcs, and scrpts) to be replaced n a cache upon a request arrval. In ths paper, we present a cache replacement algorthm (so-called FRES-CAR), whch dentfes the objects that should be evcted by consderng together three mportant crtera: object s frequency, recency and sze. Expermentaton under synthetc worloads has shown that FRES-CAR acheves hgher ht rates when compared wth the most popular and exstng algorthms. 1. Introducton The explosve growth of the Web has mposed a heavy demand on networng resources over whch Web servers and users often experence long and unpredctable delays (partcularly) when retrevng Web pages. In a certan extend, the Web has become a vctm of ts own success. Increasng bandwdth would solve problems temporarly snce t would ease the users to create more and more resource-hungry applcatons, bunchng agan the networ. To overcome the above lmtatons, Web cachng has proven to be a valuable tool [7]. Typcally, Web cachng ams to mprove the performance of Web-based systems by eepng and reusng Web objects that are lely to be used often n the near future. In ths context, one of the most crtcal research ssues n Web cachng nvolve cache replacement as well as cache consstency and valdaton. Whenever the cache s full and the proxy needs to cache a new object, t has to decde whch object to evct from the cache to accommodate the new object. The polcy used for the evcton decson s referred to as the replacement polcy. In order to evaluate a cache replacement polcy, a common practce s to measure two performance rates, the ht rate (HR - s the percentage of the number of requests that are served by the cache over the total number of requests) and the byte ht rate (BHR - s the percentage of the number of bytes that correspond to the requests served by the cache over the total number of bytes requested). A hgh HR ndcates an effectve cache replacement polcy and defnes an ncreased user servcng, reducng the average latency. On the other hand, a hgh BHR mproves the networ performance (.e., bandwdth savngs, low congeston etc.). Usually, the cache past status s used to predct the future cache replacement actons. Each object s defned by a value, the so-called cache utlty value (CUV), for each object, where the objects wth the smallest utlty outcome wll be the frst canddates to evct from the cache. Earler cache replacement polces have been categorzed n: Recency-based: The object s CUV s determned by the tme of the last reference to that object. LRU (Least Recently Used) s the most ndcatve algorthm of ths category and has been appled n several proxy cachng servers, such as Squd. Sze-based: The object s CUV s determned by ts sze. The most ndcatve algorthm of ths category s the SIZE [10] whch consders object s sze as the basc parameter (large objects are evcted frst), assumng that users are less lely to re-access large objects because of the hgh access delay assocated wth such documents. Frequency-based: The object s CUV s determned by the number of requests to that object. Frequencybased approaches are varatons of the LFU (Least Frequently Used) algorthm and they use the popularty of objects for the replacement decson. Functon-based: The object s CUV s determned by a cost functon, whch nvolves multple parameters or weghts related to a performance metrc (such as Proceedngs of the 2005 Internatonal Worshop on Challenges n Web Informaton Retreval and Integraton (WIRI 05)

2 HR, BHR). The most ndcatve algorthm of ths category s the Greedy-Dual Sze [3]. ost systems use a recency-based polcy (.e. LRU), due to ts smplcty, but the dsadvantage of such polces s that they do not tae nto account the sze of the objects and the latency tme n the server transmsson, resultng to hgh BHR. The sze-based polces usually result n hgher HR snce they mantan smaller objects n cache (.e., more objects resde n cache). The frequency-based polces eep n cache the most popular objects ndependently on ther sze and on ther recency status.e. they avod eepng n cache recently accessed objects but wth a low frequency (such as the so-called one-tmer objects1). Fnally, the functon-based polces use complex formulas and usually suffer from heavy parameterzaton and hgh overhead Paper s Contrbuton Here, we propose a cache replacement polcy that wll ntegrate the deas proposed n frequency, recency and sze based approaches. ore specfcally, the proposed algorthm ams at addressng the followng ssues: tune recency wth frequency by replacng (recently accessed) one-tmers wth more frequently requested objects. Ths practce overcomes the dsadvantage of a pure recency-based polcy whch mght favor mantanng one-tmer objects n cache. tacle the cache fragmentaton problem by parttonng the cache nto groups of exponentally ncreasng szes. Ths practce wll eep a balanced cache segmentaton and wll avod overflowng the cache wth large-scale objects. provde a cautous cache replacement that wll deal wth all the man defcences of the replacements that ndependently handle ether recency (one-tmers are not traced), or frequency (sze s not consdered), or sze (access delays due to large object capactes are not consdered). Accordng to the authors nowledge, although several cache replacements approaches (e.g. [3,4,5]) have been proposed n the past, few efforts (such as n [5]) have been devoted n ntegratng the above characterstcs together. The proposed algorthm s compared wth the most popular and wdely-used algorthms and based on extensve (synthetc) trace-drven smulatons (generated by the ProWGen tool [2]) s shown to result n benefcal HR and BHR. The rest of the paper s organzed as follows. Secton 2 formulates the problem, whereas the proposed replacement algorthm, the so-called FRES-CAR, s 1 One-tmers are the objects whch are requested only once, regardless of the duraton of the access log studed. descrbed n Secton 3. Secton 4 has the results of the expermentaton and the algorthm s performance evaluaton whch are commented and dscussed. Fnally, we conclude the paper and gve some future wor plans n Secton Problem Formulaton A cache replacement problem nvolves some parameters that are used to montor the cached objects replacement process. In practce, each cache replacement polcy defnes a CUV assgned to each cached object such that the object wth the approprate CUV wll be evcted from cache. In ths paper, the Web cache content s modeled by a lned lst n whch each node s assocated wth a partcular cached object. Therefore, the number of nodes s bounded by the number of cached objects. Here, we consder each cached object to be dentfed by ts correspondng stored object flename, along wth a number of related attrbutes (e.g. object s sze, tme of object s request etc.). The basc goal of the proposed cache replacement problem s to mantan n cache the objects wth the largest CUV n order to acheve hgh HR. The total cache sze s computed n bytes and s of fxed sze (S). The total set of objects that are stored n the cache s not fxed, but t depends on whch objects are cached each tme (denoted by N ). Let o be the Web object whch s requested at the -th request. If o s n the cache, then we have a cache ht. Otherwse, we have a cache mss and the object o should be nserted nto the cache. In case that there s not enough space n cache, there s a need to evct one or more objects from the cache n order to free suffcent space. For each cached object x we eep trac of: the object d (o x ), the object sze (s x ) n order to handle the sze balance, the number of accesses snce the last tme object x was accessed (hst x ) n order to support recency, and the node d ( r ) for the object o x whch s updated accordng x to ts frequency. It s mportant that the cache replacement process should guarantee enough space for the ncomng objects. In ths context, there are two actons related wth the replacement process (.e. ether the object wll reman stored n cache or t wll be evcted from cache). We defne a functon to dentfy the acton that should be taen for each cached object: Defnton 1: The functon that wll determne whether a cached object s wll reman n cache or not s defned by 1 f object x wll be evcted from cache f (x) 0 otherwse Problem Statement: Suppose that N s the number of objects n cache at the -th request and S s the total Proceedngs of the 2005 Internatonal Worshop on Challenges n Web Informaton Retreval and Integraton (WIRI 05)

3 capacty of the cache area, the cache replacement problem s to N maxmze x 1 N x1 ( 1 f ( x)) CUV x subject to s x (1 f ( x)) S 3. The FRES-CAR Replacement Polcy The proposed cache replacement algorthm, s called FRES-CAR (Frequency REcency and Sze CAche Replacement), and t nvolves two phases: 1) Phase 1- Cache Segmentaton: partton the cache nto a number of segments whch are n accordance to the objects szes. Therefore the objects are stored n cache nto certan varable-szed segments. 2)Phase 2- Cache replacement: each of the defned cache-segments apply a cache replacement polcy Cache Segmentaton The motvaton for a cache-segmentaton s the flexblty that t offers to manage the cached objects, snce the cache segments may grow and shrn delberately (accordng to request streams) and also at each segment a separate replacement polcy may be appled. In ths paper we segment the cache n a way nspred from the wor presented n [1]. Next, we propose a herarchcal pyramd-le cache segmentaton (such as PSS (Pyramdal Selecton Scheme) [1]) dependng on objects szes such that objects are placed n the approprate segment accordng to thers szes. ore specfcally, we assume that the -th cache segment wll host all objects that have szes rangng between 2-1 and 2-1 bytes. Thus, the total number of bytes that can be stored n a cache s gven by S= 2 1 ( s the total number of cache segments,.e. the number of cache ( S 1) ). segments s log The Cache Replacement Algorthm A typcal replacement approach nvolves updatng the cache content under a certan crteron or over a consdered tme perod. Here, we consder that a cache replacement s occurred when there s a need for cache ds space. Specfcally, whenever an object s requested and t s a cache ht, we update the place where the requested object s stored n the lst (namely, we re-order the lst by movng the requested object towards to the tal of the lst). On the other hand, n case of a cache mss, the requested object s located to the approprate cache segment wth respect to ts sze. Thus, regardng to the amount of free cache space, we have two optons. If there s enough free space (avalable spacec sx ) to accommodate the object o x n the cache, then we locate the requested object to the approprate cache segment. If not, we should mae space by evctng from the cache one or more objects. Then, we locate the requested object to the approprate cache segment Locatng Objects wthn Segments. If there s enough space n order to cache the requested object, we assgn t to the approprate cache segment wth respect to ts sze. Consderng that we have segments the total cache sze S s computed by the sum of the szes of all cache segments. In partcular S S 1, where S s the sze of cache segment Each cache segment s modeled by a lned lst where each node of the lst s assocated wth a partcular cached object. The lst structure used for each cache segment s organzed wth respect to the three major characterstcs: frequency, recency and sze. Ths s realzed by holdng n the tal of the lst the most frequently, recently and (moderate)-szed objects. Gven that the cache segment stores N objects ( N N ) at the -th request, we 1 adopt a smlar to [6] approach by consderng a varable 1 ( 1 ) for each cache segment. In our N case, the locaton of the objects s performed as follows: a. If the object s not n cache, then nsert t at node N b. If t s currently at node r, move t to node r ( N r ) The above process updates the locaton of objects wthn cache segments even when the object s already n cache such that we support a dynamc and adaptve cache. Therefore, frequently accessed objects wll reman n (close to) the tal nodes. The locatng objects process uses a lned lst to mantan the order of the objects n the cache. Therefore, t has O( N log N ) complexty, snce t needs to perform nsertons n the lst at arbtrary nodes Free Space n Cache for Replacement. When the cache s full, the cache replacement polcy should determne whch objects should be purged from the cache n order to mae room for the ncomng objects. Typcally, a functon s needed n order to determne Proceedngs of the 2005 Internatonal Worshop on Challenges n Web Informaton Retreval and Integraton (WIRI 05)

4 whch object should be evcted from the cache and n our case we use the followng CUV: Defnton 2: Each object x n cache s characterzed by ts CUV whch s evaluated by CUV x = s x s the sze of object x, and 1, where s x hst x hst x eeps the trac of the number of accesses snce the last tme object x was accessed. Accordng to the above defnton, t follows that the utlty of each object n cache s lower for large-szed objects as well as for objects that have not been requested for a long tme. Whenever we need to decde whch object to eject from the cache, we compare the CUV of all objects resdng at the head of each cache segment lst. The object wth the smallest CUV wll be evcted from the span of the cache, and not necessarly from the cache segment where the ncomng object s meant to be cached. Ths process s contnued untl to free the requred space from the span of cache area n order to mae room for the new object. Therefore, each tme we have a cache acton (add an object to a cache segment or delete an object from a cache segment), the cache segments are re-szed so that the condton S S 1 s always satsfed. The proposed FRES-CAR algorthm nvolves two phases: PHASE 1: Cache Segmentaton Step 1.1: The number of cache segments s computed. Step 1.2: An object o n a cache segment s dentfed by ts correspondng stored object flename (f ), ts sze (s ), the number of accesses snce the last access of object o (hst ), and the place (node-d) object o s assgned n the lst ( r ). Step 1.3: We only cache the object s o of sze (s ) f there s cache free space. Step 1.4: Dependng on the object s o sze (s ) we select an approprate cache segment wll be chosen. PHASE 2: Cache Update and Replacement Step 2.1: We search the resulted n Step 1.4 cache segment, whch was prevously selected, for the exstence of object o. o In case of object o beng n the cache segment, we use the cache locaton scheme (subsecton 3.2.1) to update object s o place ( r ) n the cache segment. Step 2.2: In case the object o has not been found n cache segment and the free sze n the whole cache area s greater than object s o sze (s ) we nsert the object n the prevously selected cache-segment based on cache locaton scheme. Step 2.3: Otherwse, untl the free sze of the cache s enough for the object to be cached, we evct objects from the whole cache area (.e., meanng from dfferent cache segments and not just from the one where the object s meant to be cached) accordng to the followng rules: o We compute the CUV for each of the objects n the heads of the lned-lsts of the cache segments and o we evct that wth the smallest CUV. Fnally after mang enough space for the new object to be cached, we nsert t nto the specfc cachesegment computed earler by means of cache locaton scheme. In concluson, t s beng understood that the cache locaton scheme (subsecton 3.2.1) s used to fnd the place (node d), n a specfc cache segment (lst), n whch we wll cache our newly requested object and n the approprate change of ts poston n ts parent cache segment n case of a cache ht. On the other hand, the logc of the cache segmentaton s based on the choce of whch object to evct n order to mae space for a new request that wll need to be cached. 4. Expermentaton Results In our experments, we use extensve (synthetc) tracedrven smulatons, whch are generated by the ProWGen 2 tool [2]. The motvaton to use the ProWGen s that t ncorporates fve selected worload characterstcs, whch are deemed relevant to cachng performance. Due to ts flexblty, the ProWGen worload generator has been used n several earler research efforts [4, 11]. Table 1. Input Parameters to ProWGen Tool Parameters Default Values Range Total requests 1,500,000 1,500,000 Unque objects 30% 20%-40% One tmers 70% 50%-80% Zpf slope 85% 50%-95% Pareto tal ndex Correspondence szepopularty No correlaton No correlaton Temporal localty Dynamc Dynamc ProWGen traces capture the most mportant characterstcs of Web proxy worloads that are most relevant to Web proxy cache performance [11]. The nput parameters of ProWGen tool are summarzed n Table 1. The choce of these nput parameters values, as default values, was made n order to have a more realstc Web trace. These parameters are dscussed n the next subsectons. We examned the performance of FRES-CAR aganst that of LRU, LFU, -LRU for varable-szed objects [6], HLRU [8], and PSS [1] under several worloads. Due to the lac of space we wll present the most ndcatve of the obtaned results. Before we experment on the 2 The ProWGen s a synthetc worload generaton tool for smulaton evaluaton of Web proxy caches and t s very flexble. Proceedngs of the 2005 Internatonal Worshop on Challenges n Web Informaton Retreval and Integraton (WIRI 05)

5 performance of these algorthms, we examne the behavor of -LRU for varable-szed objects as vares from 1/ N to 1, and FRES-CAR, as vares from 1/ N to 1 (for each cache segment ). Furthermore, we should nvestgate the behavor of HLRU wth respect of h value (h ndcates the number of references for a certan Web object n a specfc tme perod). The motvaton for ths evaluaton s to use the values of, and h that wll result n better performance. After several experments, we conclude that the hghest HR was observed when =0.6 n case of -LRU for varable-szed objects, =0.8 n case of FRES-CAR for each cache segment, and h=6 n case of HLRU. The expermentaton s carred out by consderng the percentage of one-tmers, the sewness of the access patterns (zpf slope), and the percentage of unque objects. Then we evaluate the mpact of the above parameters on each replacement polcy for a specfc cache sze. The sze of the cache s expressed n terms of the percentage of the total number of bytes of all objects n a Web log fle. In our experments, we consder that the default value of cache sze s defned as the 1% of bytes of all objects n a Web log One-Tme Referencng Intally, we examne the senstvty of the proposed cachng polcy to one-tmer objects. As mentoned earler, one-tmers are defned as the objects whch are requested only once, regardless of the duraton of the access log studed. Indeed, there s not use to cache one-tmers, snce they are never accessed agan. Thus, the cache replacement algorthms should dstngush such one-tmer objects n order not to reduce the cache effectveness. Authors n [11] studed varous percentages for onetmers and observed that as ther percentages ncrease, the performance of cache replacement algorthms get a small mprovement. As depcted from the Fgure 1, we confrm ths observaton snce the HR and BHR ncrease as the percentage of one-tmer objects ncrease. Concernng the performance of FRES-CAR, as depcted from the Fgure 1a, ts HR s sgnfcantly hgher than the recency-based and frequency-based polces (10% hgher HR compared to the other examned polces). On the other hand, ts BHR s moderately lower than that of the recency-based polces (Fgure 1b). Therefore, we conclude that FRES-CAR algorthm can effectvely dstngush and solate one-tmers. Fgure 1. HR and BHR vs. One-Tmers Rato Fgure 2. HR and BHR vs. Zpf Slope 4.2. Zpf-le Popularty Dstrbuton. A common characterstc n Web traces s the hghly uneven dstrbuton of references to fles. As observed n the lterature [2], Zpf s law and power-law are qute common on proxes. Partcularly, Zpf-law has been appled to model fle popularty snce Zpf s law defnes a power-law relatonshp between the popularty (P) of an object and ts frequency (l). Ths relatonshp s formulated by P=c/l, where c s a constant and s often close to 1. From the prevous dscusson follows that ncreased sewness means stronger temporal localty. Here, further expermentaton was carred out n relaton to the mpact of the cache replacement polces on the zpf-le popularty dstrbuton of objects. Thus, as depcted from the Fgure 2, the recency-based polces are benefted from ths sewness. Furthermore, we observe that the HR and BHR of all polces ncrease wth ncreasng sewness. Regardng the performance of FRES-CAR, ts HR acheves the hghest performance comparng wth all the other polces. On the other hand, we observed that ts BHR s moderately lower than the recency-based polces at the slope value of 75% and hgher. Proceedngs of the 2005 Internatonal Worshop on Challenges n Web Informaton Retreval and Integraton (WIRI 05)

6 4.3. Percentage of Unque Objects. The thrd experment studes the mpact of the cache replacement polces on the percentage of unque objects that nclude n a Web log fle. We observe that as the percentage of unque objects ncrease, the HR and BHR for all the algorthms decreases. Concernng the performance of FRES-CAR, Fgure 3a depcts that t acheves a 10% to 14% hgher HR comparng wth the recency-based and frequency-based polces. Regardng ts BHR, we observe that FRES-CAR has a qute smlar performance f the percentage of unque documents s 30% or smaller. Fgure 3. HR and BHR vs. Unque Docs Rato The most smlar to FRES-CAR polces are the PSS and the -LRU for varable-szed objects polces. Table 2 summarzes the performance of FRES-CAR wth respect to the PSS and -LRU for varable-szed objects. Table 2. FRES-CAR vs. PSS and -LRU FRES-CAR vs. PSS -LRU Cache Sze HR BHR HR BHR Large (1,5%) +0.21% -0.28% +8.90% -5.74% edum (1%) +0.16% +0.26% +9.61% -3.55% Small (0,5%) -0.10% +0.25% +9.45% -0.90% Average +0.09% +0.07% +9.32% -3.40% Each table s cell shows the FRES-CAR s performance mprovement taen over for a partcular cache sze. Note that a plus sgn (+) ndcates better performance and a mnus sgn (-) ndcates worse performance. From ths table t follows that our proposed algorthm acheves a 9% hgher HR comparng wth the -LRU for varable-szed objects. However, FRES-CAR sacrfces 3.4% of BHR n order to have such a great mprovement n HR. When compared wth the PSS algorthm, t mproves a small amount of both HR and BHR. The low varaton n ht rate values, compared wth the PSS, can be explaned by the common practce of segment-based cache and by the same values n all cache segments. A further expermentaton wth varyng s underway. 5. Concluson and Future Wor Cachng s a typcal approach for mprovng the performance of Web-based systems. In ths paper, we ntroduced the FRES-CAR replacement algorthm, where the decson about whch objects should be evcted s determned by unfyng three object s factors: frequency, recency and sze. Trace-drven smulaton was employed to evaluate and comment on the performance of the proposed cachng scheme. Results have ndcated that FRES-CAR algorthm sacrfces a small amount (~3%) of BHR n order to mprove sgnfcantly the HR. Further research should extend the proposed wor n order to study the performance of FRES-CAR under real data traces and under dfferent values of per segment. Fnally, research efforts are underway towards extendng the proposed wor under a Content Delvery Networ (CDN) [9]. 6. References [1] C. Aggarwal, J. Wolf, P. Yu, Cachng on the World Wde Web, IEEE Transactons on Knowledge and Data Engneerng, 11(1), 1999, pp [2]. Busar, C. Wllamson, ProWGen: A Synthetc Worload Generaton Tool for the Smulaton Evaluaton of Web Proxy Caches, Computer Networs, 38(6), Jun. 2002, pp [3] P. Cao and S. Iran, Cost-Aware WWW Proxy Cachng Algorthms, Proceedngs of the 1st USENIX Symposum on Internet Technologes and Systems, onterey, Calforna, USA, Dec. 1997, pp [4] D. Katsaros, Y. anolopoulos, Cachng n Web emory Herarches, Proceedngs of the AC Symposum on Appled Computng, Ncosa, Cyprus, ar. 2004, pp [5] S. Podlpng, L. Boszormeny, A Survey of Web Cache Replacement Strateges, AC Computng Surveys, 35(4), 2003, pp [6] K. Psouns, A. Zhu, B. Prabhaar, R. otwan, odelng Correlatons n Web Traces and Implcatons for Desgnng Replacement Polces, Computer Communcatons, 45(4), Jul. 2004, pp [7]. Rabnovch, O. Spatshec, Web Cachng and Replcaton, Addson Wesley, [8] A. Vaal, Proxy Cache Replacement Algorthms: A hstory-based approach, World Wde Web Journal, 4(4), 2001, pp [9] A. Vaal, G. Palls, Content Delvery Networs: Status and Trends, IEEE Internet Computng, Vol. 7(6), 2003, pp [10] S. Wllams,. Abrams, C. Standrdge, E. Fox, Removal Polces n Networ Caches for World Wde Web Documents, Proceedngs of the AC SIGCO Conference, Stanford Unversty, Aug. 1996, pp [11] C. Wllamson, On Flter Effects n Web Cachng Herarches, AC Transactons on Internet Technology, 2(1), 2002, pp Proceedngs of the 2005 Internatonal Worshop on Challenges n Web Informaton Retreval and Integraton (WIRI 05)