Content Classification for Caching under CDNs

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1 Content Classfcaton for Cachng under CDNs George Palls 1,2, Charlaos homos 2, Konstantnos Stamos 2, Athena Vakal 2, George Andreads 3 1 Department of Computer Scence, Unversty of Cyprus 2 Department of Informatcs, Arstotle Unversty of hessalonk 3 School of Engneerng, Arstotle Unversty of hessalonk gpalls@cs.ucy.ac.cy, {chthomos, kstamos, avakal}@csd.auth.gr, andread@eng.auth.gr Abstract Content Delvery Networks (CDNs) provde an effcent support for servng resource-hungry applcatons whle mnmzng the network mpact of content delvery as well as shftng the traffc away from overloaded orgn servers. However, ther performance gan s lmted snce the storage space n CDN s servers s not used optmally. In order to manage ther storage capacty n an effcent way, we ntegrate cachng technques n CDNs. he challenge s to decde whch objects would be devoted to cachng so as the CDN s server may be used both as a replcator and as a proxy server. In ths paper we propose a nonlnear non-parametrc model whch classfes the CDN s server cache nto two parts. hrough a detaled smulaton envronment, we show that the proposed technque can yeld sgnfcant reducton n user-perceved latency as compared wth other heurstc schemes. 1. Introducton CDNs (Content Delvery Networks) have been proposed to accelerate the delvery of the Web content. On a daly bass, users use the Internet for resourcehungry applcatons whch nvolve content such as vdeo, audo on-demand and dstrbuted data. For nstance, the Internet vdeo ste Youube hts more than 100 mllon vdeos per day. Estmatons of Youtubes bandwdth go from 25B/day to 200B/day. At the same tme, more and more Web content servers are delverng greater volumes of content but wth hgh senstvty to delays. For nstance, a delay on fnancal data-feed Web ste (e.g., USD to EUR currency stock markets) may cause serous problems to the end-users. A CDN s an overlay network across Internet, whch conssts of a set of surrogate servers (dstrbuted around the world), routers and network elements. Surrogate servers are the key elements n a CDN, actng as replcators. hey store copes (also called replcas) of dentcal content, such that clents requests are satsfed by the most approprate ste. Once a clent requests for content on an orgn server (managed by a CDN), clent s request s drected to the approprate CDNs surrogate server Paper s Motvaton & Contrbuton Whle the CDNs result n sgnfcant benefts, n terms of avalablty, stablty and Web transfer speed, ther performance ncrease s lmted due to the facts that: the storage space n surrogate servers s not used optmally [1], the content whch s replcated n the surrogate servers remans statc for a consderable perod of tme. In order to allevate the above problems we deploy cachng n conjuncton wth replcaton. Specfcally, we consder a CDN whose surrogate servers act smultaneously both as proxy servers and content replcators. Accordng to the lterature, the resultng problem of fndng whch objects replcas should be created where, gven that any free space wll be used for cachng, s NP-complete [1]. In ths context, two heurstc approaches have been proposed [1, 6] towards managng the capacty of surrogate servers. Specfcally, the key ssue of Hybrd [1] and SRC [6] s to determne the percentage of storage space of CDN s surrogate servers that would be devoted n cachng. However, these approaches are offlne and, consequently, are unable to handle effcently the sudden changes n the nterest of the end users. hs s a crucal ssue f we consder that the most popular objects reman popular for a short tme perod [3]. Furthermore, the Hybrd algorthm suffers from admnstratvely tunable parameters whch determne the percentage of storage space for cachng [1]. In ths context, the deal content management polcy of surrogate servers should: (a) Handle the sudden changes of Web users request streams; (b) Lack of any admnstratvely tunable parameters; (c) Acheve a delcate balance between replcaton and cachng towards mprovng the CDN s performance. he major contrbutons of ths work are: Proposng a CDN framework where the surrogate servers act both as proxy caches and statc content replcators under a cooperatve envronment. Presentng a method, the so-called R-P (Reward- Penalty), whch parttons the surrogate servers /08/$ IEEE

2 cache nto two parts: he frst part s devoted to cachng and the second one s devoted to replcaton. he replcas are classfed to one of the above categores by usng a nonlnear model. he nonlnear model s preferred snce t classfes better the replcas than any lnear model [4]. Provdng an expermentaton showng that our method performs better than the examned algorthms. We evaluate the performance of the proposed method usng a dataset whch captures the workloads of a streamng meda Web ste. he rest of ths paper s organzed as follows. In Secton 2 we present the proposed R-P method. Secton 3 presents the smulaton testbed, and secton 4 evaluates the experment results. Fnally, the concluson of our work s gven n secton he R-P Method We consder a CDN framework, where the surrogate servers act both as dynamc content replcators (proxy caches) and statc ones under a cooperatve envronment. Durng a tranng tme perod the Web objects of each surrogate server are classfed nto two categores by usng a nonlnear model: volatle and statc. he volatle objects are replcated to the dynamc part of cache, whereas, the statc ones are replcated to the statc part of cache. In the proposed framework, the avalable storage capacty of each surrogate server, whch s denoted by K, s parttoned nto two parts: he frst one ( S ) s used for replcatng content statcally and the second one ( D ) s used for replcatng content dynamcally (runnng a cache replacement polcy): D + S = K (1) From the above equaton t holds that f D = 0 the cooperatve push-based scheme s appled where, the surrogate servers cooperate upon cache msses and the content of the caches remans unchanged. On the other hand, f S = 0 the surrogate servers turn nto cooperatve proxy caches (dynamc cachng only). Our proposed method assgns a qualty value q for each object whch has been replcated n surrogate servers. In partcular, the qualty value of replcas s expressed by the users nterest (ncreasng ts value by usng equaton 2) or the lack of users nterest (decreasng ts value by usng equaton 3) for the underlyng replca. he ntuton behnd s that each tme t an object s requested, t s rewarded by the functon R(. At the same tme, all the other replcas receve a penalty by the functon P(, snce they have not been requested. Specfcally, the followng functons have been defned: 1 R( = 1 ( t t ) (2) 1 P( = ( t t ) (3) he t expresses the tme of the prevous user s request for the specfc object and denotes the tranng tme perod. Regardng the relaton between t and t, t holds t t. herefore, t s obvously occurred that R( [0,1] and P( [ 1,0]. he qualty value of each object for a specfc tme s calculated by the sum of the total reward and penalty values as follows: q = ( P( + R( ) (4) t = 0 akng nto account the qualty value of each object (equaton 4), we classfy them nto two categores by usng a classfcaton model. Consderng that lnear models do not classfy effcently the Web objects due to ther neffcency to fnd correlatons among Web objects features [4], we make use of the logstc sgmod functon. Specfcally, the logstc sgmod functon has been wdely used by neural networks [2] to ntroduce nonlnearty n the model. hus, t has been proven useful n case of two-class classfcaton [4]. Here, the followng model splts the objects populaton nto two groups (dynamc and statc) wth respect to the equaton 4: 1 N( q ) = (5) 1 e Eventually, the above nonlnear model (equaton 5) wll classfy each object nto one of the two desred categores: volatle ( N ( q ) 0) or statc ( N ( ) 1). A smlar model has also been used n q [4] n order to predct the cache utlty value of each cached object by usng features from Web users traces. In ths paragraph, a descrpton of the R-P method s gven. Intally, we consder that a warm-up phase for the surrogate servers caches has been preceded where the replcas of each surrogate server have been classfed nto volatle and statc wth respect to the equaton 5. Furthermore, t s crtcal to consder a tme perod for resettng the qualty values of replcas. he functonalty of the R-P method s depcted by the flowchart n Fgure 1. When a surrogate server receves a request for an object, the qualty values of replcas q

3 are updated wth respect to equaton 4. hen, a check to the statc cache s performed. If t s a ht, the request s served; else another check to the dynamc cache s performed. In case the requested object s n the cache, t s served and the cache s content s updated wth respect to the qualty values of objects. In case of a cache mss, the requested object s pulled from another server (selected based on proxmty measures) and stored nto the dynamc cache. hen, the end-user receves the cached object. he objects of the dynamc part of cache wll be avalable n surrogate server s cache for future requests as long as they are allowed by the cache replacement polcy. Accordng to ths polcy, f there s no space to store ths object, t s removed from the dynamc part of cache the object whch has the lowest qualty value. he qualty value of each object s calculated by the equaton 4. he above procedure s repeated untl the tme threshold () s exceeded. In such a case, all the objects, whch are stored n surrogate server, are re-classfed accordng to the equaton 5 and ther qualty values are reset. hus, for small values of, R-P captures more effcently the sudden changes of Web users request streams. Fgure 1. An Outlne of R-P Approach 3. Smulaton estbed CDNs host real tme applcatons and they are not used for research purposes. herefore, for the expermentaton needs t s crucal to mplement a smulaton testbed. In ths work, we use the CDNsm a tool that smulates a man CDN nfrastructure. A demo of our tool can be found at ~cdnsm/. It s based on the ΟΜΝeΤ++ lbrary whch provdes a publc-source, component-based, modular and open-archtecture smulaton envronment wth strong GUI support and an embeddable smulaton kernel. All CDN networkng ssues, lke surrogate server selecton, propagaton, queueng, bottlenecks and processng delays are computed dynamcally va CDNsm, whch provdes a detaled mplementaton of the CP/IP protocol, mplementng packet swtchng, packet re-transmsson upon msses, objects freshness etc. Here, the CDNsm smulates a CDN wth 20 homogeneous surrogate servers whch have been located all over the world. he sze of each surrogate server has been defned as the percentage of the total bytes of the Web server content. Fnally, the outsourced content has been replcated to surrogate servers usng the l2p algorthm [5]. Accordng to the l2p, the outsourced objects are placed to the surrogate servers wth respect to the total network s latency and the objects load. hs polcy s preferred snce t acheved the hghest performance. Consderng that the role of CDNs s prmarly focused on mprovng the QoS of the resourcehungry applcatons n Web stes, such as Dgtal elevson, Interactve V, Vdeo On Demand (VOD), etc., streamng meda servces are of nterest n CDNs. In ths context, we used the MedSyn workload generator descrbed n [7], whch generates realstc streamng meda server workloads. Specfcally, ths generator reflects the dynamcs and evoluton of content at meda stes and the change of access rate to ths content over tme. Furthermore, the MedSyn changes the popularty of objects over a daly tme scale wthn a certan perod of tme. In ths work, we have generated a data set, whch represents the HP Corporate Meda Solutons Server (HPC) Web ste. able 1 presents the characterstcs of the examned data sets. Fnally, concernng the network topology, we used an AS-level Internet topology wth a total of 3037 nodes. hs topology captures a realstc Internet topology by usng BGP routng data collected from a set of 7 geographcallydspersed BGP peers. 4. Expermentaton 4.1. Examned Polces he proposed approach (R-P) ntegrates both cachng and replcaton n CDNs. hus, we evaluate the R-P s performance wth respect to the above standalone approaches. Furthermore, we compare R-P wth SRC. Prevous results [6] have shown that SRC s the leadng algorthm n the lterature for ntegratng cachng and replcaton over CDNs. Specfcally, the followng approaches are examned: SRC: A placement smlarty measure s used n order to evaluate the level of ntegraton of Web cachng wth content replcaton.

4 Cachng: All the storage capacty of the surrogate servers s allocated to cachng. he selected cache replacement polcy s LRU snce t s used by the most proxy cache servers (e.g., Squd). Replcaton: All the objects are replcated statcally n each surrogate server usng all the avalable storage capacty. able 1. Parameters for Generated Data Set Characterstc HPC Log duraton 91 days Number of requests Number of Web objects 1434 Sze of Web ste 3.8Gbytes Fgure 2. Mean Response me vs. Fgure 3. Mean Response me vs. Cache sze 4.2. Evaluaton Measures he measures used n the experments are consdered to be the most ndcatve ones for performance evaluaton. Specfcally, the followng measures are used: Mean Response me (MR): the expected tme for a request to be satsfed. It s the summaton of all requests tmes dvded by ther quantty. hs measure expresses the users watng tme n order to serve ther requests and t should be as small as possble. Byte Ht Rato (BHR): t s defned as the fracton of the total number of bytes that were requested and exsted n the cache of the closest to the clents surrogate server to the number of bytes that were requested. A hgh byte ht rato mproves the network performance (.e., bandwdth savngs, low congeston). Ht Rato (HR): t s defned as the fracton of cache hts to the total number of requests. A hgh ht rato ndcates an effectve cache replacement polcy and defnes an ncreased user servcng, reducng the average latency Evaluaton Frstly, we nvestgate the proposed approach R-P wth respect to the mean response tme, wth varyng. he results are reported n Fgure 2. he x-axs represents the whch s expressed n days (24 hours reflect to 1 day), whereas, the y-axs represents tme unts accordng to CDNsm s nternal clock and not some physcal tme quantty, lke seconds, mnutes. So the results should be nterpreted by comparng the relatve performance of the algorthms. hs means that f one technque gets a response tme 0.5 and some other gets 1.0, then n the real world the second one would be twce as slow as the frst technque. In general, we observe that as the tme perod () for takng place a reclassfcaton of replcas ncreases, the performance of R-P s deterorated. In other words, ths means that the performance of the R-P captures better the sudden changes of Web users request streams when the replcas are reclassfed n small tme perods. For nstance, the lowest mean response tme has been observed when the classfcaton takes place every two days. hs observaton s common ndependent of the surrogate servers cache szes. Regardng the cache sze of surrogate servers the R-P presents lower mean response tmes for small-scale cache szes. he cache szes of surrogate servers are expressed n terms of the percentage of the total sze of the examned Web ste. Secondly, we test the performance of the examned ntegrated approaches (SRC and R-P) wth respect to the cache sze. Due to lack of space, we have consdered that the tme perod takes the value of two days. he results are depcted n Fgure 3. he x-axs represents the cache sze of each surrogate server. We observe that the R-P outperforms the SRC approach achevng a delcate balance between cachng and replcaton. Furthermore, we observe that as the cache sze of surrogate servers grows, the mean response tme of the examned polces ncreases n a lnear way. Fgure 4 presents the BHR of the examned polces. he x-axs represents the cache sze, whereas, the y- axs represents the BHR. he R-P s the leadng algorthm, achevng the hghest BHR. As we expected, the BHR of the examned algorthms ncreases wth respect to the surrogate server s cache sze. In partcular, the larger the cache sze s, the hgher the BHR s. As far as the cachng approach s

5 concerned, t presents hgher BHR than the replcaton one. he close performance of SRC and cachng s explaned by the fact that a large percentage of storage space n the SRC s allocated to Web cachng (about 85%). On the other hand, the pure replcaton yelds poor performance and t seems that t s not affected by the cache sze. hs behavor was expected snce the pure replcaton does not manage n an effcent way the storage space. Qute smlar results are also obtaned when we evaluate the HR of the examned algorthms. he results are reported n Fgure 5. As prevous, the R-P s the leadng algorthm, ndcatng the hghest HR comparng wth the examned approaches. o summarze the experments, we can conclude that the ntegraton of replcaton wth cachng leads to mproved performance n terms of perceved network latency, byte ht rato and ht rato. he results renforce the ntal ntuton that replcatng replcas statcally for content avalablty along wth cachng polces mproves the Web performance. he proposed method outperforms the exstng ntegratng approach, achevng a delcate balance between replcaton and cachng. Furthermore, the performance of R-P seems to handle effcently the sudden changes of Web users request streams. 5. Concluson In ths paper, we dealt wth the potental performance benefts that can be reaped by combnng both cachng and replcaton n CDNs. he challenge for such an approach s to determne whch objects would be devoted n cachng so as the CDN s server may be used both as a replcator and as a proxy server. In ths paper, we propose a nonlnear non-parametrc model whch classfes the CDN s server cache nto two parts. Inspred by the neural networks, we make use of the logstc sgmod functon n order to splt the outsourced objects nto two groups (volatle and statc). he expermentatons results show that the proposed approach acheves a delcate balance between replcaton and cachng towards mprovng the CDN s performance. Furthermore, the R-P approach captures n an effcent way the sudden changes of Web users request streams, whch usually occur n streamng meda Web stes. Fgure 4. BHR vs. Cache Sze Fgure 5. HR vs. Cache Sze 6. Acknowledgments hs work has been supported partally by the Mare Cure EU project (Contract No ) and by the Hrakletos project under the EPEAEK Framework. 7. References [1] S. Bakras,. Loukopoulos: Combnng replca placement and cachng technques n content dstrbuton networks. Computer Communcatons, 28(9): , [2] C.M. Bshop: Neural Networks for Pattern Recognton. Oxford Unversty Press, [3] Y. Chen, L. Qu,W. Chen, L. Nguyen, R. H. Katz: Effcent and adaptve Web replcaton usng content clusterng. IEEE Journal on Selected Areas n Communcatons, 21(6): , [4]. Koskela, J. Hekkonen, K. Kask: Web cache optmzaton wth nonlnear model usng object features. Computer Networks, 43(6): , [5] G. Palls, K. Stamos, A. Vakal, A. Sdropoulos, D. Katsaros, Y. Manolopoulos: Replcaton based on objects load under a content dstrbuton network. Proceedngs of the 2nd WIRI (In conjuncton wth ICDE06), Atlanta, Georga, USA, Apr [6] K. Stamos, G.Palls, C.homos, A.Vakal: A smlarty based approach for ntegrated Web cachng and content replcaton n CDNs. Proceedngs of the 10 th IDEAS, New Delh, Inda, Sep [7] W. ang, Y. Fu, L. Cherkasova, A. Vahdat: Modelng and generatng realstc streamng meda server workloads. Computer Networks, 51(1): , 2007.