An HTTP Web Traffic Model Based on the Top One Million Visited Web Pages

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1 An HTTP Web Traffic Model Based on he Top One Million Visied Web Pages Rasin Pries, Zsol Magyari, Phuoc Tran-Gia Universiy of Würzburg, Insiue of Compuer Science, Germany Absrac The ever-changing behavior of HTTP web pages requires an adjusmen of he web models used for simulaion and benchmarking. In his paper, we presen he saisical daa of he one million mos visied web sies. Using his daa, we examine he changes in size and number of objecs by comparing our findings wih well-known web raffic models. The resuls show a rend owards large pages including mulimedia conen. In addiion, oday s web pages are creaed dynamically, i.e., ha he conen is downloaded from web servers spread all around he world. Finally, we discuss new web raffic models and presen he parameers gahered from our measuremens. Index Terms web raffic, measuremen, modeling, HTTP I. INTRODUCTION Web raffic based on he HTTP proocol is sill he dominaing applicaion in he Inerne. Wihin he las few years, he percenage of web raffic even increased due o social neworks, videos embedded in web pages, and file hosing [1], [2]. The main video hoser is YouTube, which has a share of abou 8% of he complee Inerne raffic [3]. Large files are downloaded by direc download hosers such as Rapidshare and is share increased o up o 3% [4]. During he ime when he widely used raffic model by Choi and Limb [5] was se up, YouTube was no even founded and downloads were handled using FTP. This makes such old raffic models obsolee, requiring new web raffic models aking ino accoun hese new services, new feaures of web browsers, and he new web page complexiy. Such a model is essenial for nework planning, nework simulaion and emulaion. A new model can even help web server operaors and cloud providers for dimensioning purposes. According o Popa e al. [6], HTTP can also be used for mulimedia sreaming using Conen Delivery Neworks (CDNs) and HTTP proxies. Here, HTTP chunking is used, where a video is subdivided ino blocks, which are hen disribued o he CDNs and can be downloaded separaely by he user. Thus, a model would also be helpful for dimensioning he caching size and hus, o decrease he required bandwidh and o improve he Qualiy of Experience (QoE) for he end user. In his paper, we presen he resuls of our web page measuremens. Whereas oher papers in his area measure Inerne access links on campuses or a small Inerne Service Providers (ISPs), we focus on he mos visied web pages and evaluae heir srucure. Wih his differen approach, we are able o ge a generalized model, which is no limied o he web browsing behavior of a single counry or even one campus. To perform he measuremens, we firs downloaded he one million mos visied web pages from Alexa. For each web page we obained he main objec size as well as he number and size of he embedded s, separaed ino he differen caegories. The resuls show a rend owards an increased number of embedded objecs and hus o larger web pages in general. These web pages can now be consruced by downloading he conen from web servers disribued all around he world, which increases he variabiliy of he page download ime. Finally, we presen our model parameers resuling from our measuremens of he op million web pages. The res of his paper is srucured as follows. In Secion II, he background o web raffic models and measuremens of he user s web browsing behavior is presened ogeher wih he relaed work. Secion III describes our approach including he used measuremen ools. The resuls of our measuremens are shown in Secion IV. Finally, conclusions are drawn in Secion V. II. BACKGROUND & RELATED WORK The mos widely used web raffic model is based on he work of Choi and Limb [5]. The model and erms are sill used for sandardizaion, cf. [7], [8]. According o hem, a web browsing user can be modeled wih an on/off, see Figure 1(a). The on ime sars as soon as he user requess user sae objec loading objec loading connecion web reques hp on main objec main objec loading and viewing Fig. 1. viewing hp off Behavioral user model. web or video reques loading and viewing main video inline (a) (b) (c) (d) /12/$ IEEE 133

2 a web page and ends, when all objecs of a web page are downloaded. Afer finishing he loading of he web page, a silen period akes place while he user is reading he conen of he web page. This reading ime is according o [7], [8] exponenially disribued wih a mean of 30 seconds. To ge he exac numbers for he model, Choi and Limp measured HTTP raffic a he Georgia Tech campus. The focus of our paper lies however on he srucure of he web page iself, as shown in Figure 1(b). A web page consiss of a main objec, which defines he basic srucure of he web page and conains he links o s. Inline objecs can be images, scrips, syleshees, ec. Afer a user requess a new web page, he main objec is loaded followed by all s lised in he main objec, cf. Figure 1(b). While he browser has o se up a new connecion for every when using HTTP version, HTTP 1.1 enables he use of a single connecion for downloading he main objec as well as all s, cf. Figure 1(d). This decreases he loading ime of a web page, because he slow sar phase is only run once. According o he HTTP 1.1 sandard RFC 2616 [9], afer downloading he main objec, up o 2 connecions can be esablished in parallel for downloading he s. The number of parallel connecions can however be furher increased by he user, e.g., in he Firefox browser. However, Naarajan e al. [10] have shown ha a large number of parallel connecions does no always decrease he download ime of a web page, especially when he user has a low access connecion speed. In addiion o he possibiliy of configuring he number of parallel connecions, oday s browsers are able o no only download he conen of he jus accessed web page, bu can be configured o download also he linked web pages direcly afer downloading he curren web page. This of course shorens he iner-arrival ime of web pages significanly and also increases he variance of his parameer, depending wheher his new feaure is acivaed or no. Thus, he reading ime is hard o measure as i depends on he seings of he web browser. This new opion reduces he waiing ime of he user when clicking a link, bu increases he load in he nework as he browser downloads a lo of conen, which would never be requesed by he user. The web raffic model of Choi and Limb is compared o oher models from Mah [11], Barford and Crovella [12], and ohers in Tran-Gia e al. [13]. Whereas according o Choi and Limb, he main and size can be fied using a Lognormal disribuion wih a mean of abou 10 kb, all ohers recommend a Pareo disribuion wih a median of abou 2 kb. The number of s varies beween 5.5 measured by Choi and Limb and 2.8 from Mah. Table I liss he imporan web raffic model parameers from differen sudies. Wih he increasing smarphone usage, Zhu e al. [15] se up wo differen web raffic models, one for wired and one for wireless (3G) devices. According o heir analysis, he number of s is smaller in he wireless model, because he web pages are especially opimized for mobile, small devices. However, his difference is of less imporance because oday s TABLE I OVERVIEW OF WEB TRAFFIC MODELS, PARTIALLY TAKEN FROM TRAN-GIA ET AL. [13]. lieraure main inline number of reading source objec size objec size s ime Lognormal Lognormal Gamma Weibull Choi mean:10 kb mean:7.7 kb mean:5.5 mean:39.5 s [5] med.:6 kb med.:2 kb med.:2 med.:11 s σ=25 kb σ=126 kb σ=11.4 σ=92.6 s Mah [11] Barford [12] [14] Pareo Pareo - mean:1000- α=5-7 α= mean: s med.:2-2.4 kb med.:1.2-2 kb med.:1 med.:15 s Pareo Pareo Weibull α=1 α=2.43 α=1.46 k=1000 k=1 β=8 Lognormal Lognormal Gamma Lognormal mean:11.9 kb mean:12.5 kb mean:5.07 mean:39.7 s σ=38 kb α=116 kb σ= smarphones are capable of displaying he same web pages as wired devices. A new raffic model was se up by and Gupa in 2007 [14]. They claim ha exising models are no valid anymore because a user can reques anoher web page, when he download of he las was sill no compleed. This behavior can also be modeled using an on/off, bu he on period can conain now muliple web page requess whose objecs overlap. In Secion IV, we compare heir measuremen resuls wih ours. In conras o he papers menioned above, Bukiewicz e al. [16] did no came up wih a new HTTP model, bu showed he complexiy of oday s web pages. Whereas in former imes, he complee conen was downloaded from a single web server, especially news pages download he conen from many servers and origins. This of course increases he variabiliy of he page load imes. A he same conference, Ihm and Pai [17] presened heir sudy on modern web pages. They analyzed modern web raffic and compared objec sizes and number of objecs of users in differen counries as well as he change beween 2006 and In all counries, an increased objec size was measured and a small increase in number of embedded objecs. The saisics show ha here is a huge difference beween he web pages accessed in France o hose in China or Brazil. In conras o he oher publicaions in his area, hey observed a large number of embedded video files. The large number of embedded video files are underlined by our measuremens performed in 2008 [1], [2] and 2010 [18]. One resul of our measuremens of 600 households in 2010 is shown in Figure 2. The char illusraes he applicaion disribuion in erms of ransmied Byes, leaving he number of no idenified flows ou. The figure presens hree imporan hings. Firs, HTTP raffic is sill he dominaing applicaion in he Inerne. Second, we measured 8% flash raffic, which is mainly caused by YouTube. YouTube videos are embedded in a web page as /12/$ IEEE 134

3 Fig. 2. Byes. HTTP (47%) Oher (4%) Direc Download (3%) Flash (8%) BiTorren (38%) Pie char showing he applicaion disribuion in erms of ransferred an objec wih a link o he acual.swf video file. This means ha no he complee video is inegraed in a web page, bu a direc link owards i. Finally, we measured a high percenage of direc download links. These links are also embedded ino web pages and can be accessed by clicking on he link. Thus, we need o differeniae beween web page and video/direc downloads for our web raffic model shown in Figure 1. While Figure 1(b) illusraes he normal web browsing behavior similar o Choi and Limb [5], Figure 1(c) presens he case when he user requess a video from he previously accessed web page. This has an impac on he viewing ime, which is assumed o be much shorer and can also be negaive, meaning ha he user requess he video before he complee web page has been downloaded. The same applies for a direc download. Which model o choose depends on he percenage of video and direc download flows, which is according o our measuremens 2-5% of he oal number of flows. The complexiy of oday s web pages makes i difficul o deermine he reading ime as i depends on he preferences of he user and he web browser seings. Therefore, we focus on he srucure of he web page iself by measuring he main objec size as well as he number and size of he s. In he nex secion, we describe how we gahered he parameers for he model. In conras o previous works, where links on campuses or access neworks are measured and analyzed, we ake a look a he one million mos visied web pages and analyze hem. This way, a more generalized web raffic model can be se up because i does no only reflec he browsing behavior of sudens on campuses or a single counry. The sudy of Ihm and Pai [17] underlines our mehod for seing up a web raffic model because hey observed a huge difference in he web page saisics accessed in various counries and wih measuring he mos visied web pages worldwide, he model is free of sie- and counry-specific bias. III. MEASUREMENT METHODS AND SETTINGS As menioned in he previous secion, our goal is o ge deailed informaion abou he one million mos accessed web pages. Therefore, we firs downloaded a lis of he op one million sies from Alexa 1. As i would ake oo long o crawl all pages from a single compuer, we se up a cluser consising of 31 nodes. Each of hese nodes runs one virual machine and execues 10 o 30 hreads in parallel. Each hread works as follows: 1) A URL of he op one million lis is rerieved from a cenral daabase. 2) The web page is opened using Mozilla Firefox, emulaing he normal user behavior. 3) Web page saisics are gahered using WebDeveloper [19], which displays he web saisics on a second browser ab. 4) The displayed saisics are rerieved by Chickenfoo [20] and sored in a daabase on a cenralized server. In oal, we capured he web page saisics from 881,640 pages. The oher web pages have been unavailable during he measuremens. In he following, we describe he crawling in deail. A. Mozilla Firefox Web Browser Today, Firefox is one of he mos popular browser due o is availabiliy on almos all plaforms. We use he browser o emulae he user s surfing behavior and he nework raffic on a deskop, o achieve he same behavior as a browser has when he user surfs he web on i. This includes running scrips, which can load oher conen as described in he HTML file. Wih a Java crawler his could no be achieved. Firefox exensions enable o auomae he crawling hrough scriping. Add-ons help o coun and measure downloaded objec sizes. For he scriping and auomaion work we used Chickenfoo [20] and he measuremen of file sizes and he objec coun were done wih WebDeveloper [19]. Problems we were facing when downloading he web pages using Firefox were worms, viruses, and bugs of he Firefox browser by he parsing of he page sending iself ino nirvana. For hese problems we used VMware worksaion wih snapshos. B. WebDeveloper and Chickenfoo WebDeveloper [19] is coded by Chris Pederick in JavaScrip. I is an add-on for Firefox wih a huge se of funcions o ge informaion abou he currenly loaded web page in he browser. Afer loading each web page, Chickenfoo calls he funcion of WebDeveloper o display page informaion abou objecs and heir sizes in a new window. Chickenfoo s scriping language can be used o manipulae web pages and for auomaed web browsing. I herefore adds a paern maching sysem for idenifying elemens in he rendered model [20]. Embedding auomaion ools inside a browser guaranees he same look and feel of a sie ha a user accesses wih all he inline-objecs and dynamic conen. Chickenfoo is able o conrol he browser, grab conen informaion from a sie, and auo fill in forms. We used i o ge and send daa o our daabase. 1 hp:// /12/$ IEEE 135

4 During he loading of he web page, Chickenfoo commands are suspended. When he sie does no load wihin 30 seconds, i hrows an excepion and he whole crawling is sopped. This sandard seing caused many problems wih Chinese web pages. Therefore, we increased he imeou o 300 seconds for all web page downloads. Afer he sie is loaded, Chickenfoo sends an even o he Acivae WebDeveloper Exension, which calls he funcion webdeveloper_viewdocumensize(); from WebDeveloper. Afer he new ab opens and he informaion is moved o he curren ab, Chickenfoo sars o parse he able of WebDeveloper. I collecs he URLs of documens, images, objecs, syleshees, and scrips from he able shown on he screen colleced from he Documen Objec Model (DOM) of Firefox. If here are any s loaded dynamically by a scrip, his mus be found by WebDeveloper in he DOM. Afer all informaion is colleced, he crawling scrip sores he daa in our MySQL daabase and sars anoher crawling session. C. VMware Worksaion Crawling one million web pages confrons he browser wih viruses, bugs, popup windows, and oher surprises. In addiion, pop-up windows sop he crawling waiing for a user ineracion. On every 20-50h web page we observed a disurbing elemen ha makes he crawling difficul. We crawl using a virual machine running inside of VMWorksaion and using snapshos, we refresh he whole operaing sysem wih he crawling every 5 minues. We sared he crawling scrip and made a snapsho of he virual machine. When he crawling scrip sared, i wais 20 seconds unil he crawling sars. We needed 2 seconds o snapsho he virual machine and he remaining 17 seconds are needed every ime, afer he snapsho is resored, o load he VM ono memory and o sar. To use more ors, we ried o run more han one VM bu he nework address ranslaion of he VMWorksaion was unaccepably slow. Wih a single virual machine afer ens of visied sies, he NAT seems o slow down oo. Thus, every ime we resar he virual machine, we resar he VMware NAT service oo. Refreshing he NAT service on he hos operaing sysem susains he speed of he crawling on he gues VM. IV. RESULTS AND HTTP MODEL Firs, we ake a look a he number of s per HTML web page. This is shown in Figure 3(a) spli ino he differen objecs. No surprising, mos embedded objecs are images. On average, 23 images are placed on a web page. The maximum number of images we measured on a web page was The second larges number of s are scrips. These scrips, generally Javascrip, are used for user ineracions, e.g. buon reacions, bu can also include HTML 5 video players. On average, 4.8 scrips are included in a web pages. This quie large number of scrips per documen was also observed by Ihm e al. [17]. In addiion, he paper shows an increase of he number of embedded scrips from 2006 o Wih he spread of HTML 5, we expec ha his number will furher increase. Alhough he number of objecs embedded on a web page seems o be quie low, we have o be aware of he fac ha each objec presens a linked video file. The larges number of embedded video files was measured on a web page conaining adul conen and included 124 objecs. To compare our measuremen resuls wih already published papers in he field, we ploed he number of s of four oher measuremens in Figure 3(b). The lowes number of s was observed by Barford and Crovella [12] in and Gupa [14] and Choi and Limb [5] measured a similar number of s per web page wih a mean of abou 5 objecs. This is quie surprising as he paper from and Gupa was published 9 years laer. From he newes sudy, he paper of Ihm e al. [17], we are only able o show he median of he number of s observed in China (CN), Unied Saes (US), Brazil (BR), and France (FR). Alhough he median is wih 14 lower han our median wih 22, i illusraes ha more and more objecs are embedded wihin a web page. Afer having shown he increase in number of embedded objecs over ime, we now wha o ake a look if also he size All s Images Scrips Syleshees Objecs number of s (a) Number of s spli ino objec ype. Choi Barford Ihm CN Ihm US Ihm BR Ihm FR number of s (b) Comparison of number of s wih oher models. Fig. 3. Number of s embedded in a web page /12/$ IEEE 136

5 All s Images Scrips Syleshees Objecs size [kb] (a) Size of s spli ino objec ype. compressed main objec size [kb] (a) Size of main objecs. compressed size [kb] (b) Comparison of size of s wih oher models number of documens (b) Number of documens. Fig. 4. Size of s embedded in a web page. Fig. 5. Main objec size and number of documens. of he s has increased. The resuls, again separaed for he differen embedded objec ypes are illusraed in Figure 4(a). The larges measured was an image wih a file size of 8 MB. Alhough he larges embedded objec was an image, on average he embedded objecs are much larger han he picures wih a mean of 101 kb. I has however o be menioned ha a flash video embedded in a web page is no couned wih is complee file size, bu only he objec iself, which has a size of abou 3 kb. This explains also ha 10% of he measured objecs are 3 kb in size. Finally, we can also observe from he figure ha abou 6% of he scrips have a size of 24 kb. To see if he size of he number of s has increased over ime, we compare our resul wih he measuremens from and Gupa in Figure 4(b). Two ineresing hings can be observed from he figure. Firs, he percenage of large s remained similar compared o he resuls aken from and Gupa in Sill, abou 7% of all embedded objecs have a size larger han 50 kb. Second, he average size of he objecs has increased. Choi and Limb measured an average size of 7.7 kb in and Gupa measured an average size of abou 12 kb in 2007, whereas our average size almos doubled wih 23.9 kb. In addiion, we ploed he size for compressed conen. Compression of s can decrease he download ime from a web server. Main objecs as well as inline objecs are available in boh compressed and uncompressed form. According o Desounis e al. [21], downloading only he compressed objecs can save beween 30% and 50% of he bandwidh. In our case, he median objec size decreased from 10,284 Byes o 7,338 Byes, which is a saving of abou 29%. Similar o he increased size, also he size of he main objec increased as illusraed in Figure 5(a). While and Gupa observed an objec size of less han 12 kb on average, we measured a size of 31.6 kb or 22.5 kb when he conen is compressed. Also he maximum main objec size is wih abou 8 MB larger compared o a maximum of 2 MB observed in In addiion o he s and he main objec, several addiional documens are inegraed in oday s web pages, which are loaded from oher web servers. While in former imes, he complee web page was loaded from one /12/$ IEEE 137

6 Parameer Mean Median Max TABLE II HTTP MODEL PARAMETERS. Sandard Deviaion Bes fi Main objec size 31,561 Bye 19,471 Bye 8 MB 49,219 Bye Weibull ( ,14944) Compressed 22,468 Bye 11,535 Bye 8 MB 41,295 Bye Weibull ( ,71807) Number of main objecs Lognormal µ = 73844, σ = Inline objec size 23,915 Bye 10,284 Bye 8 MB 128,079 Bye Lognormal µ = , σ = Compressed 21,208 Bye 7,338 Bye 8 MB 127,979 Bye Lognormal µ = , σ = Number of s Exponenial µ = Reading ime [14] 39.7 s - 10,000 s s Lognormal µ = 95204, σ = server, conaining one main objec or if frames are used some addiional documens, he web pages are now creaed wih documens from differen web servers. The main reason for his are social neworks. Several web pages now conain like i buons from facebook, wees from wier, ec. These are direcly loaded from he social neworks, e.g. like.php or wee buon.hml. We hink ha he number of documens inegraed ino he web page will increase even more, especially when looking a dynamically creaed web pages such as i-google, where he conen is combined according o he user s preference. The number of hese inegraed documens is shown in Figure 5(b). The x-axis shows he number of documens including he main objec using a logarihmic scale. Alhough 60% of all web pages jus conain he main objec, 10% of all web pages are composed of more han 5 documens. While on average, 1.2 documens wih an average size of kb are inegraed in addiion o he main objec, he larges number of inegraed objecs was 211. This underlines he rend owards dynamically creaed web pages, bu necessiaes similar download imes from all requesed web servers. The measured web page parameers are lised in Table II wih saisical analysis in erms of mean, median, and variance. In addiion, Table II provides he bes fiing funcions for he parameers including he esimaed parameers. The fiing of he main and size is also shown in Figure 6. size main objec size compressed Fied compressed Fied objec size [kb] Fig. 6. Fiing of main and size. The main objec size, boh in compressed and uncompressed form can be bes fied wih a Weibull funcion. Using a Weibull disribuion o fi he size has a larger error han using he Lognormal disribuion. Alhough i does no fi perfecly due o some very large s, he error is small enough so ha all fiings can be used for simulaing or emulaing web browsing users. To sill be able o model a web browsing user wih an on/off, we recommend o use he reading ime from and Gupa [14] wih he parameers lised in Table II. V. CONCLUSIONS In his paper, we presened a new raffic model for HTTP raffic. In conras o oher web raffic models, which are based on measuremens on campuses or access neworks of small ISPs, our model is based on measuremens of he mos visied web pages. Thus, our model conains web page saisics accessed from web users all around he world and no limied o one counry or even one campus. According o our observaions, we find ha here is a rend owards larger web pages wih an increasing number of inline objecs. The second finding is ha oday s web pages are no loaded from a single web server bu creaed gahering conen from all around he world. This fac is especially ineresing when looking a he user QoE. If he conen is loaded from differen web servers, also he delay for downloading he pars of he web page varies, which has an impac on he perceived QoE of he user. Alhough his is no ineresing for web server and cloud dimensioning, he new feaures inegraed ino oday s web browsers are. We found ha i is possible wih some web browsers o download he conen of linked web pages auomaically afer he requesed web page is downloaded. This causes a lo more raffic when he user requess a web page as no only a single web page is downloaded bu several linked web pages. In fuure work, we will ake a look a he spread of hose link ree downloads and will also include he embedded mulimedia conen in our model. ACKNOWLEDGMENTS This work was also parially suppored by he COST TMA (European Cooperaion in Science and Technology) framework /12/$ IEEE 138

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