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ith the constant increase in the number ofmobile devices in use worldwide, there is afast growing demand for mobile videos.According to the Cisco Visual Networking

Index [1], mobile video traffic has exceeded 50 percent oftotal mobile data traffic in 2011 (597 Pbytes/mo) and a 25-foldincrease by 2016 is expected. The combination of three factorsexplains the popularity of mobile videos: wireless communica-tion technology improvement; the availability of highly flexi-ble, versatile, and video-friendly mobile terminal platforms,such as smartphones and tablets; and finally, the emergenceof major video content providers like YouTube, Netflix, Hulu,and PPTV, which provide a large catalog of attractive con-tents. The conjunction of these three factors has enabledubiquitous access to users who can enjoy their favorite videoson their mobile devices, phone or tablet, wherever they areand whenever they wish.

While a relatively large body of research has targetedunderstanding video on demand (VoD) over Internet charac-teristics [2–6], these works mainly studied fixed scenarios,while mobile devices have particular constraints and limita-tions (processor capacity, bandwidth, memory, energy capaci-ty, etc.) that hinder simply extending conclusions drawn onfixed scenarios to the mobile environment. In particular, whilepeer-to-peer (P2P) TV is now becoming popular in the fixedInternet because of reduced servers’ load and better adapta-tion to network capacity changes, mobile video services arestill based on a client-server approach. Introducing P2P tech-nology into mobile video systems entails leveraging on thepeers’ uploads. As we will see later, these mandatory uploads

make the use of a P2P approach in third generation (3G)mobile networks more challenging, if not impossible.

In this article, we look from the viewpoint of a large-scalecommercial P2P mobile video provider system, PPTV, andexplore the challenges of applying a P2P paradigm to mobilevideo distribution over commercial 3G or 4G wireless broad-band networks. PPTV [7] is currently the largest commercialonline video service in China and the largest media platformworldwide, offering both online live and on-demand videobroadcasting.

Our analysis is backed by real measurements coming fromthe PPTV network and feedback from its design team, whohave experience in running the PPTV mobile video distribu-tion platform. It is noteworthy that our main target in thisarticle is P2P-based mobile video distribution over a 3G wire-less broadband network. This puts aside WiFi-based wirelessnetworks where the problems are different.

We first present some observations and analysis comingfrom a comprehensive measurement of PPTV mobile diffu-sion platform. This analysis extracts the characteristics ofmobile videos viewed through 3G and enables us to studytheir impact on P2P transfer mode. We also briefly describesome practical problems encountered during the developmentof P2P mobile solutions and the deployment process in PPTV.

Related WorkDobrian et al. [2] analyzed the impact of video quality on userengagement, and Li et al. [3] investigated the popularity evo-lution of the contents in a VoD system. Yin et al. [4] present-

WW

AbstractMobile video is becoming extremely popular, and P2P mobile video platforms arebeing considered for large-scale deployment in this context. However, the designand deployment of realistic P2P video systems have to consider specific character-istics of mobile networks. In this article, we look from the viewpoint of a large-scalecommercial P2P mobile video provider system, PPTV, and describe the implementa-tion challenges of a P2P mobile video system over 3G networks. Our analysis isbacked by real measurements and experience from PPTV. We extract from thesemeasurements the characteristics of mobile videos and analyze their impact on P2Pvideo systems. We also briefly discuss other practical issues in the design of amobile P2P system for PPTV.

The Case for P2P Mobile Video Systemover Wireless Broadband Networks:A Practical Study of Challenges for a

Mobile Video ProviderYi Sun, Yang Guo, Zhenyu Li, Jiali Lin, and Gaogang Xie, Chinese Academy of Sciences

Xiaobing Zhang, Shanghai Synacast Media Tech. CO. LTD (PPLive)Kave Salamatian, University of Savoie France

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ed measurement results of a large-scale live VoD systemthrough a dataset provided by ChinaCache, the largest con-tent distribution network (CDN) in China, during the 2008Olympics. However, these works only described measurementsand did not use the obtained insight to design operationalVoD systems. In [5], the challenges and design of the PPLiveP2P VoD system are described, and the results of a large-scale measurement of user behavior and system performanceare discussed. In [6], the authors presented measurementstudies of a large VoD system deployed by China Telecomand analyzed their implications on the design of multimediastreaming systems. Both [5, 6] are in the context of fixedInternet but do not describe mobile scenarios.

In our work, however, we focus on the commercial wirelessbroadband network. We study the viewing behaviors of mobileusers through the dataset obtained from PPTV, and discussthe challenges of developing a large-scale P2P mobile videosystem based on the observations we get.

Dataset DescriptionThe dataset we have used came from a comprehensive mea-surement of PPTV, also referred to as PPLive. PPTV offersaccess, in both live and on-demand video streaming, toapproximately 100 million video clips and a library of morethan 200,000 licensed movies and TV shows. Currently, PPTVhas three different diffusion platforms: an HTTP-based web-site, PC client software, and a mobile platform (containingsome mobile applications for different devices). The contentdistribution relies on a provisioned hybrid CDN-P2P contentdistribution network.

Our dataset is extracted from the logs of the PPTV mobileplatform and covers over 120 TV broadcasting stations, and300 live and 20,000 VoD channels obtained between Decem-ber 1 and 14, 2011. The logs were uniformly sampled, result-ing in a total of 111,702,242 views, out of which 108,851,161views were VoD requests. By filtering out the logs that hadundefined access methods, we obtained 86,521,403 video view-ing logs, corresponding to 3,759,129 users watching 427,316unique videos. Among these, 92.58 percent views wereaccessed via WiFi and 7.42 percent via 3G.

In Table 1 we show an overview of the dataset where weclassified the sessions into different categories according totheir access methods (3G or WiFi) and terminal types (smart-phones or pads). It is noteworthy that almost all viewing camefrom IOS (iPad and iPhone) and Android (aPad and aPhone)devices. For WiFi accesses, the share of views from smart-phones and tablets is similar; for 3G access, most of the viewsare coming from smartphones. Different from U.S. and West-ern European observations, where the market share ofAndroid and IOS in video and data consumption is similar[1], we observed that iPad and iPhone devices have generatedmany more views than aPads and aPhones. This might be dueto the particular device market in China. As explained earlier,the main target of our study in this article is 3G networks.

Challenges of Developing a Large-Scale P2PMobile Video SystemIn this section, we describe several insights obtained overmeasurements of the PPTV mobile platform and investigatetheir impact on the P2P transfer mode. In addition, we brieflydiscuss some practical issues for building such a large-scaleP2P mobile video system resulting from our experience in theoperation and design of PPTV.

Difference in Behaviors between Mobile and FixedUsersLet us first give a comparison of viewing behaviors of mobileand fixed immobile users. Figure 1 depicts the viewing com-pletion proportion of video contents stratified in three classesof video lengths: 0~10 min, 20~30 min, and 40~50 min; andtwo types of access to PPTV: standalone PC client and mobileapplication. Each figure contains in its y-axis the proportionof users on the right side, and the number of users on the left,whereas the x-axis shows the video completion proportion, thepercentage of the video that has been viewed before the userdisconnected. Each figure contains in blue a histogram and inred a cumulative distribution function (CDF). As we can see,video length has a clear impact on the viewed proportion ofvideos, especially for mobile users. Users tend to watch morethan half of short videos, while long videos are rarely seenbeyond 70 percent of their length. Compared with fixed clientusers, mobile users generally see a smaller proportion of themovies, with a high percentage (almost 50 percent for medi-um and long videos) watching less than 25 percent of thevideo length. This can be explained by the higher sensitivity ofmobile users to battery consumption, bandwidth usage, or thepoor quality of the video viewing experience in wireless net-works.

The above observation shows the challenges of designing amobile P2P video system. First of all, P2P requires more timeto establish connections between nodes compared to client-server mode. However, as mobile user viewing time is veryshort, this connection time can become comparable with thepatience limit of the users before leaving. Moreover, the factthat in a mobile environment users usually do not finishwatching a whole video results in a very limited number ofcopies of the end parts of the video file being available in thenetwork. As P2P downloading performance depends heavilyon the number of resource copies in the network, this pointgreatly hinders the usability of a P2P system.

Comparison of Behaviors of Different Categories ofMobile UsersIn this section, we further focus on mobile user behaviors andinvestigate the behavioral differences of mobile users usingdifferent access modes and devices.

We show in Fig. 2 the CDF of the viewing time for the twodifferent access methods. Surprisingly, a considerably largefraction of views last less than 10 s, and the number of longviewing times decreases sharply. This can be explained byobserving that mobile users are cautious about their data traf-fic and energy consumption, so they leave the video quickly ifit is not what they expected. We can observe that the viewingtime is generally longer for WiFi users than 3G ones. A moredetailed view shows also some artifacts in the tail of the WiFiviewing time CDF that correspond to the typical length of ani-mation and TV series. These artifacts are not observed for 3Gusers, giving another difference between 3G and WiFi users;while WiFi users view these types of videos, 3G users rarely

Table 1. Dataset statistics.

Accessmethod

Percentage of views from

iPad iPhone aPad aPhone Others

3G 1.02 73.84 4.35 20.78 ~0

WiFi 40.86 43.52 4.03 11.58 0.01

WiFi+3G 37.91 45.57 4.17 12.34 0.01

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finish watching a full video. This point is fundamental as itmeans that stable users that are essential for the stability of aP2P network [8], are less frequent among 3G users than forWiFi users.

Additionally, we compare the distribution of viewing timeover different mobile devices in 3G and WiFi networks. Weshow in Table 2 the median (50th percentile) and the 95thpercentile of the view time of these devices. These results con-firm that users using pads view longer videos online thanthose using phones. The fact that pads have larger screensand higher processing capacities, resulting in a better viewingexperience, largely explains this observation. A second reasonis that phones are more mobile than pads. Indeed, devicemobility is a challenge for P2P transfer mode, as P2P connec-tions are difficult to maintain with high user dynamicity.

Impact of Different Business Models of MobileOperators on the Behaviors of UsersThe business model of 3G operators is generally differentfrom classical IP service providers. While wired network Inter-net service providers (ISPs) generally charge a flat rate forunlimited upload and/or download, 3G customers pay per

upload/download consumption (volume-based) or have a lim-ited upload/download allowance. WiFi providers also followthe model of generally applying a flat rate (or free accesswhen connected to a free hotspot) with unlimited volume. Inthis section, we analyze the impact of different business mod-els of mobile operators on user behaviors. To eliminate theimpact of different access methods, we only focus on 3Gusers. Fortunately, since last year, PPTV and China Unicom(Shanghai) have started a new cooperation. 3G users of ChinaUnicom (Shanghai) are paying a flat rate (10 CNY/mo) towatch PPTV mobile platform videos. These users are there-fore charged differently from other users, who are chargedfollowing a volume-based model. This agreement has enabledus to study the impact of the business model on mobile users’viewing behavior. For this purpose, we have calculated sepa-rately, for the group of mobile users charged a flat rate andthose charged per volume, the metrics: the mean viewing timefor a single video, the mean number of videos viewed per day,and the cumulative viewing time per day. We thereafter calcu-lated the ratio of each metric for the two charging schemes.We show in Fig. 3 these ratios derived for each day. Our firstobservation is that the charging model has almost no impacton the single-view average viewing time (the relative ratio

Figure 1. Distributions of video completion rate.

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remains between 99 and 107 percent). Therefore, we can con-clude that the main factors that influence the average viewingtime per view are user expectation to the video and quality ofexperiences. Nonetheless, the charging model impacts users’access to the mobile video platform of PPTV: flat chargedusers watch more videos (the mean number of views increasesfrom 39 to 65 percent in different days), and the user cumula-tive time spent on the platform per day also increases inalmost the same proportion (from 38 to 74 percent).

Despite pioneering agreements between network operatorsand mobile video service providers, currently the main pricingstrategy for 3G users is still volume-based. This creates majorissues for P2P networks, where participating users are requiredto do both uploading and downloading. As people with the vol-ume-based model have to pay for the uploading traffic that hasno direct benefit for them, they are likely to stop uploading andbecome leechers, significantly deteriorating the P2P network effi-ciency. One possible solution to this issue can be to let mobileusers only download contents and asking only fixed users of aP2P network to upload contents. Unfortunately, this policy willnot be very effective. According to our analysis of PPTV logs, wefind that users of different platforms have different interests; forexample, while fixed client users frequently see complete moviesand live sports shows, 3G users are more likely to watch movietrailers and sports highlights. Therefore, videos favored by 3Gusers may still not have enough copies in the fixed-line network,resulting in poor downloading performance for 3G users.

A solution to provide help for mobile (or other) users can beadopting a push approach consisting of pushing contents towired users in the network even if they do not request them.This ensures that there are enough copies available in the net-work, but at the cost of unfairness to fixed wired users who haveto give service to mobile users without any benefits in return.

In conclusion, the current volume-based business model of3G networks hinders mobile users in uploading data,as it will negatively affect them directly. Moreover,even if a move to flat-based charging happens, it willnot change, as described, the mean viewing time forsingle views, and the famine of ending parts of videoswill still be an issue. These two points appear to bemajor barriers to the wide spread of P2P mobile videosystems.

Difficulty in Traffic Localization for a MobileP2P SystemUser locality evaluates the geographic collocation ofusers watching the same video. Traffic localizationschemes [9, 10] are often used by P2P service providers

to reduce the interdomain P2P traffic and improve download-ing performance by selecting peers located in the samedomain in physical networks.

Let us first describe the geographic distribution of videoresources popularity on the PPTV 3G mobile platform. Forthis sake, we have divided locations in China into 33 differentdomains, each corresponding to a province. For the purposeof describing the geographical disparity, we have randomlychosen four popular videos with different types and lengths,and will describe them. We evaluate the user locality by thelocality entropy of a video i defined as:

(1)

where K = 33 is the number of domains, uik is the number ofusers in the domain k watching video i and ui = SK

k=1 uik isthe aggregated number of users of video i. The locality entro-py is between 0 and 1, where 0 means that the video is onlyviewed by users from a single domain, and 1 means that thevideo is viewed by users uniformly distributed among all 33domains.

Figure 4 shows the evolution of the entropy for the first 7days of the observation period. The entropy values for allvideos are around 0.8, meaning that users are almost evenlydistributed over all domains. We also observe a daily patternfor the entropy evolution. The entropy reaches its minimum at6 a.m. each day (i.e., the time with the fewest users). The evendistribution of users in different domains indicates the rele-vance of a traffic localization scheme for P2P transfer mode ina 3G network. Otherwise, the neighboring peers of a usermight reside in any of the 33 domains with almost the sameprobability, generating huge interdomain traffic and signifi-cantly reducing the downloading performance of P2P users.

Most of the existing traffic localization methods [9, 10]used by P2P service providers are based on the geolocationestimation of a node by its IP address. This is effective forwired Internet and WiFi users, as the IP address prefixes ofthe nodes in such networks are shared with the access router.However, the situation is more complicated in a 3G network,as the IP address of a node is allocated by the gateway Gener-al Packet Radio Service (GPRS) support node (GGSN) of thenetwork. Usually, the GGSN selects an available IP addressfor a node, and this is not related to the node’s geographiclocation. In [11] it is shown that 3G users hundreds of milesapart share the same IP address space. Therefore, reverseengineering from IP address to geolocation is not usable in3G networks, and the current P2P traffic localization schemesare not suitable under this scenario.

To get the precise location of a mobile node in a 3G net-

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Figure 2. Viewing time for different access modes.

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Table 2. Impact of devices on viewing time.

DeviceWiFi 3G

50th prctile 95th prctile 50th prctile 95th prctile

iPad 121 s 45.1 min 8 s 27.6 min

iPhone 44 s 34.7 min 6 s 20.7 min

aPad 80 s 45.8 min 16 s 29.1 min

aPhone 58 s 42.8 min 2 s 24.7 min

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work, we propose two possible solutions. First, cooperativemobile operators can easily provide the positions of mobileusers to P2P video service providers. Sharing this informationcan be beneficial to the mobile operators as it reduces thetraffic load on their precious and costly interdomain links.Another possible solution for P2P service providers is to usethe GPS modules embedded in most mobile devices to get theprecise locations of users to implement traffic localizationschemes.

Other Practical Issues for Designing a Real Large-Scale P2P Mobile Video SystemThis section briefly discusses some practical issues resultingfrom our experiences in operating and designing PPTV.

First of all, because of the lack of IP addresses and thelarge number of mobile terminals in a 3G network, NetworkAddress Translation (NAT)-based IP address sharing is imple-mented in 3G networks, meaning that a NAT traversal mech-anism [12] should be implemented to establish and maintainIP connectivity with terminals behind NAT. However, in 3Gnetworks, NAT boxes are generally centralized to simplify net-work management. These boxes deployed by mobile operatorshave to maintain a large number of sessions and IP addresstransformation status coming from hundreds of thousands ofdevices in the 3G network. The situation is likely to worsen ifP2P technology is used in a mobile network, as each mobileterminal will have to maintain several connections with differ-ent nodes to download and upload data, and as a result thenumber of sessions in the NAT devices will become even larg-er. According to PPTV experience, sometimes the mobileISPs block connections to reduce the burden on their NATservers. This brings new obstacles for mobile P2P applicationsover wireless broadband networks.

Another issue is relative to the mobile devices still beinglimited in storage. Even if typical storage is currently as largeas 4~16 Gbytes for smartphones and 16~64 Gbytes for pads,the volume of information in the form of images, music, andapplications stored on them is also steadily growing. P2Papplications usually need a large amount of free space [13] forcontent buffering, which is still considerable for mobile termi-nals. However, another limitation exists: the current flash stor-age technology used for storage in a mobile terminal is limitedin the amount of writing (around 5000 program/erase cyclesper memory cell are reported for flash memory commonlyused in mobile devices [14]), while no limitations exists forreading. The SSD disk controllers used in PCs implement alot of optimization on writing uniformly on the whole storagedisk. However, most mobile devices still use the old FAT (or

FAT32) format for organizing their flash disk, which is notadapted to writing optimization to the whole disk space,resulting in writing operations concentrating on the samememory cells and significantly reducing the lifetime of thestorage in mobile devices. This limitation should be particular-ly considered in designing P2P applications that have to fre-quently write and rewrite their buffered contents. In ourdesign of PPTV, we have made a careful choice on the size ofthe chunk in order to try our best to reduce the concentrationof writing operations on the same storage cells.

Another significant limitation of mobile devices is band-width and large variability of the network connection quality.As observed earlier, lots of users leave a video session becauseof the bad viewing experience. This leads to frequent topologychange in P2P networks, generating instability that worsensthe downloading experience.

Last but not least is the battery capacity problem. Upload-ing information is one of the most power-consuming opera-tions in a mobile platform. Moreover, it is difficult for a P2Pplatform to let the communication transceiver go into sleepmode, which is one of the major strategies used to save bat-tery consumption. This means that P2P applications formobile video distribution are likely to greatly reduce the bat-tery life of mobile devices. This is indeed unacceptable forusers [15].

ConclusionIn this article, we analyze the challenges a content providerhas to face in order to implement and deploy a P2P mobilevideo distribution system over a wireless broadband network.This analysis was backed by real measurements and industryexperience. Designing a P2P video distribution system for amobile environment is quite different from designing it for afixed one. In particular, the fact that mobile users usually donot view a whole video and leave it early, added to the 3Gcharging model, the difficulty of geolocalization, the obliga-tion to manage more NAT sessions, and the lack of capacityof mobile devices in terms of storage, bandwidth, and battery,bring strong limitations to the development of P2P-basedmobile video distribution systems over 3G networks.

Indeed, in the mid-term, some of the above issues might besolved. For example large deployment of IPv6 can solve theissues with NAT, or new storage technologies might reducethe negative impact of rewriting information on mobile stor-age. However, the accumulation of the above challengesseems to be unbearable, as any of the above issues alonemight block the deployment of a P2P based mobile video dis-tribution systems over 3G networks. We are therefore not

Figure 3. User behaviors under different 3G business models.

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Figure 4. Locality entropy over seven days for four popularvideos.

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very optimistic on the emergence of such systems for commer-cial mobile video distribution.

AcknowledgmentThis work was supported by a grant from the National

Basic Research Program of China under Grant No.2012CB315806, the National Science Foundation of Chinaunder Grant Nos. 61003266, 61100178, 61070188, and61100176, and the NSFC-ANR pFlower project under GrantNo. 61061130562, and was also sponsored by the ShanghaiScience and Technology Committee (No. 11dz1500700).

References[1] Cisco Visual Networking Index, “Global Mobile Data Traffic Forecast

Update, 2011–2016,” technical report, Cisco, 2012.[2] F. Dobrian et al., “Understanding the Impact of Video Quality on User

Engagement,” Proc. SIGCOMM, Toronto, Canada, 2011.[3] Z. Li et al., “Watching Video from Everywhere: a Study of the PPTV

Mobile VoD System,” Proc. IMC, Boston, MA, 2012.[4] H. Yin et al., “Inside the Bird’s Nest: Measurements of Large-Scale Live

VoD from the 2008 Olympics,” Proc. IMC, Chicago, IL, 2009.[5] Y. Huang et al., “Challenges, Design and Analysis of A Large-Scale

p2p-Vod System,” Proc. SIGCOMM, Seattle, WA, 2008.[6] H. Yu et al., “Understanding User Behavior in Large-Scale Video-On-

Demand Systems,” Proc. EuroSys, Leuven, Belgium, 2006.[7] PPTV, http://www.pptv.com.[8] F. Wang, J. Liu, and Y. Xiong, “Stable Peers: Existence, Importance,

and Application in Peer-to-Peer Live Video Streaming,” Proc. INFO-COM, Phoenix, AZ, 2008.

[9] H. Xie et al., “P4P: Provider Portal for Application,” Proc. SIGCOMM,Seattle, WA, 2008.

[10] R. Binda et al., “Improving Traffic Locality in Bittorrent via BiasedNeighbor Selection,” Proc. ICDCS, Lisboa, Portugal, 2006.

[11] M. Balakrishnan, I. Mohomed, and V. Ramasubramanian, “Where’sThat Phone?: Geolocating IP Addresses on 3G Networks,” Proc. IMC,Chicago, IL, 2009.

[12] P. Srisuresh, B. Ford, and D. Kegel, “State of Peer-to-Peer (P2P) Com-munication Across Network Address Translators (NATs),” IETF RFC5128, 2008.

[13] S. Shakkottai, R. Srikant, and L. Ying, “The Asymptotic Behavior ofMinimum Buffer Size Requirements in Large P2P Streaming Networks,”IEEE JSAC, vol. 29, issue 5, May 2011, pp. 928–37.

[14] M. G. Laura, D. D. John and S. Steven, “The Bleak Future of NANDFlash Memory,” Proc. FAST, San Jose, CA, 2012.

[15] S. Deng, Reducing 3G Energy Consumption on Mobile Devices, M.S.thesis, MIT, 2012.

BiographiesYI SUN (sunyi@ict.ac.cn) has been an associate professor at the Institute ofComputing Technology (ICT) since 2009, and an adjunct associate profes-sor at Macquarie University, Australia. His research interests cover networkresource management, mobile computing, and flow distribution in hetero-geneous wireless networks. Since 2007, his research started to focus onfuture network design, including service oriented routing and traffic opti-mization. He has published more than 50 academic papers.

YANG GUO (guoyang@ict.ac.cn) is a Ph. D candidate at ICT, ChineseAcademy of Sciences (ICT/CAS). He received his B.S. degree from WuhanUniversity, China, in 2011. His research interests include Internet architec-ture and mobile Internet.

XIAOBING ZHANG (davidzhang@pplive.com) is the co-founder ofPPLive/PPTV. Before he joined PPLive, he was a lecturer at Huazhong Uni-versity of Science and Technology. Since 2005, he has worked for PPLiveand focused on P2P architecture design and protocol implementation. Hehas a lot of experience in live streaming, video on demand, CDN, DRM,and so on. He was a member of the IETF ALTO Working Group.

ZHENYU LI (zyli@ict.ac.cn) received his Ph.D. degree from ICT/CAS in2009, where he now serves as an associate professor. His research inter-ests include future Internet design and Internet measurement.

JIALI LIN (linjiali@ict.ac.cn) is a graduate student at ICT/CAS. He receivedhis B.S. degree from Xiamen University, China, in 2010. His research inter-ests include network measurement and online social networks.

KAVE SALAMATIAN (kave.salamatian@univ-savoie.fr) is a professor at theUniversity of Savoie. His main areas of researches are Internet measure-ment and modeling and networking information theory. He was previouslya reader at Lancaster University, United Kingdom, and an associate profes-sor at Université Pierre et Marie Curie. In 2011 he was a visiting professorat CAS.

GAOGANG XIE (xie@ict.ac.cn) received his Ph.D. degree in computer sci-ence from Hunan University in 2002. He is a professor at ICT/CAS. Hisresearch interests include future Internet architecture, programmable virtualrouter platforms, and Internet measurement and modeling.

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