Enhancing Internet-scale Video ServiceDeployment Using Microblog-based Prediction

Zhi Wang, Member, IEEE, Lifeng Sun, Member, IEEE, Chuan Wu, Member, IEEE, andShiqiang Yang, Senior Member, IEEE

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Abstract—Online microblogging has been very popular in today’s In-ternet, where users follow other people they are interested in andexchange information between themselves. Among these exchanges,video links are a representative type on a microblogging site. Theimpact is fundamental — not only are viewers in a video service directlycoming from the microblog sharing and recommendation, but also arethe users in the microblogging site representing a promising sample toall the viewers. It is intriguing to study a proactive service deploymentfor such videos, using the propagation patterns of microblogs. Basedon extensive traces from Youku and Tencent Weibo, a popular videosharing site and a favored microblogging system, we explore how videopropagation patterns in the microblogging system are correlated withvideo popularity on the video sharing site. Using influential factorssummarized from the measurement studies, we further design a neuralnetwork-based learning framework to predict the number of potentialviewers and their geographic distribution. We then design proactivevideo deployment algorithms based on the prediction framework, whichnot only determines the upload capacities of servers in different regions,but also strategically replicates videos to these regions to serve users.Our PlanetLab-based experiments verify the effectiveness of our design.

Index Terms—Online microblogging, video streaming, video servicedeployment

1 INTRODUCTION

Recent years have witnessed the blossom of microblog-ging services in the Internet, e.g., Twitter, Google+, Plurk.In a microblogging system, users can create and maintainsocial connections among each other, as well as sub-scribe to contents shared by others from external contentsharing system, as followers [1]. Among the variety ofcontents to exchange, links to videos on video sharingsites are a popular type — users from microbloggingexchanges are constituting a large portion of viewers inYouTube-like video sharing sites [2]. Popularity patterns

Z. Wang is with the Graduate School at Shenzhen, Tsinghua University,Shenzhen, China (email: wangzhi@sz.tsinghua.edu.cn). L. Sun and S. Yangare with the Department of Computer Science and Technology, TsinghuaUniversity, Beijing, China (e-mail: {sunlf,yangshq}@tsinghua.edu.cn).C. Wu is with the Department of Computer Science, the University of HongKong, Hong Kong (e-mail: cwu@cs.hku.hk).This work is supported in part by the National Basic Research Programof China (973) under Grant No. 2011CB302206, the National NaturalScience Foundation of China under Grant No. 61133008, 61210008, and61272231, the research fund of Tsinghua-Tencent Joint Laboratory for InternetInnovation Technology, Beijing Key Laboratory of Networked Multimedia,and a grant from Hong Kong RGC under the contract HKU 717812E.

of such socialized videos have greatly changed as follows:(1) video popularity is highly effected by the onlinesocial networks; (2) the popularity becomes more instan-taneous [3]. These changes make traditional popularity-based approaches for video service deployment subop-timal, if not completely ineffective [4].

Since a microblogging system is closely connectedto many content sharing sites, it ideally samples valu-able information about how users produce and sharecontents from those sites. Video propagation models ina microblogging system can be exploited for a betterprediction of video popularity patterns, so as to improvethe service quality of a video sharing system In this pa-per, we advocate to exploit the sampling and predictioncapabilities of a microblogging system to provide betterInternet video services.

In a typical video sharing site today, large volumes ofvideos are uploaded by users, with viewers from all overthe world. In 2013, more than 100 hours’ worth of videoswere uploaded every minute in YouTube, serving up to1 billion unique viewers every month [5]. A commonpractice to provide these video services is to replicatevideos in servers at different geographic locations [6],but it is impractical to replicate all the videos in everylocation. An effective and adaptive replication strategyto serve the dynamic demand for different videos indifferent geographic regions, is in need.

In this paper, we propose to exploit video sharingpatterns from a microblogging system for this purpose.The potential benefits are two-fold: (1) a video sharingsite typically has no information about how video viewspropagate among its users, while a view propagationmodel could enable more effective view prediction; (2)the exchanges of video links in a microblogging systemtypically happen earlier than the actually video viewson a video sharing site, and the time lag between bothevents can allow more timely and proactive deploymentof videos. Based on our preliminary findings of theconnection between the popularity of a video and howthe video is shared in a microblogging system [7], inthis paper, we focus on employing video microblogpropagation patterns to improve the deployment ofvideo services. Our contributions can be summarized as

follows.▷ In Sec. 3, we explore connections between mi-

croblogging exchanges of video links and popularityof videos, based on extensive traces collected fromTencent Weibo (hereafter, Weibo, a Twitter-like Chinesemicroblogging system) and Youku (an Internet videosharing site with immense popularity in China). Weidentify important characteristics of Weibo, which influ-ence video access patterns on Youku: (1) the numberof users that have imported a video to Weibo, (2) thenumber of users which re-share links to the video to theirfollowers, (3) the number of followers that the videolink share can reach, and (4) the geographic distributionof Weibo users.▷ In Sec. 4, we exploit these influential factors in the

design of a neural network-based learning framework,for predicting the number of potential viewers of differ-ent videos and the geographic distribution of viewers.The accuracy of our prediction models is verified bytrace-driven cross-validation experiments, as comparedto a classical approach of linear regression-based fore-casting.▷ In Sec. 5, we further design a proactive video

deployment algorithm based on the prediction frame-works. The algorithm can significantly improve thevideo download performance of users, according to ourtrace-driven experiments.

2 RELATED WORK

Many architectures have been proposed to implementa large-scale video service, which distributes videosto users across the Internet as follows. (1) Server-basedstrategies, e.g., the content distribution networks (CDNs)[8], which have powered today’s dominating HTTPstreaming. (2) Client-based strategies, e.g., peer-to-peercontent distribution were widely used in live videodistribution and on-demand video distribution [9]. (3)Hybrid strategies, e.g., a hybrid CDN and P2P distributionframework [10]. Traditional video distributions generallywork in a passive way, in that video replication andcache are scheduled according to the video access pat-terns perceived by the servers or peers, e.g., using linearregression approach to infer the future popularity of avideo [11].

Video services in the Web 2.0 era focus more on the“social effects”, including user experience, user partici-pation and interaction with rich media. The impact ofsuch social effects is fundamental, because of not onlythe huge amount of videos generated by users, but alsothe change of the video popularity distribution[12]. Liet al. [13] have studied the video sharing in the onlinesocial network, and observed the skewed popularity dis-tribution of contents and the power-law activity of users.Traditional video distribution strategies designed with-out the consideration of such social influence, achievesub-optimal performance in distributing videos in thecontext of online social network. Saxena et al. [14] have

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Fig. 1. Popularity of the sampled videos.

revealed that at some locations, the average service delayof YouTube used to be as large as 6.5 seconds, due to theinefficient replication and distribution strategies [6], e.g.,the slow reaction to the popularity change.

Only by carefully considering the social network in-formation can the video sharing systems effectively dis-tribute social/socialized video contents. Krishnamurthyet al. [15] have investigated an online microbloggingsystem, Twitter, and identified distinct classes of Twitterusers and their behaviors, as well as geographic growthpatterns of the social network. With increasing popular-ity, a microblogging system resembles the real society. In-terests, beliefs, and behavior of users in a microbloggingsystem are representative of those in the real world [16].To exploit the similarities, Ritterman et al. [17] advocatedto forecast a swine flu pandemic based on a belief changemodel summarized from Twitter. In context of contentpopularity prediction based on a microblogging system,different models have been used for prediction in avariety of scenarios, including various linear regressionmodels [18] and machine learning models [19]. Yang etal. [20] investigate the prediction of information diffusionin Twitter, in terms of the speed, scale, and range. Szaboet al. [21] study how to predict the popularity of con-tents on Digg and YouTube, using their own historicalpopularities. To the best of our knowledge, we are notaware of any existing study exploiting characteristics of amicroblogging system to predict video access in anotherexternal video sharing system.

3 MEASUREMENTS AND ANALYSIS

3.1 Collection of Traces

We have obtained traces from Youku and Weibo asfollows.

Youku. In our study, we collected 2, 291 representativevideos from 5 popular categories on Youku, including“Music”, “News”, “Entertainment”, “Baby” and “Orig-inal”. As video sharing systems usually do not sharedetailed video popularity information, we crawled theview numbers of the videos periodically, so as to studytheir popularity change over time. The crawling wascarried out during June 20 to June 30, 2011, on an hourlybasis to avoid being blocked from frequent crawling.Each trace log indicates the cumulative number of viewsof each video since when the video was published until

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Fig. 3. The number of views vs. thenumber of re-share users in the previ-ous time slot.

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Fig. 4. The number of views vs. thenumber of influenced users in the pre-vious time slot.

when the log was recorded, based on which we cancalculate the average number of views of a video in eachhour.

These 2, 291 videos cover a large variety of populari-ties, in terms of the number of views and comments. Asillustrated in Fig. 1(a), each sample represents the totalnumber of views of a video since its publication untilthe end of the ten days, versus the rank of the video.In Fig. 1(b), each sample represents the total number ofcomments posted by users (Youku allows users to postcomments to videos) starting from its publication to theend of the ten days, versus the rank of the video. Weobserve that our dataset contains representative videosin a sense that they cover a large variety of popularities.

Tencent Weibo. We obtained Weibo traces from thetechnical team of Tencent, containing valuable runtimedata of the system in the entire span of June 2011.Each entry in the traces corresponds to one microblogpublished, including ID, name, IP address of the pub-lisher, time stamp when the microblog was posted, IDsof the parent and root microbloggers if it is a re-post,and contents of the microblog. We parsed the tracesand obtained 4, 468, 398 microblogs related to the videosabove, i.e., the microblogs that either contain the linksto the videos or are re-shares of the microblogs thatcontain the links. In our study, we also used the socialrelationship between users involved in these microblogs.

3.2 View Number Predictability

To investigate the correlation between video link prop-agation on Weibo and the actual number of viewers inYouku, we study the following measurements in Weibotraces.

Number of Views and Number of Root Users. OnWeibo, different users may introduce links to the samevideo on Youku from time to time, each of whombecomes a root user in this video’s propagation. The moreroot users a video has, the more likely the video canattract more views in the future. With all the sampleswe collected, a Pearson’s sample correlation coefficient[22] of 0.31 is computed from the pairs of the numbersof root users and the numbers of views at a lag of 1

time slot (in this paper, each time slot is 4 hours) forthese videos, showing positive correlation between thetwo quantities, as illustrated in Fig. 2.

Number of Views and Number of Re-share Users.Similarly, when more users are re-sharing (referred toas re-share users) the links to their followers, the moreviews can be expected on Youku. We have computed aPearson’s sample correlation coefficient of 0.29 betweenthe pairs of the numbers of re-share users and thenumbers of views at a lag of 1 time slot. Again, positivecorrelation is observed between the two quantities, asillustrated in Fig. 3.

Number of Views and Number of Influenced Users.The influenced users on Weibo (followers of root and re-share users of a video link, who can see the microblogs ofthe video) may likely become actual viewers on Youkuthemselves. Specifically, a Pearson’s sample correlationcoefficient of 0.15 is derived from the pairs the numbersof influenced users and the numbers of views at a lag of1 time slot, as illustrated in Fig. 4.

3.3 Geographic Distribution Predictability

For video service deployment, we also need informationabout geographic distribution of viewers of differentvideos. Since Weibo users sharing a video link are“samples” of all viewers of that video on Youku, weinvestigate the geographic distribution of Weibo userswho have published a microblog containing a link tothe video, and use it to estimate the distribution of allviewers in Youku. The rationale is that such microblogsare published by root and re-share users of the video,who may well have just viewed the video before postingthe microblogs. Our design in this paper, however, isnot limited to the Weibo sample of viewers’ geographicdistribution.

Given that the majority of viewers of Youku videos arein China, we consider 5 representative regions in China,namely BJ (Beijing), SH (Shanghai), SZ (Shenzhen), CD(Chengdu) and XA (Xi’an), where large CDNs in Chinacommonly deploy data centers [23], and an overseas re-gion, referred to as OS (overseas). We useR to denote theset of these regions, i.e.,R = {BJ, SH, SZ,CD,XA,OS}.

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Fig. 5. Geographic distri-bution of Weibo users in-volved in propagation ofdifferent videos.

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Fig. 6. Correlation be-tween fractions of Weibousers at a region in twoconsecutive time slots.

We map the IP addresses of users in our Weibo traces tothe six regions using an IP-to-location mapping database,and estimate the geographic distribution of viewers ofvideo v at time T by a 6-dimensional vector

GTv = {rBJ

v (T ), rSHv (T ), rSZ

v (T ), rCDv (T ), rXA

v (T ), rOSv (T )},

where rXv (T ) is the normalized fraction of Weibo mi-croblogs containing links to video v, posted by users inregion X in time slot T , and rBJ

v (T )+rSHv (T )+rSZ

v (T )+rCDv (T ) + rXA

v (T ) + rOSv (T ) = 1.

Skewed Geographic Distribution. Fig. 5 plots theaverage fraction of microblogs posted in each regioncontaining links to each of the videos, among all themicroblogs posted for all videos in the ten-day tracespan. We observe that the distribution over differentregions is highly skewed: as large as 40% of viewers ofover 50% of the videos reside in Beijing and Shanghairegions, while very small fractions of viewers are fromthe overseas. This observation indicates that heteroge-neous video service deployment is needed for differentregions.

Predictability of Future Geographic Distribution. Toinvestigate whether future geographic distribution canbe predicted by historical distributions, we plot in Fig. 6the fraction of microblogs posted in a region in timeslot T (i.e., rXv (T ), X ∈ R), versus that in the previoustime slot T − 1 (i.e., rXv (T − 1)) , for each of the regions(X ∈ R) and each of the videos (v ∈ V). Positivecorrelation between the two can be observed, with acorrelation coefficient of 0.29. This observation suggeststhat historical geographic distribution can potentiallypredict the future distribution.

3.4 Impact of Measures at Different Time Lags

Besides observing correlations between numbers ofroot/re-share/influenced users (resp. fraction of Weibousers of a video) in time T −1 and Youku view numbers(resp. fraction of Weibo users of a video) at T , we furtherinvestigate the correlation at different time lags betweenthe two. To avoid the impact of videos that are onlyviewed in a very short time span, we have selected 100videos that have a relatively long popularity span, i.e.,they were regularly viewed by users in 10 days.

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Fig. 7. Influence of the numbers of root/re-share/influenced users on the view number andgeographic distribution at different time lags.

First, for each of the 100 selected videos, we calcu-late the Pearson correlation coefficient between a 10-day series of the number of views of the video onYouku and a 10-day series of the number of root/re-share/influenced users of the video, at different timelags between the two series, respectively. In Fig. 7(a),each sample represents the average correlation coeffi-cient over those of all videos, calculated at a specific timelag between the involved time series. We see that thecorrelation weakens as the time lag becomes larger, andthe correlation coefficients are quite small when the lagis larger than 7 days. Hence, we will use measurementscollected in the previous 7 days for the prediction ofview numbers only.

Second, we plot in Fig. 7(b) the Pearson correlationcoefficients between the fractions of a video’s microblog-gers in the six regions at T and those at different timelags, where each sample is the average over those of all100 videos with data extracted from a 10-day interval.Similarly, recent geographic distributions have more in-fluence on the future geographic distribution, which willbe weighted more in our prediction model in Sec. 4.

4 NEURAL NETWORK FOR VIEW PREDICTION

When making video service deployment decisions, thevideo service provider is interested to know two thingsabout a video in the near future: (1) Will the video attractmore or fewer viewers, so that more or fewer serversshould be allocated to serve the video? (2) Servers inwhich regions should be used, so as to best satisfy theseusers from different locations?

4.1 Using Neural Network as Prediction Model

In our design, we predict the number of views of a video,and the geographic distribution of the viewers, based onhistorical information from the microblogging system,using neural networks.

Merits of using neural networks. (1) Neural networkshave been proven effective for time series prediction [24],as what we are pursuing. (2) According to our mea-surement study, the relationship between the numberof root/re- share/influenced users and the number of

Youku views can be non-linear. Neural networks areeffective for learning such non-linear relationship [25].

The structure of the neural networks. In our study,we train two neural network models, (A) one for theprediction of the total number of views of a video, and(B) the other for forecasting the geographic distribu-tion of viewers. We use a 3-layer feed-forward neuralnetwork for both predictions, since it has been proventhat multilayer neural networks with only one hiddenlayer are universal approximators [26]. In predictingvideo views, the structure of the neural network mayremain for a relatively long time, but the training ofthe networks may take place frequently, using the newtraining dataset in the most recent time slots, as longas the video service provider has enough computationresource.

4.2 Constructing Input Features

To use the social measurements in Sec. 3 as input fea-tures, we need to decide a time window, a frequency forfeature exaction in the time window (i.e., the number oftime slots sampled in the time window), and the weightof each feature in the learning framework.

Time Window. We use a proper time window to avoidnoisy features to be selected for the training. Accordingto Sec. 3.4, the correlation between the number of Youkuviews and its influential Weibo measurements (i.e., thenumbers of root/re-share/influenced users), as well asthe correlation between the geographic distribution ofviewers and its influential measurements (i.e., the histor-ical fraction of users residing in different regions), areweaker when the time lags are larger. Hence, we onlyextract features within the recent 7 days to train neuralnetworks (A) and (B). For a newly published video witha lifetime shorter than 7 days, we use measurementsthroughout its past lifetime.

Frequency. The features are extracted from the follow-ing time slots: T − 1, T − 7, T − 13, . . ., i.e., consecutivefeatures are collected with a time interval of 24 hours(recall that each time slot is 4 hours), to capture the dailypatterns. Let M denote the number of days the featuresare extracted.

Weight. Existing studies have shown that the learningperformance of a neural network model can be im-proved by properly weighting the input features [27]. Weweight the features from different time slots accordingto their levels of correlation with the prediction targets.In Fig. 7(a) and (b), the curves of Pearson correlation co-efficients can be fitted well by generalized bell functionsf(x) = e

1+| x−ca |2b

+ d (in Fig. 7, for simplicity, we denote

a particular bell function by “e(a, b, c) + d”). Hence, weweight the number of root/re-share/influenced users, tobe used as features in neural network (A), by α(x) =

0.61+|x/3|4 + 0.1, and the past geographic distributions ofviewers, which are used as features in neural network(B), by β(x) = 0.85

1+|x/3|4 , where x is the time lag between

the time slots when the prediction target and the corre-sponding features happen, respectively.

In our design, we use RTv (i), ST

v (i), and ITv (i) to denotethe number of root, re-share, and influenced users ofvideo v in the ith time slot in the time window beforeT , respectively. We use GT

v,r(i) to denote the fraction ofmicroblogs of video v posted by users in region r in theith time slot in the time window before T .

Let XTv and Y T

v be the features of the sam-ples for training neural network (A) and neu-ral network (B), respectively. We have XT

v ={α(i)RT

v (i), α(i)STv (i), α(i)I

Tv (i)|i = 1, 2, . . . ,M} in neu-

ral network (A), and features Y Tv = {β(i)GT

v,r(i)|∀r ∈R, i = 1, 2, . . . ,M} in neural network (B).

4.3 Samples for Training and Evaluation

To train and evaluate the neural networks, we firstpre-labeled samples from the traces, and each sampleconsists of the input features and the prediction target(s).In neural network (A), a sample is {XT

v , V̄Tv }, where

V̄ Tv is the vector denoting the level of view number of

video v in time slot T . Using the level of view numberis sufficient for bandwidth allocation, and can providemuch better prediction accuracy than using the exactview number. According to the popularity distribution ofthe videos illustrated in Fig. 1(a), we classify the numberof views N for a video into 5 levels: (1) N < 500,(2) 500 ≤ N < 5000, (3) 5000 ≤ N < 10000, (4)10000 ≤ N < 100000, and (5) N ≥ 100000. V̄ T

v isa 5-dimensional binary vector with V̄ T

v [i] = 1 andV̄ Tv [j] = 0, ∀j ̸= i, denoting that the number of views

belongs to the ith level. The rationale of classifying theview numbers into different levels is that in video servicedeployment, the level of view numbers determines thebandwidth resource needed.

On the other hand, in neural network (B), a sample is{Y T

v , GTv }, where GT

v is a 6-dimensional vector in whicheach element represents the fraction of microblogs of thevideo posted in one of the six regions in R in time slotT , respectively.

In summary, we totally have 35, 000 samples for theneural network training and evaluation, correspondingto 1000 videos, covering both old videos and newlypublished ones. We randomly use 6, 000 of them for theevaluation, and the other 29, 000 for the training process.

4.4 Training Neural Networks

Training Neural Network (A). The output layer in theneural network for predicting the number of views,consists of 5 neurons, corresponding to the elements invector V̄ T

v , respectively. The input layer has 3M nodes,corresponding to the feature set XT

v , i.e., M = 7 for oldvideos, and M = 1, 2, . . . , 6 for new videos publishedM days ago. In the hidden layer, the number of neuronsis decided as follows: we vary the number of hiddenneurons from 15 to 25, and measure the number of

samples whose view numbers can be classified into thecorrect levels, using a validation set containing 25% of allsamples from the training set (i.e., 7, 250 samples out of29, 000). We observe that 15 hidden neurons can achievethe best results in cases of old videos with a lifetimelonger than 7 days, and 18 hidden neurons are neededfor most of the new videos.

Training Neural Network (B). The output layer in theneural network for predicting the geographic distribu-tion of viewers corresponds to the 6-dimensional vectorGT

v . The input layer corresponds to a 6M -dimensionalvector, containing M vectors of viewer geographic dis-tributions (each element is the fraction of microbloggersof video v in each of the six regions) in the previousM days. To determine the number of neurons in thehidden layer, we measure the MSE (mean squared er-ror) between the output geographic distribution vectorand the actual geographic distribution vector. A smallerMSE indicates that the predicted geographic distributionvectors are more accurate, i.e., the differences betweenthe actual geographic distribution vectors and them aresmaller. Neural network (B) for geographic distributionprediction is trained using the same traces as used intraining Neural network (A), and our training resultsgive that 20 hidden neurons provide the best accuracyfor old videos, and 35 hidden neurons for new videos.Next, we will evaluate the accuracy of both the neuralnetwork models.

4.5 Evaluating the Predication Accuracy

We evaluate the accuracy of our neural network modelsusing the 6000 samples. We compare the accuracy ofour neural networks with that of a linear regressionapproach [11], in which the parameters (i.e., γ, ϵ) in thelinear model (i.e., y = γx + ϵ), are calculated based onminimizing the squared error of the M samples — theview numbers or user geographic fractions (i.e., y), andtime points (i.e., x) as used for training samples in ourneural network models.

Fig. 8 shows the evaluation results. We observe thatour neural network models achieve much better pre-diction accuracy than that achieved by the linear re-gression approach. In addition, the number of viewsand geographic distribution of old videos can be betterforecasted, than those of the new videos, due to thefewer number of features in the learning frameworkfor new videos. These results indicate that in today’ssocial video sharing, using social information whichreflects how videos are shared among users throughsocial connections, for predicting video popularity ispromising.

5 ENHANCED VIDEO SERVICE DEPLOYMENT

5.1 Deployment Scheme and Objective

A distributed video service platform involves data cen-ters in different geographic regions. In each data center,

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Fig. 8. Prediction accuracy: a comparison.

there is a shared storage backbone that stores the de-ployed videos, and streaming servers that upload thevideos to the requesting viewers. Better Internet connec-tivity and upload bandwidth can be achieved when auser downloads the video from a server located at aregion that is close to the user [28].

The objective of our deployment algorithms is toproactively decide the total amount of upload band-width to reserve in each data center for this videoservice, which videos to be replicated in each data centerat each time, and how to schedule the upload to viewersof different videos from different regions, so that littlevideo download delay is experienced by the viewers inthe entire system.

Let binary vector FTv = {fT

v,r|∀r ∈ R} denote thereplication plan for video v in time slot T , where fT

v,r = 1indicates that video v is stored in region r in T andfTv,r = 0 otherwise. We use ci,r to denote the gain for

a user in region i to download a video from serversin region r, which is calculated as the inverse propor-tion of the average delay between regions i and r, i.e.,ci,r = 1

RTT (i,r) , where RTT (i, r) is the average round-triptime (RTT) between the two regions (other gain functioncan also apply in our design, e.g., end-to-end bandwidth,etc.). When downloading a video, a viewer in region ifirst tries to request it from servers with the smallest RTT,so as to achieve the largest gain. If servers in that regionare not able to serve this request, the viewer’s requestis redirected to the next best region, until the requestis eventually served. On the other hand, a streamingserver serves all the requests when it has enough uploadbandwidth; otherwise, it selects the requests to serve, byprioritizing those from regions with small RTTs to theregion where the server is located.

5.2 Regional Upload Bandwidth Reservation

Video upload bandwidth in each region is purchasedfrom the respective Internet service providers. Let Wdenote the total budget that the video service provider iswilling to spend on the upload capacity. We decide theamount of upload bandwidth to reserve in each regionaccording to the average number of concurrent requestsVr,∀r ∈ R, and the unit prices Pr, ∀r ∈ R, for uploadbandwidth in different regions. Suppose that every videorequest is served by one unit upload bandwidth.

The upload bandwidth reservation in each region iscarried out every L time slots, considering the practicethat bandwidth reservations are made for long periodsin the real world. We compute Vr as the average numberof concurrent requests from region r in the previous Ltime slots. We determine the bandwidth reservation bysolving the following optimization problem:

maxU

∑r∈R

zr(Ur) (1)

subject to:∑r∈R

PrUr ≤W, and Ur ≥ 0, ∀r ∈ R,

where U = {Ur, ∀r ∈ R}, and Ur is the upload capacityreserved in region r. zr(Ur) is the gain for reservingupload capacity Ur in region r. Suppose cr0,r ≥ cr1,r ≥cr2,r . . . ≥ crR−1,r, where r0 = r and ri ∈ R \ {r}, i =1, . . . , R − 1 (Recall ci,r = 1

RTT (i,r) ). zr(Ur) is defined asfollows:

zr(Ur) =

cr0,rUr, if 0 ≤ Ur ≤ Vr0 ;

zr(Vr0) + cr1,r(Ur − Vr0), if Vr0 < Ur ≤ Vr0 + Vr1 ;

. . .

zr(∑R−2

i=0 Vri) + crR−1,r(Ur −∑R−2

i=0 Vri),

if∑R−2

i=0 Vri < Ur ≤∑R−1

i=0 Vri ;

zr(∑R−1

i=0 Vri), if∑R−1

i=0 Vri < Ur.

The rationale is as follows: When the reserved uploadcapacity in region r is no larger than the amount neededfor serving all requests in the region, all the reservedbandwidth in r is to be used to serve only the requestsfrom r, achieving a gain of cr,rUr. When the reservedupload capacity in region r is more than that neededfor serving this region, the extra bandwidth is first toserve requests from region r1 with the largest cr1,r (i.e.,smallest delay to r), achieving an additional gain ofcr1,r(Ur − Vr); if there is further bandwidth left, it canbe used to serve requests from region r2, and so on.

We design Algorithm 1 to solve the optimal band-width reservation problem in (1): We always allocatea certain amount of upload bandwidth (∆) to regionr′ with the current largest positive marginal gain perunit price z′r(Ur)/Pr, where the marginal gain z′r(Ur) isderived as follows:

z′r(Ur) =

cr0,r, if 0 ≤ Ur ≤ Vr0 ,

cr1,r, if Vr0 < Ur ≤ Vr0 + Vr1 ,

. . .

crR−1,r, if∑R−2

i=0 Vri < Ur ≤∑R−1

i=0 Vri ,

0, if∑R−1

i=0 Vri < Ur.

The amount of upload bandwidth to allocate to regionr′ is computed by ∆ = min(

∑ki=0 Vr′i − Ur′ ,W

′/Pr′),where

∑k−1i=0 Vr′i ≤ Ur′ <

∑ki=0 Vr′i , and W ′ denotes

the remaining budget. r′i, i = 0, 1, . . ., are the series ofregions such that cr′0,r′ ≥ cr′1,r′ ≥ cr′2,r′ ≥ . . .. Regionk is the one selected from the ranked regions whose

Algorithm 1 Upload bandwidth reservation for differentregions (executed every L time slots)

1: procedure UPLOADALLOCATION(W,Pr, cri,r, i =1, . . . , R− 1, ∀r ∈ R)

2: Update Vr∀r ∈ R as the average number ofconcurrent video requests

3: Ur ← 0,∀r ∈ R4: W ′ ←W5: while W ′ > 0 and ∃r ∈ R, z′r(Ur) > 0 do6: Choose the region r′ with the largest

z′r(Ur)/Pr among all the regions7: if ∃k,

∑k−1i=0 Vr′i ≤ Ur′ <

∑ki=0 Vr′i then

8: ∆← min(∑k

i=0 Vr′i − Ur′ ,W′/Pr′)

9: Ur′ ← Ur′ +∆10: W ′ ←W ′ − Pr′∆11: end if12: end while13: end procedure

viewing requests are to be served by servers in regionr′, to achieve the largest gain. Then we deduct Pr′∆from the budget W ′ and repeat the allocation until allthe budget is used up or the upload capacities allocatedcan serve all the requests already.

The complexity of Algorithm 1 depends on the num-ber of regions to allocate the upload bandwidth resourceto. In particular, the algorithm iteratively allocates theupload bandwidth to the “slots” in each region, wherea slot represents the amount of bandwidth allocatedin each iteration. Thus, the time complexity of thisalgorithm is O(|R|2 log |R|), where log |R| is a result ofmaintaining a heap structure for the ranking. Since thereare only tens of regions deployed by a large video serviceprovider, this algorithm can be carried out effectivelyeven in a centralized manner.

5.3 Video ReplicationNext, we use the following heuristic algorithm to repli-cate videos to these regions, as summarized in Algorithm2.

(i) Predicting views using neural network models.The video service provider collects statistics from theonline microblogging system, i.e., the number of root/re-share/influenced users and the geographic distributionof video microblogs in the previous M days, and esti-mates the level of views, V̄ T

v , and the geographic distri-bution of views, GT

v,r, of each video from each region inthe next time slot, using our proposed neural networks.Note that the neural network models are calibrated atthe end of each time slot, based on the newly collectedview numbers and distribution.

Let V Tv,r denote the predicted number of concurrent

video requests from region r for video v in time slotT , which is derived by V T

v,r = avg(V̄ Tv )GT

v,rtvtt

, whereavg(V̄ T

v ) is the average number of views in output levelV̄ Tv . We assume that the video popularity is uniformly

distributed in the popularity groups (1–4), and the aver-age number is then calculated as follows, avg(1) = 250,avg(2) = 2750, avg(3) = 7500, avg(4) = 55000, and forthe last popularity group 5, we let avg(5) = 150000,where avg(5) corresponds to videos with the numberof views larger than 100000 – there are very few videoswith view number larger than 100000 and the averagenumber is closer to 150000, as illustrated in Fig. 1(a). tvis the average service time for a request of video v andtt is the time slot length (tv << tt, since tv is generally atminutes for short videos and tt is 4 hours). In doing so,we seek to use the number of concurrent video requestsfor bandwidth allocation (recall that each video requestis served by one unit upload bandwidth).

(ii) Replicating videos to different regions. Letqv(i, j, F

Tv ) denote the estimated number of concurrent

viewing requests for video v from region i that willbe sent to region j under replication plan FT

v in T .According to the request service scheme discussed at thebeginning of this section, qv(i, j, FT

v ) can be derived asfollows:

qv(i, j, FTv ) =

0, if fT

v,j = 00 fT

v,i = 1, i ̸= j

V Tv,i i = j

V Tv,i∑

k∈R fTv,k

fTv,i = 0

, if fTv,j = 1

.

The rationale lies in the following: (1) if v is not deployedin j or v is deployed in i itself, where i ̸= j, no requestfor video v from region i is to be sent to region j, i.e.,qv(i, j, F

Tv ) = 0; (2) if i = j and the video is deployed in

region i (j), all requests are potentially served locally,i.e., qv(i, j, F

Tv ) = V T

v,i; and (3) if v is not deployedin i but deployed in j, each region caching the videoreceives an equal share of the requests from i, assumingusers’ region preference is uniformly distributed, i.e.,qv(i, j, F

Tv ) =

V Tv,i∑

k∈R fTv,k

. We calculate the gain of video v

under a particular deployment plan FTv as follows:

Bv(FTv ) =

∑r∈R

∑i∈R

ci,rqv(i, r, FTv ).

To find out whether a video should be replicated toa particular region, we evaluate a marginal gain bv,r,which represents the potential improvement in video v’sgain if v is to be deployed in region r, than not deployed:

bv,r = Bv(F′Tv )−Bv(F

Tv ), (2)

where F ′Tv = {f ′T

v,i|∀i ∈ R}, and f ′Tv,i =

{fTv,i, i ̸= r

1, i = r.

We first initialize FTv ← {0, 0, . . .}, ∀v ∈ V , so that

videos will be deployed according to their requests inthe current time slot. Let B = {bv,r|∀v ∈ V, ∀r ∈ R}, andsort B in descending order. To best utilize the reservedbandwidth Ur in each region r, each time we select avideo-region pair with the largest bv,r, deploy video v inregion r in T (or retain video v in region r in T if it isalready deployed) if r still has enough upload capacity to

Algorithm 2 Video Deployment based on Prediction.

1: procedure VIDEODEPLOYMENT(V̄ Tv , GT

v , ∀v ∈ V)2: Initialize FT

v ← {0, 0, . . .}, ∀v ∈ V3: Calculate bv,r, ∀v ∈ V, ∀r ∈ R, according to Eq. (2)4: B ← {bv,r|∀v ∈ V,∀r ∈ R}5: Sort B in descending order6: Q(v, r, FT

v )←∑

i∈R qv(i, r, FTv ), ∀v ∈ V, ∀r ∈ R

7: while ∃r, Ur >∑

v∈V Q(v, r, FTv ) and B ̸= Φ do

8: Pick the largest bv,r in the sorted order of B9: f ′T

v,r ← 1

10: if Ur −∑

k∈V Q(k, r, FTv ) ≥ Q(v, r, F ′T

v ) then11: FT

v ← F ′Tv

12: Update bv,l, ∀l ∈ R13: Re-sort B in descending order14: end if15: Remove bv,r from B16: end while17: end procedure

serve all the requests for the video that will be sent to r,and remove bv,r from B. If v is deployed in r, bv,l, ∀l ∈ Rshould be updated and B is resorted. The steps repeatuntil none of the regions has upload capacity left or thereis no video-region candidate in set B.

The complexity of Algorithm 2 depends on rankingB and maintaining the rank in lines 7–16. The timecomplexity is thus O(|V||R| log |V||R|) ∼ O(|V| log |V|),since |R| ≪ |V|. The algorithm is efficient enoughwhen the candidate video set is small (e.g., the mostrecently published videos are to be deployed) and thedeployment is not adjusted very frequently – we willevaluate the performance of the deployment by varyingthe execution interval in Sec. 5.4.

5.4 Evaluating the Video Service Performance

5.4.1 Setup of Trace-driven PlanetLab Experiments▷ Geo-distributed servers. We implement our algorithmsin C++ and deploy them on PlanetLab [29]. We use5 PlanetLab nodes in China and 1 node in the US,corresponding to the regions studied. Each PlanetLabnode acts as a video streaming server. This setting isonly limited by the traces, and our design can scale whenmore regions are used in the prediction.

▷ Video deployment and user requests. We use the 1000videos as described in the measurement studies in Sec. 3.The length of each video is 10 minutes and the streamingrate is 600 Kbps. Each server is assigned a number ofvideos in each time slot to serve according to the deploy-ment algorithms. A PlanetLab node also generates therequests from viewers in that region, which are sent todifferent regions according to the video service schemediscussed in Sec. 5.

▷ Traces and parameters. We emulate the actual videovisits recorded in our Youku traces in each time slot andthe geographic distribution of viewers from our Weibo

traces. The video requests in each time slot are uniformlydistributed in a time slot. The upload bandwidth pricesin the regions are randomly assigned in the range of[0.5, 1.5] per MB, and the default budget for the deploy-ment is 20, 000. Parameter M is set as the same used inthe prediction model (i.e., 7 for old videos and n for newvideos published n days ago), and L is set to 6 so thatthe allocation is performed everyday.▷ Benchmark. For benchmarking the performance, we

measure the delay between the time a viewer issues arequest and the time it receives the first 1, 000 KB ofthe video stream, which is the typical size of the videodata before a viewer starts the playback. This delayconsists mainly of the RTT between the request andservice regions, as well as the queueing and processingdelay at the server.

5.4.2 Experimental ResultsAn Overview of Performance Comparison. First, wecompare the performance of different upload bandwidthreservation schemes: (1) our scheme given in Algorithm1; (2) a random allocation scheme in which a smallamount of upload bandwidth is progressively allocatedto a randomly selected region, until the total expenseexceeds the budget; (3) a proportional allocation schemewhere each time a small amount of upload bandwidthis allocated to a region selected randomly according to aprobability that is proportional to the number of viewsin that region, i.e., more upload bandwidth is allocatedto a region with more viewing requests. Under eachupload bandwidth reservation scheme, the same videodeployment algorithm (Algorithm 2) is employed.

In Fig. 9, we observe that our bandwidth reservationscheme can achieve much smaller delays for the viewers,given that not only the number of viewing requests ineach region, but also the user preference of differentregions to receive videos from are incorporated in ourdesign.

We also study the request load of these service regionsunder different upload reservation schemes. In Fig. 10,each sample represents the fraction of requests served bya region versus the region’s rank. We observe that in ourdesign, the request load is neither very uniformly dis-tributed as achieved by the random scheme, nor highlyskewed as in the proportional approach. Instead, ourupload bandwidth allocation scheme supplies adequatebandwidth in each region that matches the number ofrequests in that region. The reason is that our reservationis based on the microblogging prediction framework,which provides a better estimation of future requests ina region than the proportional scheme.

Impact of Deployment Interval. The video deploy-ment algorithm is carried out periodically, but frequentchanges of deployment may face potential challenges:(1) videos may need to be frequently replicated amongregions, incurring migration bandwidth consumption;(2) input features to the learning frameworks need to becollected frequently, incurring heavy load on the online

1 3 5 7 91000

1500

2000

2500

3000

3500

Deployment interval (time slot)

Ave

rage

del

ay (

ms)

Our deploymentRandom deploymentFull deploymentLR based deployment

Fig. 12. Comparison un-der different deploymentintervals.

0 10 20 30 40 501000

1200

1400

1600

1800

Time slot

Ave

rage

del

ay (

ms)

Old videosNew videos

Fig. 13. Comparison be-tween old and new videos.

microblogging system. We evaluate performance of thevideo service system when the video deployment iscarried out in different intervals. Fig. 12 shows that forboth our video deployment algorithm and the linearregression based scheme, the delay increases when theinterval for deployment becomes larger; while for boththe full replication and random deployment schemes, thedelays remain at the same level regardless of the intervallengths. The results indicate that the deployment intervalaffects the performance of our design, and frequentadjustments of video deployment should be appliedas long as the features from the online microbloggingsystem can be collected timely.

Impact of Deployment Budget. We next compareour video deployment algorithm in Algorithm 2 withthe following strategies: (1) a linear regression (LR)based scheme, i.e., the same video service deploymentalgorithm but using the linear regression approach topredict the number and geographic distribution of videoviews; (2) a random deployment scheme, where eachvideo is replicated in 1 randomly selected region in eachtime slot; (3) a full deployment scheme, where eachvideo is replicated to all the 6 regions. As for uploadbandwidth reservation, we use the same scheme as inAlgorithm 1 but under different budgets. In Fig. 11, weobserve that our video deployment algorithm performsmuch better than the other schemes, especially when thebudget is small. When the budget is larger than 25, 000,which is about enough for purchasing bandwidths toserve all requests locally, all the schemes achieve similardelays. The observation indicates that compared to otherschemes, our algorithm works effectively for a largerrange of deployment budgets, since it more precisely de-ploy videos to a region with enough bandwidth, wherethey will be requested by many users in that region;while in the other deployment schemes, many viewingrequests are redirected to regions without enough band-width capacity, resulting in large delays.

Impact of Prediction Accuracy. We compare the de-lays at viewers of old videos and new videos, respec-tively, which are treated differently in the predictionframeworks. Fig. 13 shows that viewers of old videos ex-perience smaller delays than those of new videos. This isconsistent with our evaluation results for the predictionaccuracy in Sec. 4.5 — better deployment performancecan be achieved with videos having a larger feature

0 10 20 30 40 501000

1500

2000

2500

Time slot

Ave

rage

del

ay (

ms)

Our upload reservationRandom upload reservationProportional upload reservation

Fig. 9. Comparison under differ-ent upload bandwidth reservationschemes.

1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

Rank of region

Fra

ctio

n of

req

uest

s se

rved

Our upload reservationRandom upload reservationProportional upload reservation

Fig. 10. Request load in differentregions.

1 2 3 4x 10

4

0

1000

2000

3000

4000

5000

Budget

Ave

rage

del

ay (

ms)

Our deploymentRandom deploymentFull deploymentLR based deployment

Fig. 11. Comparison under differentvideo deployment schemes.

window, where the prediction accuracy is higher. As theprediction accuracy directly determines the video servicedeployment performance in our design, exploring newfeatures from the microblogging services to improve theprediction accuracy is an approach to enhancing thestreaming quality for video sharing sites.

6 CONCLUDING REMARKS

In this paper, we explore the connections between in-formation propagation in a microblogging system andthe number and distribution of actual views in a videosharing site, using extensive traces from two large-scalereal-world microblogging and video sharing systems.Based on our discoveries of the connection betweenWeibo and Youku, we develop two neural networkmodels for predicting the future number of video viewsand geographic distribution of the viewers, respectively.We further exploit the prediction frameworks to improveglobal-scale video service deployment. Our PlanetLab-based experiments illustrate the effectiveness of our de-ployment algorithms in reducing video access delays ex-perienced by users, as compared to classical approacheswithout microblogging-assistant predictions.

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Zhi Wang received his B.E. and Ph.D. degreesin Computer Science in 2008 and 2014, from Ts-inghua University, Beijing, China. He is currentlyan assistant professor in the Graduate School atShenzhen, Tsinghua University. His research ar-eas include online social network, mobile cloudcomputing and large-scale multimedia systems.He is a member of IEEE.

Lifeng Sun received his B.S. and Ph.D. de-grees in System Engineering in 1995 and 2000from National University of Defense Technology,Changsha, Hunan, China. He was an postdoc-toral fellow from 2001 to 2003, an assistantprofessor from 2003 to 2007, an associate pro-fessor from 2007 to 2013, and currently a profes-sor all in the Department of Computer Scienceand Technology at Tsinghua University. His re-search interests lie in the areas of online socialnetwork, video streaming, interactive multi-view

video, and distributed video coding. He is a member of IEEE and ACM.

Chuan Wu received her B.E. and M.E. degreesin 2000 and 2002 from Department of ComputerScience and Technology, Tsinghua University,China, and her Ph.D. degree in 2008 from theDepartment of Electrical and Computer Engi-neering, University of Toronto, Canada. She iscurrently an assistant professor in the Depart-ment of Computer Science, the University ofHong Kong, China. Her research interests in-clude cloud computing, peer-to-peer networksand online/mobile social network. She is a mem-

ber of IEEE and ACM.

Shiqiang Yang received the B.E. and M.E. de-grees in Computer Science from Tsinghua Uni-versity, Beijing, China in 1977 and 1983, re-spectively. From 1980 to 1992, he worked asan assistant professor at Tsinghua University.He served as the associate professor from 1994to 1999 and then as the professor since 1999.From 1994 to 2011, he worked as the associateheader of the Department of Computer Scienceand Technology at Tsinghua University. He iscurrently the President of Multimedia Committee

of China Computer Federation, Beijing, China and the co-director ofMicrosoft-Tsinghua Multimedia Joint Lab, Tsinghua University, Beijing,China. His research interests mainly include multimedia procession,media streaming and online social network. He is a senior member ofIEEE.