Adele Lu Jia

Fast download but eternal seeding: The reward and punishment of Sharing Ratio Enforcement

Many private BitTorrent communities employ Sharing Ratio Enforcement (SRE) schemes to incentivize users to contribute their upload resources. It has been demonstrated that communities that use SRE are greatly oversupplied, i.e., they have much higher seeder-to-leecher ratios than communities in which SRE is not employed. The first order effect of oversupply under SRE is a positive increase in the average downloading speed. However, users are forced to seed for extremely long times to maintain adequate sharing ratios to be able to start new downloads. In this paper, we propose a fluid model to study the effects of oversupply under SRE, which predicts the average downloading speed, the average seeding time, and the average upload capacity utilization for users in communities that employ SRE. We notice that the phenomenon of oversupply has two undesired negative effects: a) Peers are forced to seed for long times, even though their seeding efforts are often not very productive (in terms of low upload capacity utilization); and b) SRE discriminates against peers with low bandwidth capacities and forces them to seed for longer durations than peers with high capacities. To alleviate these problems, we propose four different strategies for SRE, which have been inspired by ideas in social sciences and economics. We evaluate these strategies through simulations. Our results indicate that these new strategies release users from needlessly long seeding durations, while also being fair towards peers with low capacities and maintaining high system-wide downloading speeds.

Socializing by Gaming: Revealing Social Relationships in Multiplayer Online Games

Multiplayer Online Games (MOGs) like Defense of the Ancients and StarCraft II have attracted hundreds of millions of users who communicate, interact, and socialize with each other through gaming. In MOGs, rich social relationships emerge and can be used to improve gaming services such as match recommendation and game population retention, which are important for the user experience and the commercial value of the companies who run these MOGs. In this work, we focus on understanding social relationships in MOGs. We propose a graph model that is able to capture social relationships of a variety of types and strengths. We apply our model to real-world data collected from three MOGs that contain in total over ten years of behavioral history for millions of players and matches. We compare social relationships in MOGs across different game genres and with regular online social networks like Facebook. Taking match recommendation as an example application of our model, we propose SAMRA, a Socially Aware Match Recommendation Algorithm that takes social relationships into account. We show that our model not only improves the precision of traditional link prediction approaches, but also potentially helps players enjoy games to a higher extent.

When Game Becomes Life: The Creators and Spectators of Online Game Replays and Live Streaming

Online gaming franchises such as World of Tanks, Defense of the Ancients, and StarCraft have attracted hundreds of millions of users who, apart from playing the game, also socialize with each other through gaming and viewing gamecasts. As a form of User Generated Content (UGC), gamecasts play an important role in user entertainment and gamer education. They deserve the attention of both industrial partners and the academic communities, corresponding to the large amount of revenue involved and the interesting research problems associated with UGC sites and social networks. Although previous work has put much effort into analyzing general UGC sites such as YouTube, relatively little is known about the gamecast sharing sites. In this work, we provide the first comprehensive study of gamecast sharing sites, including commercial streaming-based sites such as Amazon’s Twitch.tv and community-maintained replay-based sites such as WoTreplays. We collect and share a novel dataset on WoTreplays that includes more than 380,000 game replays, shared by more than 60,000 creators with more than 1.9 million gamers. Together with an earlier published dataset on Twitch.tv, we investigate basic characteristics of gamecast sharing sites, and we analyze the activities of their creators and spectators. Among our results, we find that (i) WoTreplays and Twitch.tv are both fast-consumed repositories, with millions of gamecasts being uploaded, viewed, and soon forgotten; (ii) both the gamecasts and the creators exhibit highly skewed popularity, with a significant heavy tail phenomenon; and (iii) the upload and download preferences of creators and spectators are different: while the creators emphasize their individual skills, the spectators appreciate team-wise tactics. Our findings provide important knowledge for infrastructure and service improvement, for example, in the design of proper resource allocation mechanisms that consider future gamecasting and in the tuning of incentive policies that further help player retention.

How to Survive and Thrive in a Private BitTorrent Community

Many private BitTorrent communities employ Sharing Ratio Enforcement (SRE) schemes to incentivize users to contribute. It has been demonstrated that communities that adopt SRE are greatly oversupplied, i.e., they have much higher seeder-to-leecher ratios than communities in which SRE is not employed. Most previous studies focus on showing the positive effect of SRE in achieving high downloading speed. However, in this paper we show through measurements that SRE also induces severe side-effects. Under SRE, users are forced to seed for excessively long times to maintain adequate sharing ratios to be able to start new downloads, though most of the time their seedings are not very productive (in terms of low upload speed). We also observe that many users who seed for very long times still have low sharing ratios. We find that this is due to the counter-intuitive phenomenon that long seeding times do not necessarily lead to large upload amounts. Based on these observations, we discuss possible strategies for users to gain sharing ratios efficiently, which help them to survive and thrive in private communities.

Modeling and Analysis of Sharing Ratio Enforcement in Private BitTorrent Communities

Providing incentives for user contribution has been one of the primary design goals of Peer-to-Peer systems. The newly-emerged BitTorrent private communities adopt Sharing Ratio Enforcement (SRE) on top of BitTorrents incentive mechanism, Tit-For-Tat, in order to strictly enforce a minimum contribution a member has to provide, in relation to the amount of service it has received. In this paper, we provide a theoretical model to analyze 1) how SRE provides seeding incentives, and 2) how SRE influences the download performance in the system. Specifically, we study the influence of the SRE threshold (i.e., the minimum sharing ratio requirement) and the bandwidth heterogeneity of the peers in the system. In our analysis, we assume users to be rational, i.e., peers seed only the minimum amount required by SRE, and we show that the download performance as predicted by our model represents a lower bound for the actual performance that can be reached in a BitTorrent private community. Hence, following our model, community administrators can predict the minimum performance level their systems will be able to reach.

User behaviors in private BitTorrent communities

Many private BitTorrent communities employ Sharing Ratio Enforcement (SRE) schemes to incentivize users to contribute. It has been demonstrated that users in private communities are highly dedicated and that they seed much longer than users in communities where SRE is not employed. While most previous studies focus on showing the positive effect of user dedication in achieving high download speed, in this paper we explore the user behaviors in private communities, we argue the reasons for these behaviors, and we demonstrate both the positive and the negative effects of these behaviors. We show that under SRE, users seed for excessively long times to maintain required sharing ratios, but that their seedings are often not very productive (in terms of low upload speed) and that their long seeding times do not necessarily lead to large upload amounts. We find that as users evolve in the community, some users become more committed, in terms of increasing ratios between their seeding and leeching times. In the mean time, some users game the system by keeping risky and low sharing ratios while leeching more often than seeding. Based on these observations, we analyze strategies that alleviate the negative effects of these user behaviors from both the users and the community administrators perspective.

Dissecting Darknets: Measurement and Performance Analysis

BitTorrent (BT) plays an important role in Internet content distribution. Because public BTs suffer from the free-rider problem, Darknets are becoming increasingly popular, which use Sharing Ratio Enforcement to increase their efficiency. We crawled and traced 17 Darknets from September 2009 to February 2011, and obtained datasets about over 5 million torrents. We conducted a broad range of measurements, including traffic, sites, torrents, and users activities. We found that some of the features of Darknets are noticeably different from public BTs. The results of our study reflect both macroscopic and microscopic aspects of the overall ecosystem of BitTorrent Darknets.

An Analysis on a YouTube-like UGC site with Enhanced Social Features

YouTube-like User Generated Content (UGC) sites are nowadays entertaining over a billion people. Resource provision is essential for these giant UGC sites as they allow users to request videos from a potentially unlimited selection in an asynchronous fashion. Still, the UGC sites are seeking to create new viewing patterns and social interactions that would engage and attract more users and complicate the already rigorous resource provision problem. In this paper, we seek to combine these two tasks by leveraging social features to provide the reference for resource provision. To this end, we conduct an extensive measurement and analysis of BiliBili, a YouTube-like UGC site with enhanced social features including user following, chat replay, and virtual money donation. Based on datasets that capture the complete view of BiliBili---containing over 2 million videos and over 28 million users---we characterize its video repository and user activities, we demonstrate the positive reinforcement between on-line social behavior and upload behavior, we propose graph models that reveal user relationships and high-level social structures, and we successfully apply our findings to build machine-learnt classifiers to identify videos that will need priority in resource provision.

Predicting the implicit and the explicit video popularity in a User Generated Content site with enhanced social features

Abstract User Generated Content (UGC) sites like YouTube are nowadays entertaining over a billion people. Identifying popular contents is essential for these giant UGC sites as they allow users to request contents from a potentially unlimited selection in an asynchronous fashion. In this work, we conduct an analysis on the popularity prediction problem in UGC sites and complement previous work with two new aspects, namely differentiating contents that attract a lot of attention and that users really appreciate, and leveraging built-in social features to predict the content popularity immediately upon publication. To this end, we conduct an extensive measurement and analysis of BiliBili, a YouTube-like UGC site with enhanced social features including user following, chat replay, and virtual money donation. Based on datasets that contain over 2 million videos and over 28 million users, we characterize the video repository and the user activities, we analyze the video popularities, we propose graph models that reveal user relationships and high-level social structures, and we successfully apply our findings to build machine-learned classifiers to identify popular videos.