Social voting is an emerging new feature in online social networks. It poses unique challenges and opportunities for recommendation. In this paper, we develop a set of matrix-factorization (MF) and nearest-neighbor (NN)-based recommender systems (RSs) that explore user social network and group affiliation information for social voting recommendation. Through experiments with real social voting traces, we demonstrate that social network and group affiliation information can significantly improve the accuracy of popularity-based voting recommendation, and social network information dominates group affiliation information in NN-based approaches. We also observe that social and group information is much more valuable to cold users than to heavy users. In our experiments, simple metapath-based NN models outperform computation-intensive MF models in hot-voting recommendation, while users' interests for nonhot votings can be better mined by MF models. We further propose a hybrid RS, bagging different single approaches to achieve the best top-k hit rate.

In a multiparty video conference, multiple users simultaneously distribute video streams to their receivers. As the traditional server-based solutions incur high infrastructure and bandwidth cost, conventional peer-to-peer (P2P) solutions only leveraging end-users' upload bandwidth are normally not self-sustainable: The video streaming workload increases quadratically with the number of users as each user could generate and distribute video streams, while the user upload bandwidth only increases linearly. Recently, hybrid solutions have been proposed that employ helpers to address the bandwidth deficiency in P2P video-conferencing swarms. It is also noticed that a system hosting multiple parallel conferencing swarms can benefit from cross-swarm bandwidth sharing. However, how to optimally share bandwidth in such systems has not been explored so far. In this paper, we study the optimal bandwidth sharing in multiswarm multiparty P2P video-conferencing systems with helpers and investigate two cross-swarm bandwidth-sharing scenarios: (1) swarms are independent and peers from different swarms share a common pool of helpers; (2) swarms are cooperative and peers in a bandwidth-rich swarm can further share their bandwidth with peers in a bandwidth-poor swarm. For each scenario, we develop distributed algorithms for intraswarm and interswarm bandwidth allocation under a utility-maximization framework. Through analysis and simulation, we show that the proposed algorithms are robust to peer dynamics and can adaptively allocate peer and helper bandwidth across swarms so as to achieve the system-wide optimum.

Despite the success to deliver increasingly large number of channels to millions of users, the current multi-channel P2P video streaming systems still suffer several fundamental performance problems, such as large start-up delays and poor performance for unpopular channels. To alleviate the impact of channel churn and resource imbalance, the View-Upload Decoupling (VUD) P2P streaming design decouples peer downloading and uploading, and enables cross-channel resource sharing. However, VUD incurs upload bandwidth overhead and distribution swarm management cost. It is also challenging to adapt VUD distribution swarms in extreme peer churn scenarios, such as flash-crowd. In this paper, we propose ViVUD, a Virtual Server Cluster based VUD design. In ViVUD, a virtual server cluster consisting of bandwidth-rich peers is provisioned to improve the streaming quality of each channel. A virtual server cluster provides stable video feeds to boost peers newly joining a channel to reduce their start-up delays. To enable cross-channel bandwidth sharing, following the VUD design, virtual server clusters for unpopular channels are formed by bandwidth-rich peers watching popular channels. Through analysis and simulations, we show that, compared with the original VUD design, ViVUD incurs less upload bandwidth overhead, has lighter management requirement, achieves lower channel start-up delays, and adapts faster to flash crowds.

As an efficient distribution mechanism, Peer-to-Peer (P2P) technology has become a tremendously attractive solution to offload servers in large-scale video streaming applications. However, in providing on-demand asynchronous streaming services, P2P streaming design faces two major challenges: how to schedule efficient video sharing between peers with asynchronous playback progresses? how to provide incentives for peers to contribute their resources to achieve a high level of system-wide Quality-of-Experience (QoE)? In this paper, we present iPASS, a novel mesh-based P2P VoD system, to address these challenges. Specifically, iPASS adopts a dynamic buffering-progress-based peering strategy to achieve high peer bandwidth utilization with low system maintenance cost. To provide incentives for peer uploading, iPASS employs a differentiated prefetching design that enables peers with higher contribution prefetch content at higher speed. A distributed adaptive taxation algorithm is developed to balance the system-wide QoE and service differentiations among heterogeneous peers. To assess the performance of iPASS, we built a detailed packet-level P2P VoD simulator and conducted extensive simulations. It was demonstrated that iPASS can completely offload server when the average peer upload bandwidth is more than 1.2 times the streaming rate. Furthermore, we showed that the distributed incentive algorithm motivates peers to contribute and collaboratively achieve a high level of system wide QoE.

In recent years, peer-to-peer (P2P) technology has been demonstrated tremendously effective in delivering large-scale video streaming services. Although P2P streaming is scalable and robust, the network-oblivious peering and scheduling in the current designs impede the further improvement in streaming quality and the efficient usage of network resources. New P2P streaming systems exploit network information provided by Internet service providers (ISPs) to achieve a higher level of application performance and generate lower traffic stress. In this paper, utilizing information from ISPs, we investigate the strategies on the topology construction and maintenance of multi-tree based P2P streaming systems. We study topology optimization to minimize the average height of sub-stream trees and the average propagation latency in each tree. We first present the optimization formulations, and then propose a set of heuristic algorithms for the construction and dynamic management of the multiple sub-stream trees for practical implementation. Through numerical comparison study, we show that our algorithms can significantly improve the delay performance of existing P2P streaming systems.

As an efficient distribution mechanism, peer-to-peer technology has become a tremendously attractive solution to offload servers in large scale video streaming applications. However, in providing on-demand asynchronous streaming services, P2P streaming design faces two major challenges: how to schedule efficient video sharing between peers with asynchronous playback progresses? how to provide incentives for peers to contribute their resources to achieve a high level of system-wide quality-of-experience (QoE)? In this paper, we present iPASS, a novel mesh-based P2P VoD system, to address these challenges. Specifically, iPASS adopts a dynamic buffering-progress-based peering strategy to achieve high peer bandwidth utilization with low system maintenance cost. To provide incentives for peer uploading, iPASS employs a differentiated pre-fetching design that enables peers with higher contribution pre-fetch content at higher speed. Through packet-level simulations, it was demonstrated that iPASS can effectively offload server and the proposed distributed incentive algorithm motivates peers to contribute and collectively achieve a high level of of QoE.

In current multi-channel live P2P video systems, there are several fundamental performance problems including exceedingly-large channel switching delays, long playback lags, and poor performance for less popular channels. These performance problems primarily stem from two intrinsic characteristics of multi-channel P2P video systems: channel churn and channel- resource imbalance. In this paper, we propose a radically different cross-channel P2P streaming framework, called view-upload decoupling (VUD). VUD strictly decouples peer downloading from uploading, bringing stability to multichannel systems and enabling cross-channel resource sharing. We propose a set of peer assignment and bandwidth allocation algorithms to properly provision bandwidth among channels, and introduce substream swarming to reduce the bandwidth overhead. We evaluate the performance of VUD via extensive simulations as well with a PlanetLab implementation. Our simulation and PlanetLab results show that VUD is resilient to channel churn, and achieves lower switching delay and better streaming quality. In particular, the streaming quality of small channels is greatly improved.

Peer-to-peer (P2P) technology has recently been employed to deliver large scale video multicast services on the Internet. Considerable efforts have been made by both academia and industry on P2P streaming design. While academia mostly focus on exploring design space to approach the theoretical performance bounds, our recent measurement study on several commercial P2P streaming systems indicates that they are able to deliver good user quality of experience with seemingly simple designs. One intriguing question remains:how elaborate should a good P2P video streaming design be?Towards answering this question, we developed and implemented several representative P2P streaming designs, ranging from theoretically proved optimal designs to straightforward “naive” designs. Through an extensive comparison study on PlanetLab, we unveil several key factors contributing to the successes of simple P2P streaming designs, including system resource index, server capacity and chunk scheduling rule, peer download buffering and peering degree. We also identify regions where naive designs are inadequate and more elaborate designs can improve things considerably. Our study not only brings us better understandings and more insights into the operation of existing systems, it also sheds lights on the design of future systems that can achieve a good balance between the performance and the complexity.

Peer-to-Peer (P2P) technology has recently been employed to deliver large scale video multicast services on the Internet. Considerable efforts have been made by both academia and industry on P2P streaming design. While academia mostly focus on exploring design space to approach the theoretical performance bounds, our recent measurement study on several commercial P2P streaming systems indicates that they are able to deliver good user Quality of Experience with seemingly simple designs. One intriguing question remains: how elaborate should a good P2P video streaming design be? Towards answering this question, we developed and implemented several representative P2P streaming designs, ranging from theoretically proved optimal designs to straight forward "naive" designs. Through an extensive comparison study on PlanetLab, we unveil several key factors contributing to the successes of simple P2P streaming designs, including system resource index, sever capacity and chunk scheduling rule, peer download buffering and peering degree. We also identify regions where naive designs are inadequate and more elaborate designs can improve things considerably. Our study not only brings us better understandings and more insights into the operation of existing systems, it also sheds lights on the design of future systems that can achieve a good balance between the performance and the complexity.

Peer-to-peer video streaming has been gaining popularity. However, it is still challenging to manage a P2P system efficiently to support high video playback rate. In this paper, we propose HCPS - Hierarchically Clustered P2P Streaming system that can support a streaming rate approaching the optimum upper bound with short delay, yet is simple enough to be implemented in practice. In HCPS, the peers are grouped into clusters and a hierarchy is formed among clusters to retrieve video data from the source server. By actively balancing the uploading capacities among clusters, and executing the perfect scheduling algorithm [1] within each cluster, the system resource can be fully utilized. The simulation experiments driven by the traces collected from a real P2P streaming system demonstrate the effectiveness of HCPS.

Under device failures and maintenance activities, network resources reduce and congestion may arise inside networks. In this paper, we study a dual approach that combines traffic blocking (rate-limiting) at the edge of a network and traffic rerouting inside the network. We formulate a joint ingress blocking and routing optimization problem and develop mechanisms to introduce blocking differentiations among users with different service priorities and with different level of impact to network congestions. Our evaluation result shows that by blocking only a small fraction of traffic, one can greatly reduce network congestion under severe failures and maintenance activities. Our solution efficiently identifies the optimal blocking among heterogeneous users and achieves much better performance in comparison with proportional traffic blocking. The proposed algorithms can be easily adopted by network service providers in their traffic engineering practices.

An emerging Internet application, IPTV, has the potential to flood Internet access and backbone ISPs with massive amounts of new traffic. Although many architectures are possible for IPTV video distribution, several mesh-pull P2P architectures have been successfully deployed on the Internet. In order to gain insights into mesh-pull P2P IPTV systems and the traffic loads they place on ISPs, we have undertaken an in-depth measurement study of one of the most popular IPTV systems, namely, PPLive. We have developed a dedicated PPLive crawler, which enables us to study the global characteristics of the mesh-pull PPLive system. We have also collected extensive packet traces for various different measurement scenarios, including both campus access networks and residential access networks. The measurement results obtained through these platforms bring important insights into P2P IPTV systems. Specifically, our results show the following. 1) P2P IPTV users have the similar viewing behaviors as regular TV users. 2) During its session, a peer exchanges video data dynamically with a large number of peers. 3) A small set of super peers act as video proxy and contribute significantly to video data uploading. 4) Users in the measured P2P IPTV system still suffer from long start-up delays and playback lags, ranging from several seconds to a couple of minutes. Insights obtained in this study will be valuable for the development and deployment of future P2P IPTV systems.